Optical Fiber Sensing Technology for Sports Monitoring: A Comprehensive Review

Abstract

The advancement of sports science has heightened demands for precise monitoring of athletes’ technical movements, physiological status, and performance. Optical fiber sensing (OFS) technology, with its unique advantages including high sensitivity, immunity to electromagnetic interference, capability for distributed sensing, and strong biocompatibility, demonstrates significant application potential in sports science. This review systematically examines the technical principles, innovative breakthroughs, and practical application cases of optical fiber sensors in various domains: monitoring key human physiological parameters such as respiration, heart rate, and body temperature; capturing motion and analyzing movement covering muscle activity, joint angles, and gait; integrating within smart sports equipment and protective gear; and monitoring sports apparatus and environments. The value of OFS technology is further analyzed in areas including sports biomechanics analysis, training load monitoring, injury prevention, and rehabilitation optimization. Concurrently, current technical bottlenecks such as the need for enhanced sensitivity, advancements in flexible packaging technologies, cost control, system integration, and miniaturization are discussed. Future development trends involving the integration of OFS with artificial intelligence, the Internet of Things, and new materials are explored, aiming to provide a theoretical foundation for sports medicine and training optimization.

1. Introduction

In contemporary sports science, the real-time acquisition of precise physiological, biomechanical, and environmental data has emerged as a critical methodology and central objective for optimizing training effectiveness and enhancing athletic performance through accurate monitoring of athletes’ technical movements and physiological status. Driven by the widespread adoption of scientific training methodologies, escalating demands for individualized training protocols, fatigue management, and injury prevention necessitate advanced monitoring technologies capable of delivering real-time, high-fidelity measurements [1].

The advancement of sports science increasingly depends on high-accuracy motion capture and physiological monitoring technologies. Conventional monitoring techniques such as inertial measurement units (IMUs), surface electromyography (sEMG), and optical motion capture systems find widespread application [2]. However, their inherent limitations become increasingly pronounced in dynamic, high-intensity, or environmentally challenging athletic scenarios. Traditional electronic sensors—including accelerometers and surface electrodes—are susceptible to electromagnetic interference and environmental perturbations, while their bulk and weight restrict natural athlete movements. These constraints hinder high-density multipoint distributed measurements. Under complex conditions like elevated humidity, subzero temperatures during winter sports, or strong electromagnetic fields common in indoor arenas [3], sensor performance tends to degrade. Collectively, these challenges impede the acquisition of continuous, high-fidelity datasets in authentic, multifaceted sporting environments.

Against this backdrop of technological challenges, optical fiber sensing technology (OFST) has rapidly advanced in sports science [4], leveraging its intrinsic electromagnetic interference immunity and high sensitivity. This technology demonstrates unique value for real-time monitoring of athletes’ physiological parameters, quantitative motion analysis, and injury prevention [5,6]. Operating via light-guided modulation through optical fibers, OFST quantifies physical parameters by detecting stimulus-induced changes in optical signals. Key advantages include intrinsically safe current-free operation, exceptional electro-magnetic interference resistance, high sensitivity, corrosion durability, seamless embedment in textiles/equipment, and native support for distributed sensing and multiplexing [7,8,9].

Recent advances in material science—particularly flexible polymers, elastomers, and graphene—combined with progress in signal processing algorithms [10,11], as well as breakthroughs in flexible optical fibers, microstructured optical fibers, and smart textile integration technologies [12,13], have enabled optical fiber sensing technology to transition from established infrastructure monitoring applications (e.g., bridges, pipelines) into life science domains such as competitive sports and rehabilitation medicine [14]. Particularly notable progress has been made in wearable applications for physiological and movement monitoring [15]. Within sports monitoring, optical fiber sensing technology demonstrates significant value across multiple dimensions. First, its minimal weight and flexibility permit direct sensor embedment into athletic equipment or epidermal attachment, enabling continuous unobtrusive monitoring of joint kinematics, muscle contractions, and postural dynamics while substantially reducing interference with athletic performance [16,17,18]. Second, the technology’s exquisite strain sensitivity captures subtle biomechanical variations during athletic movements—including muscle fasciculations and instantaneous joint angle deviations—delivering precise datasets for technique refinement [19,20]. Third, multiplexing capabilities through wavelength-division (WDM), space-division (SDM), and time-division multiplexing (TDM) enable multiple sensing nodes to be deployed along a single fiber. This facilitates synchronous tracking of full-body biomechanics and generates comprehensive motion profiles [21,22]. Finally, environmental robustness—characterized by moisture resistance, corrosion durability, and electromagnetic interference immunity—ensures reliable operation in challenging environments where conventional sensors falter, such as winter sports venues, aquatic settings, and electromagnetically noisy arenas. Collectively, these attributes position optical fiber sensing as an ideal solution for sports science applications requiring high precision, real-time feedback, and environmental adaptability—particularly in dynamic, multiparametric monitoring scenarios with complex conditions. This technology overcomes inherent limitations of conventional sensors, permitting acquisition of critical biomechanical and physiological signals previously inaccessible with precision [23].

Given the rapid advancement and demonstrated utility of optical fiber sensing in sports monitoring, this review synthesizes current progress and practical implementations to systematically examine the technology’s fundamental principles, system architectures, and application outcomes within athletic contexts. Critical technical constraints are evaluated, while future trajectories integrating artificial intelligence, advanced materials, and the Internet of Things are prospectively analyzed. This comprehensive assessment provides a reference framework for ongoing research and field applications.

2. Basic Principles and Main Types of Optical Fiber Sensing Technology for Sports Performance Monitoring

Since the proposal of optical fibers as a transmission medium for optical communications in 1966, optical fiber sensing technology has undergone continuous development. The working principle involves transmitting optical signals through optical fibers to a modulator, where light interacts with the target external parameters. This interaction alters the optical properties of the light—such as intensity, wavelength, phase, and polarization state—resulting in modulated optical signals. These signals are then transmitted via optical fibers to photoelectric elements and subsequently processed by signal demodulators before reaching data processing platforms for parameter extraction [3,4,18,21], as illustrated in Figure 1. In sports applications, the system operation begins with a laser source emitting a stable beam, which is transmitted through optical fibers to sensor arrays integrated into athletes’ bodies or equipment. During athletic movements, minute deformations or pressure changes induced by body motions modulate parameters of the light waves within the fibers, including intensity, wavelength, and phase. The returning optical signals, carrying motion information, are converted into electrical signals via photoelectric converters. Specially designed demodulation algorithms extract characteristic parameters—such as joint angle variations, gait cycles, muscle contraction frequencies, and pressure distribution—with final visualization and analysis of sports data achieved through host computer software or mobile terminals [10,20].

Based on the different sensing mechanisms, fiber optic sensing technologies applied in sports performance monitoring are primarily categorized as follows.

2.1. Fiber Bragg Grating Sensors

Fiber Bragg grating sensors, particularly the standard Fiber Bragg Grating (FBG), play a critical role across the entire field of fiber optic sensing [24,25] and represent a key technology widely used in sports science monitoring. Their operational principle is based on wavelength modulation mechanism: a periodic refractive index structure is inscribed within the fiber core using ultraviolet laser irradiation. When external physical quantities—such as strain or temperature—change, the grating period or the effective refractive index of the core is altered, resulting in a shift in the central characteristic wavelength (Bragg wavelength) of the reflected or transmitted spectrum. This wavelength shift is proportional to the change in the measured quantity, enabling high-precision sensing through accurate demodulation [21,25]. The central wavelength of the reflected light satisfies the following Bragg condition:

$${\mathsf{\lambda }}_{\mathrm{B}\mathrm{r}\mathrm{a}\mathrm{g}\mathrm{g}}=2\mathrm{n}\mathsf{\Lambda }$$

(1)

where n is the effective refractive index and Λ is the grating period [9]. When external physical parameters change, the resulting Bragg wavelength shift ${\mathsf{\Delta }\lambda }_B$is caused by the combined effects of strain $\epsilon$ and temperature variation $\mathsf{\Delta }T$. This relationship is generally expressed as:

$${\displaystyle \frac{{\mathsf{\Delta }\lambda }_B}{{\lambda }_B}}=\left(1-p_e\right)\epsilon +\left(\alpha +\xi \right)\mathsf{\Delta }T$$

(2)

Here, $p_e$represents the photoelastic coefficient, $\alpha$ denotes the thermal expansion coefficient, and $\xi$ is the thermo-optic coefficient. In specific applications such as sports monitoring, the analysis is often simplified by estimating under constant temperature conditions using the strain sensitivity coefficient $K_{\epsilon }$:

$${\mathsf{\Delta }\lambda }_B=K_{\epsilon }·\epsilon$$

(3)

where $K_{\epsilon }\approx 1.2 \mathrm{p}\mathrm{m}/\mathsf{\mu }\mathsf{\epsilon }$. Similarly, when used for temperature monitoring, the sensitivity coefficient is denoted as $K_T$:

$${\mathsf{\Delta }\lambda }_B=K_T·\mathsf{\Delta }T$$

(4)

with $K_T\approx 10 \mathrm{p}\mathrm{m}/\mathrm{℃}$. To address the issue of cross-sensitivity between temperature and strain, a common approach employs a dual-grating decoupling method. By establishing a sensitivity matrix equation:

$$\left[\begin{array}{c}{\mathsf{\Delta }\lambda }_{B1}\\ {\mathsf{\Delta }\lambda }_{B2}\end{array}\right]=\left[\begin{array}{cc}{K}_{\epsilon 1}& K_{T1}\\ K_{\epsilon 1}& K_{T2}\end{array}\right]·\left[\begin{array}{c}\epsilon \\ \mathsf{\Delta }T\end{array}\right]$$

(5)

The strain and temperature can be distinguished and measured independently by solving this matrix equation. FBG sensors are extensively applied in monitoring physiological parameters during exercise, analyzing movement technique, and assessing the mechanical properties of sports equipment [22,24,25,26] (Figure 2).

However, several significant limitations remain in the application of FBG sensors in sports monitoring.

• First, cross-sensitivity between strain and temperature considerably impairs measurement accuracy. Since FBGs are responsive to both temperature and strain—which often vary simultaneously in athletic settings—signal aliasing occurs, making it difficult to distinguish physiological or mechanical signals from environmental thermal noise. Conventional FBGs exhibit a temperature sensitivity of approximately 10 pm/$℃$ [27]. In outdoor sports with substantial ambient temperature fluctuations, such as marathons and skiing, temperature variations can induce wavelength shifts exceeding 200 pm, severely interfering with strain signal interpretation. Although reference grating methods or dual-parameter matrix decoupling can be employed, these approaches increase system complexity and cost, and their real-time performance under dynamic conditions remains suboptimal [28,29].
• Second, FBG sensors are prone to nonlinear responses during high-dynamic-range and large-amplitude movements (e.g., weightlifting or jumping). When strain exceeds 5000 $\mu \epsilon$, nonlinear material behavior, encapsulation slippage, and changes in the photoelastic effect [30] may cause the wavelength–strain relationship to deviate from linearity, reducing measurement accuracy and often necessitating complex calibration or algorithmic compensation.
• Third, the multiplexing capability and associated costs of FBG systems limit their scalability. Although WDM is supported, the number of sensors that can be practically multiplexed is constrained by the source bandwidth and the dynamic range of the demodulator. Typically, only several dozen sensors can be serially connected along a single fiber, making large-scale multi-point monitoring challenging. Moreover, high-speed and high-precision demodulation equipment is expensive, and installation and calibration require specialized personnel, hindering widespread adoption in community or amateur sports settings [31].
• Finally, packaging reliability and long-term stability present important challenges. Packaging structures—including adhesive layers and substrate materials—may degrade or fail under repeated mechanical stress, sweat exposure, and fluctuations in temperature and humidity [32]. Additionally, fully bonded encapsulation is susceptible to stress concentration, leading to spectral broadening or chirping, which compromises measurement linearity and reliability [33,34].

In summary, although FBG sensors offer advantages such as high precision, interference immunity, and multi-parameter sensing, their applications in complex athletic environments are constrained by issues such as temperature–strain cross-sensitivity, nonlinear behavior under high strain, limited multiplexing capacity, system cost, and packaging reliability. Future efforts should focus on developing intelligent decoupling algorithms, optimizing packaging techniques, and reducing costs to enhance their practicality and adoption in sports science.

2.2. Distributed Optical Fiber Sensors

In contrast to discrete point-based FBG sensors, distributed optical fiber sensing (DOFS)—encompassing Distributed Temperature Sensing (DTS) and Distributed Acoustic Sensing (DAS)—utilizes the entire length of the optical fiber as a continuous sensor. Common techniques are based on Rayleigh, Brillouin, and Raman scattering mechanisms, employing different reflectometry and demodulation schemes such as Optical Time-Domain Reflectometry (OTDR) and Optical Frequency-Domain Reflectometry (OFDR). An optical pulse is launched into the fiber, and the spatial distribution of parameters like temperature and strain along the fiber length is obtained by analyzing changes in the intensity, frequency, or phase of the backscattered light. Specifically, Raman scattering is primarily responsive to temperature, Brillouin frequency shift is sensitive to both strain and temperature, and Rayleigh scattering intensity is sensitive to vibration and strain.

The sensing characteristics of the different scattering mechanisms can be quantitatively described by their mathematical models. For Brillouin scattering, which shows great potential in sports monitoring, its core relationship is the linear superposition of the Brillouin frequency shift ${\mathsf{\nu }}_B$with strain $\epsilon$ and temperature change $\mathsf{\Delta }T$:

$${\mathsf{\nu }}_B\left(\epsilon ,T\right)={{\nu }_{B0}+C}_{\epsilon }·\epsilon +C_T·\mathsf{\Delta }T$$

(6)

Here, ${\mathsf{\nu }}_{B0}$ is the reference frequency shift, while $C_{\epsilon }$ and $C_T$ are the strain and temperature coefficients, respectively. It is this very property that enables Brillouin sensors to perceive both physical quantities simultaneously, but it also leads to cross-sensitivity between strain and temperature. To address this issue, a dual-parameter decoupling algorithm is often employed, which essentially involves solving the following matrix equation:

$$\left[\begin{array}{c}{\mathsf{\Delta }\nu }_{B1}\\ {\mathsf{\Delta }\nu }_{B2}\end{array}\right]=\left[\begin{array}{cc}{C}_{\epsilon 1}& C_{T1}\\ C_{\epsilon 2}& C_{T2}\end{array}\right]\left[\begin{array}{c}\epsilon \\ \mathsf{\Delta }T\end{array}\right]$$

(7)

Furthermore, key performance parameters in distributed sensing systems are subject to inherent trade-offs. The spatial resolution $\mathsf{\Delta }z$ is directly determined by the optical pulse width $\tau$:

$$\mathsf{\Delta }z={\displaystyle \frac{c·\tau }{2·n}}$$

(8)

This explains why pursuing high spatial resolution by reducing τ leads to a decrease in signal-to-noise ratio due to lower scattered light energy. In contrast, temperature demodulation in Raman scattering-based Distributed Temperature Sensing systems relies on the ratio of anti-Stokes-to-Stokes intensity, which follows an exponential relationship with temperature T:

$${\displaystyle \frac{I_{AS}}{I_S}}\propto \mathrm{e}\mathrm{x}\mathrm{p}(-{\displaystyle \frac{h\mathsf{\Delta }\nu }{kT}})$$

(9)

The extremely weak nature of this scattered light fundamentally determines the inherent limitation of low signal-to-noise ratio in DTS. This capability enables distributed fiber optic sensors to perform spatially continuous measurements over long distances, utilizing a single fiber as a complete sensing network. These systems are particularly suitable for environmental monitoring in large venues and integrity assessment of extended structures such as race tracks and ski trails.

Notwithstanding these advantages, the practical application of various DOFS techniques in motion monitoring presents significant challenges. While Raman-based DTS exhibits high temperature sensitivity, its utility is constrained by a low signal-to-noise ratio (SNR), a spatial resolution typically confined to the meter scale, and a slow response time, rendering it inadequate for capturing rapid motions or transient physiological signals. Brillouin scattering-based sensors, despite their capability for simultaneous strain and temperature measurement, are limited by a spatial resolution often no better than one meter and a demodulation speed that requires seconds of averaging, thus restricting their application in high-speed motion tracking or fine movement identification. Moreover, the inherent cross-sensitivity between strain and temperature necessitates complex decoupling via additional algorithms or hardware, which increases system complexity [35]. Although Rayleigh-based DAS offers high sensitivity to vibration, achieves centimeter-scale spatial resolution, and enables real-time response, its performance is severely compromised by environmental noise (e.g., crowd cheers, mechanical vibrations) in complex sporting settings, leading to significant SNR degradation [36]. Furthermore, all DOFS technologies mandate the installation of dedicated fiber infrastructure, presenting ongoing practical challenges related to durability, flexibility, and integration methods with sports environments (e.g., embedding into turf, attachment to equipment or apparel) [37]. At present, most research remains in the laboratory or small-scale field validation stage, with a pronounced lack of large-scale, long-term empirical data from real-world sports monitoring applications to substantiate their widespread deployment.

2.3. Polymer Optical Fiber Sensors

Polymer optical fiber (POF) sensors are fabricated from flexible polymeric materials, such as polymethyl methacrylate (PMMA) or polydimethylsiloxane (PDMS) [38] (Figure 3). POF sensors are characterized by superior flexibility (enabling small bending radii), high biocompatibility, notable impact resistance, low cost, and ease of processing, making them highly suitable for integration into wearable devices. In particular, intensity-modulation-type POF sensors—where optical intensity attenuates in response to strain—are well-suited for wearable applications [11,14].

Conventional silica optical fiber sensors exhibit high rigidity, can only withstand limited strain, and are prone to fracture. In contrast, sensors fabricated from polymeric materials overcome the limitations of traditional silica fibers in terms of flexibility and strain tolerance. They offer significantly higher strain tolerance and excellent biocompatibility, and can maintain their original sensing performance even after prolonged wear, making them highly suitable for addressing the challenges of human motion monitoring. For instance, materials such as PMMA or PDMS can provide strain tolerance exceeding 30%. Compared to electronic sensors, POF sensors offer distinct advantages including compactness, light weight, flexibility, immunity to electromagnetic interference, chemical stability, and reusability. These properties have effectively promoted the widespread adoption of POF sensor technology in smart textiles [38]. Consequently, POFs have emerged as promising instrumentation for wearable robotics and biomedical applications (Figure 4).

However, POFs still present several limitations in motion monitoring applications. Firstly, their optical loss is significantly higher than that of silica fibers (typically ranging from 0.1 to 1 dB/cm or even higher), which constrains the sensing distance and signal stability. This leads to a marked decline in the signal-to-noise ratio, particularly in long-range or high-precision measurement scenarios [39]. Secondly, the high thermal expansion coefficient and thermo-optic coefficient of polymer materials result in pronounced temperature cross-sensitivity. Fluctuations in ambient temperature can readily introduce errors in strain measurements [40]. Furthermore, although POFs exhibit excellent flexibility, their mechanical durability remains a challenge. Under long-term cyclic loading or high strain conditions, issues such as creep, stress relaxation, or permanent deformation may occur, compromising measurement reliability and lifespan [41]. Concurrently, the multimodal nature of POFs causes significant modal interference and source-induced noise, necessitating complex signal processing or compensation algorithms [42]. Finally, current demodulation techniques for POF sensing predominantly rely on intensity modulation, which is susceptible to interference from source fluctuations, connector losses, and micro-bending effects. Consequently, the measurement accuracy and repeatability often fall short of those achieved by wavelength-based silica fiber sensing technologies [43]. These factors collectively restrict the widespread application of POFs in high-precision sports biomechanics analysis, with current research remaining largely confined to laboratory studies and short-duration demonstrations.

Table 1. Performance comparison of polymer optical fiber (POF) and conventional silica fiber under harsh environments for sports monitoring.

Property	POF	Silica Optical Fiber	Recommendations for Sports Monitoring Applica	References
Mechanical Performance	High toughness (strain tolerance > 15%), impact-resistant, fatigue-resistant to repeated bending, lightweight, and flexible.	Brittle (strain tolerance < 1–2%), poor bending fatigue resistance, and relatively heavy.	POF: Ideal for wearable devices, impact monitoring, and smart textiles. Silica fiber: Suitable for embedded structural micro-strain monitoring.	[44,45,46]
Environmental Tolerance	PMMA exhibits strong moisture absorption; CYTOP/Zeonex series offer good moisture resistance; Chemical resistance is generally moderate (poor for PMMA, excellent for CYTOP); Limited temperature resistance (PMMA < 85 °C, specialty POFs < 120 °C).	The fiber core is non-hygroscopic (coatings may absorb moisture); Excellent chemical resistance (except for HF acid); Exceptional high-temperature resistance (can exceed 300 °C).	High-humidity environments: Prefer CYTOP POF or polyimide-coated silica fiber. High-temperature environments: Silica fiber holds an absolute advantage.	[47,48,49]
Signal Characteristics	Higher attenuation (10–100 dB/km); compatible with low-cost light sources (e.g., LEDs).	Very low attenuation (~0.2 dB/km); suitable for long-distance transmission.	POF: Preferred for short-distance monitoring applications. Silica fiber: Essential for long-distance and distributed sensing.	[50,51]
Electromagnetic Properties	Entirely immune to electromagnetic interference (EMI/RFI).	Entirely immune to electromagnetic interference.	Both perform excellently; suitable for replacing electronic sensors in strong electromagnetic environments (e.g., near gym equipment or medical devices).	[52,53]
Cost and Handling	Lower cost; simple termination (can be achieved with a blade); easier integration	Higher cost; termination requires precision equipment (e.g., fusion splicer) and skilled technique.	POF: Ideal for consumer-grade markets, temporary monitoring setups, and scenarios requiring frequent reconfiguration. Silica fiber: Suited for professional, fixed installations and high-precision laboratory environments.	[54,55]

compares the performance of polymer optical fibers and conventional silica fibers in harsh environments and provides recommendations for sports monitoring.

In summary, compared to traditional electronic sensors, optical fiber sensing technology offers multiple advantages for sports monitoring:

• Immunity to Interference: Optical fibers are fabricated from dielectric materials (e.g., silica, polymers), rendering them immune to electromagnetic fields. This allows stable operation in complex athletic environments—such as those with high humidity, low temperatures, or strong electromagnetic interference—making them particularly suitable for winter sports, aquatic events, and other challenging conditions [16,23].
• Safety and Reliability: Fiber optic sensing systems operate without electrical current, eliminating risks of electric shock and spark generation. This ensures safe deployment even in flammable or explosive environments [3,4].
• High Sensitivity and Real-Time Capability: These sensors exhibit high responsiveness to minute physical changes, enabling high-precision data acquisition essential for detailed sports technique analysis.
• Multi-Parameter Sensing and Multiplexing Capability: A single optical fiber can incorporate multiple sensors (e.g., an array of FBGs) to enable synchronous monitoring of multiple body segments. Different types of sensors can also be integrated into a unified system, allowing simultaneous acquisition of biomechanical and physiological parameters such as temperature, strain, and vital signs [9,21,22].
• Lightweight and Wearable Design: Optical fiber sensors are inherently lightweight and can be seamlessly embedded into textiles, significantly minimizing impediments to athlete movement. Figure 5 illustrates the monitoring of human kinetics and scientific analysis employing diverse response or sensing principles.

2.4. Innovative Applications of Two-Dimensional Material Integrated Optical Fibers

Two-dimensional (2D) materials, such as graphene and transition metal dichalcogenides (TMDs, e.g., MoS₂, WS₂), exhibit exceptional properties including atomic-level thickness, ultrahigh specific surface area, outstanding photoelectric characteristics—such as high carrier mobility, tunable bandgap, and strong nonlinear effects—as well as remarkable mechanical properties like high strength and extreme flexibility. These attributes provide a revolutionary approach to overcoming the performance limitations of conventional optical fiber sensors [57,58]. By efficiently integrating 2D materials with various optical fibers, including silica fibers, POF, and micro/nanofibers, the interaction between light and matter can be significantly enhanced. This enables high-sensitivity, rapid-response, and multi-functional detection of various measurands, such as strain, pressure, temperature, and chemical/biological molecules [59,60] (Figure 6). Integration strategies are primarily categorized into the following two approaches:

• Direct Growth Methods: This approach involves the in situ growth of 2D materials directly on optical fiber substrates—such as end faces, sidewalls, or within the channels of microstructured optical fibers—using techniques like chemical vapor deposition (CVD) or atomic layer deposition (ALD). The resulting material–fiber interface exhibits strong adhesion and low optical loss, which can be reduced to approximately 0.1 dB/cm. Furthermore, the significantly increased interaction length greatly enhances nonlinear optical effects, such as signal amplification by up to 300-fold, and improves sensing sensitivity [60]. This method is particularly suitable for highly sensitive distributed or point-based biochemical sensing and intracavity laser sensing.
• Transfer-Coating Method: In this method, Pre-synthesized 2D materials—in the form of thin films or nanosheet dispersions on separate substrates—are integrated onto fiber surfaces, such as grating regions, tapered zones, or D-shaped areas, via wet transfer, dry transfer, or direct coating/spin-coating techniques. This process offers greater flexibility and facilitates integration with various types of optical fibers, including flexible POF. It is well-suited for the development of wearable sensors for strain, pressure, and biochemical detection [10,59].

2D materials significantly enhance the performance of optical fiber sensors through four core mechanisms: optical field modulation and enhancement, electronic property modulation, surface functionalization, and performance augmentation.

Table 2. Enhancement characteristics of major two-dimensional materials in optical fiber sensing and their potential for exercise physiological monitoring.

Material Type	Key Properties	Representative Fiber Structure	Sensing Application Area	Value and Potential for Exercise Physiological Monitoring	References
Graphene	High conductivity, ultra-high mechanical sensitivity, broad-spectrum absorption	Coated/tapered POF, SMF	Strain, pressure, temperature sensing	Joint/muscle activity monitoring, pressure distribution mapping in smart equipment, body temperature monitoring	[61,62,63]
Graphene Oxide (GO)	Excellent biocompatibility, abundant surface functional groups, ease of modification	Tapered MZI, SPR fiber	Biomolecule detection	Monitoring of biochemical markers in sweat (e.g., lactate, electrolytes, cortisol)	[64]
TMDs	High refractive index, tunable bandgap, strong nonlinear effects, excitonic effects	Microstructured fiber, TFBG-SPR	SPR, gas, strain sensing	High-sensitivity strain perception, detection of specific hormones or gas molecules	[65,66,67]
Black Phosphorus (BP)	Anisotropy, high in-plane carrier mobility	TFBG-SPR	Biomolecule detection	Monitoring of immune function-related proteins (e.g., immunoglobulins)	[68,69]
MXene	Metallic conductivity, high electrical conductivity, rich surface chemistry	Electrochemical sensing platform	Electrochemical sensing	Potential in exercise monitoring awaits exploration	[70,71]
2D COFs	High specific surface area, tunable pore structure, abundant active sites	Microstructured fiber	Multi-component gas/molecule identification	Analysis of respiratory gas composition	[72]
Composite Materials	Synergistic performance enhancement (e.g., MoS2-ZnO)	Coreless fiber MZI, POF	Gas sensing, strain sensing	Environmental NO2 monitoring, muscle fatigue assessment (e.g., via sweat ammonia)	[73,74]

summarizes the enhancement characteristics of major 2D materials in optical fiber sensing. Particularly in exercise physiological monitoring, materials such as graphene oxide (GO) demonstrate immense application potential due to their excellent biocompatibility and abundant surface functional groups, enabling the non-covalent adsorption of exercise-related biomarkers like hemoglobin, cortisol, and lactate.

The core advantages of 2D material-integrated optical fiber sensing lie in:

• Optical Field Localization and Enhancement: The high refractive index and atomic-level thickness of these materials effectively enhance the evanescent field of optical fibers, thereby improving the sensitivity of surface-based sensing schemes, such as surface plasmon resonance (SPR) and microring resonators [57,75].
• Multifunctional Sensing Mechanisms: The intrinsic properties of 2D materials—including optical characteristics such as absorption, fluorescence, and nonlinear coefficients; electrical properties such as resistance and capacitance; and mechanical properties—exhibit high sensitivity to external stimuli such as strain, molecular adsorption, and temperature. These responsive properties provide diverse pathways for signal modulation in fiber-optic sensors [58,59].
• Mechanical Reinforcement: The incorporation of high-strength materials, such as graphene, improves the durability and fatigue resistance of optical fibers, particularly polymer optical fibers, making them suitable for applications involving repeated deformation, such as those encountered in sports monitoring.
• Biocompatibility and Functionalization: Some 2D materials, including GO, exhibit good biocompatibility and possess abundant surface functional groups. These features facilitate specific biomolecular modifications, thereby extending their applicability to physiological parameter monitoring [75].

Table 3. Characteristics of Typical Two-Dimensional Materials for Optical Fiber Sensing and Their Potential Applications in Sports Monitoring.

Two-Dimensional Material	Primary Integration Method	Key Advantages	Potential in Sports Monitoring Applications	References
Graphene	Coating/transfer (POF/SMF), CVD growth	High electrical conductivity, superior mechanical strength, broad-spectrum optical response	Wearable strain/pressure sensing (joints, muscles), equipment condition monitoring, body temperature measurement	[58,59,75]
Graphene oxide	Coating/functionalization (POF/SMF, SPR fibers)	Excellent biocompatibility, abundant modifiable functional groups	Monitoring biochemical markers in sweat (lactate, electrolytes)	[57,75]
Transition metal dichalcogenides (TMDs)	CVD growth (microstructured fibers), transfer coating	Tunable bandgap, high nonlinear coefficient, strong excitonic effects	High-sensitivity strain sensing, specific molecular detection (hormones in sweat)	[57,60]
Heterostructures	Sequential transfer/co-growth	Enhanced performance beyond single components, multifunctional integration	High-performance saturable absorbers (laser-based physiological signal sensing)	[57,76]

summarizes the integration strategies, sensing properties, and potential applications of major two-dimensional materials in fiber-optic sensing for sports monitoring.

However, the integration of 2D materials with optical fibers still faces multiple challenges in practical exercise monitoring applications.

• Firstly, fabrication and integration processes: Large-scale, high-quality preparation and low-damage, high-strength integration with complex fiber structures (e.g., microstructured fiber channels, tapered regions) remain difficult. The high-temperature environment of CVD may be incompatible with the thermal properties of polymer optical fibers, causing substrate deformation or performance degradation. Transfer coating methods can easily introduce contamination, wrinkles, or cracks, increasing interfacial losses and affecting the sensor’s mechanical stability and optical performance consistency [60,77].
• Secondly, inherent material limitations: The zero-bandgap nature of graphene can lead to high dark current and low on/off ratio, affecting the signal-to-noise ratio; the carrier mobility of TMDs (e.g., MoS₂) in practical devices may be lower than theoretical values due to defects and interface scattering, limiting response speed; the environmental stability of materials like black phosphorus (BP) is poor, as they are prone to degradation under humid, hot, or illuminated conditions, compromising their reliability for outdoor sports or long-term monitoring [78].
• Furthermore, temperature cross-sensitivity: Fluctuations in ambient temperature can easily interfere with the accurate measurement of target parameters like strain and pressure, necessitating complex decoupling algorithms or compensation mechanisms [79].
• Finally, practical application bottlenecks: Most research remains at the laboratory proof-of-concept stage. The durability, repeatability, cost-effectiveness of large-scale preparation, and integration methods with conventional sports equipment require further in-depth study. There is still a considerable distance to go before large-scale application in real sports scenarios becomes feasible [80,81].

In summary, the integration of 2D materials with optical fibers significantly enhances overall sensor performance through synergistic multi-mechanism interactions, showcasing broad prospects in sports science—from non-invasive physiological and biochemical monitoring, movement biomechanics analysis, to smart equipment and environmental sensing. However, the transition from the laboratory to the practical sports field still urgently requires overcoming key technical challenges related to material preparation, integration processes, environmental stability, and cost.

As shown in

Table 4. Performance Comparison Between Optical Fiber Sensing Technology and Conventional Motion Monitoring Techniques.

Characteristic	Optical Fiber Sensing Technology	Inertial Measurement Unit (IMU)	Surface Electromyography (sEMG)	Optical Motion Capture	References
Measurement Accuracy	High, capable of detecting sub-microstrain levels	Moderate	Moderate	High	[2,9]
EMI Resistance	Excellent	Poor	Poor	Good	[10,16]
Environmental Adaptability	Strong, resistant to moisture and corrosion	Moderate	Moderate	Weak, requires specific lighting conditions	[82]
System Weight	Very light, under 50 g	Light, 50–200 g	Light, 50–150 g	No wearable burden	[18]
Multi-Parameter Monitoring	Yes	Limited	No	Limited	[21,22,83]
Real-Time Feedback	Yes	Yes	Yes	High latency	[3,18]
Suitable Sport Scenarios	Broad	Primarily land-based	Primarily land-based	Fixed indoor areas	[23]

, optical fiber sensing technologies are compared with conventional motion monitoring techniques, highlighting the advantages of optical fiber sensors in measurement accuracy, interference resistance, environmental adaptability, system weight, and multi-parameter monitoring capability. For instance, optical fiber sensors weigh less than 50 g, markedly lighter than IMUs. Their multi-parameter monitoring capability surpasses that of sEMG, and their environmental adaptability exceeds that of optical motion capture systems.

In summary, optical fiber sensing technology is profoundly transforming data acquisition in sports science by leveraging its core advantages, including immunity to electromagnetic interference, high sensitivity, real-time multi-parameter monitoring, and excellent wearability. From wavelength-modulated FBGs and spatially continuous distributed sensing to highly flexible POFs, along with the incorporation of innovative materials such as graphene-based coatings, diverse types of fiber optic sensors collaboratively form a high-precision, non-invasive sensing network for sports biomechanical and physiological information. These advancements deliver reliable data support for detailed analysis of athletic techniques, optimization of sports equipment, and management of training load.

Table 5. Classification and Characteristics of Optical Fiber Sensing Technologies for Sports Monitoring.

Technology Type	Principle of Operation	Key Advantages	Suitable Scenarios	Typical Application Examples	References
Fiber Grating Sensing	Bragg wavelength shift	High sensitivity (e.g., 177 pm/N), multiplexing capability	Strength training, gait analysis	Push-up pressure monitoring, shooting stability assessment	[9,18,19,21,23]
Distributed Optical Fiber Sensing	Raman/Rayleigh scattering	Spatially continuous measurement, long-distance coverage	Large venue monitoring, ice and snow temperature tracking	Temperature monitoring in Winter Olympics curling venues	[24]
Polymer Optical Fiber Sensing	Variation in light absorption by fluorescent dyes	High elasticity (withstands > 30% strain), biocompatibility	Joint motion monitoring, respiratory analysis	Finger flexion monitoring, swimming stroke analysis	[10]
Graphene-Coated Fiber Sensing	Alterations in the electrical properties of graphene	Ultra-high sensitivity, fast response time	Fine motor skill analysis, muscle dynamics	Swimming technique analysis, jump mechanics assessment	[10,19]

summarizes the classification and characteristics of major optical fiber sensing technologies used in sports monitoring.

3. Core Applications of Optical Fiber Sensing Technology in Sports Monitoring

Building upon the aforementioned technological foundations and recent advancements, the core value of optical fiber sensing technology in sports science lies in its capacity to overcome the sensing limitations inherent in traditional contact electrodes and inertial sensors. It enables unobtrusive capture and high-precision analysis of critical human physiological parameters, including respiration, heart rate, pulse wave, micro-strain, temperature, and humidity [84,85,86,87,88]. This quality of being “unobtrusive” is manifested in the sensors’ ability to be flexibly integrated into clothing, protective gear, or attached directly to the skin without significantly interfering with athletic performance [11,16,17,18]. The “high precision” is demonstrated by its millimeter-level spatial resolution, applicable in areas such as plantar pressure distribution mapping [89], millisecond-level dynamic response for tracking instantaneous changes in muscle contraction and joint angles [19,90], and a stable measurement capability resilient to complex environmental interference, including electromagnetic fields, moisture, and low temperatures [3,23,82]. Consequently, optical fiber sensing facilitates the continuous and accurate acquisition of biomechanical and physiological signals that were previously difficult to capture reliably in real-world sports settings, thereby providing an unprecedented data foundation for both sports science research and training practices.

3.1. Monitoring of Human Physiological Parameters

The real-time acquisition of respiratory and metabolic indicators is essential for endurance training [86]. In recent years, there has been a significant increase in interest toward applying FBG technology for measuring vital signs [91,92,93,94]. The advantages of optical fiber-based wearable devices enable reliable and unobtrusive monitoring of multiple physiological parameters (Figure 7), including respiratory rate, pulse, body temperature, and humidity. Furthermore, this non-invasive approach eliminates the need for cumbersome attachments and offers a comfortable user experience. Novel flexible optical sensor designs are increasingly focused on achieving non-invasive, continuous monitoring of key physiological signals such as respiration and pulse. These capabilities make the technology suitable for physical activity tracking, stress management, and detection of abnormalities potentially associated with cardiac conditions.

• Respiratory Monitoring: An array of FBG sensors embedded in an elastic belt or vest monitors strain variations in thoracic and abdominal circumference induced by respiration, from which respiratory frequency and depth can be derived. This approach offers notable advantages, including strong resistance to interference—making it suitable for swimming and electromagnetic environments—excellent conformity to the body, and higher accuracy compared to piezoresistive sensors [86,87], Figure 8 presents a schematic diagram of the respiratory sensor interfaced with the measurement circuit and a personal computer (PC). Integrated FBG textiles have been demonstrated for respiratory rate detection, while 3D-printed encapsulation enhances wearing comfort. An alternative low-cost strategy involves POF combined with smartphone camera-based detection of respiratory waveforms. A high-performance example is the Light Lace chest strap, which measures intensity changes in optical fiber under stretching to simultaneously determine maximal oxygen uptake (VO₂max), ventilation volume, and respiratory rate. This system achieves a sampling rate ten times faster than electronic sensors and remains unaffected by sweat [87].
• Cardiovascular Monitoring: FBG or POF-based wristbands can monitor heart rate and radial arterial pulse waveforms. By employing signal processing algorithms, such as wavelet denoising and feature point extraction, in combination with physiological models, blood pressure can be estimated with an error margin of less than 5.8 mmHg. A system with superior performance incorporates a graphene-PDMS optical fiber wristband that derives blood pressure from radial artery pulse waveforms, achieving a deviation of approximately $\pm$2 mmHg. Figure 9 demonstrates selected practical application cases based on fiber-optic blood pressure monitoring technology. This method is further enhanced by wavelet denoising algorithms to improve robustness [99,100]. It is noteworthy that anti-interference signal processing strategies developed for FBG-based non-invasive fetal heart rate monitoring [90] offer valuable insights for enhancing the robustness of pulse wave monitoring in dynamic conditions, such as during physical activity in pregnant women.
• Temperature and Humidity Monitoring: Body Temperature Monitoring: Microfiber optic sensors encapsulated with PDMS, which exhibit a sensitivity of approximately 0.02 dBm/$℃$, are utilized for dynamic skin temperature monitoring and early warning of heat stress. Distributed FBG sensor networks integrated into smart garments enable temperature measurement through multi-point weighted averaging, achieving an accuracy of $\pm$0.2 °C [84,85]. The PDMS coating enhances adhesion to the skin and improves temperature measurement precision to $\pm$0.1 °C. Relevant developments include a Mach–Zehnder interferometer-based temperature sensor developed by Wang et al. for real-time body temperature monitoring during tumor thermotherapy, as well as a stretchable polymer optical fiber temperature sensor fabricated by Guo et al. [101]. Humidity Monitoring: Optical fibers functionalized with specialized coatings such as polyacrylamide hydrochloride/silica (PAH/SiO₂) have been deployed to monitor humidity levels within sportswear. These sensors demonstrate a sensitivity of 3.02 mV/%RH, enabling accurate comparison of the moisture-wicking properties between cotton and polyester fabrics and facilitating objective evaluation of textile moisture management performance [82].
• Metabolite and Biochemical Indicator Monitoring: Fiber-optic sensor-based optical wearables also enable non-invasive monitoring of biochemical indicators in biofluids such as sweat, saliva, and tears [102]. Such monitoring facilitates the optimization of fitness regimens and daily activities. Recent developments include photonically intelligent bandages capable of simultaneously measuring pressure and pH levels in wound areas to assess healing progress. Meanwhile, sweat lactate detection remains primarily confined to laboratory-scale investigations [103].
• Multi-Parameter Simultaneous Monitoring: Optical fiber systems integrating piezoelectric sensing principles demonstrate the potential for synchronized non-invasive monitoring of multiple vital signs, such as heart rate and respiratory activity [100,104]. This capability provides a more comprehensive dataset for integrated physiological status assessment during physical exercise.

3.2. Monitoring of Biomechanical Parameters

3.2.1. Muscle Activity and Vibration Monitoring

In addition to physiological parameters, optical fiber sensing plays a significant role in capturing real-time muscle activity, such as contraction and vibration [105]. The underlying mechanism fundamentally relies on the highly sensitive detection of micro-displacement caused by strain or vibration resulting from muscle contraction, often combined with indirect measurement of specific physiological signals. The primary methodologies are outlined below:

• Fiber-Optic Strain Sensing: As the core technique for directly monitoring contractile deformation, this method involves attaching highly elastic optical fibers—such as silicone-encapsulated or specialized polymer optical fibers—along the direction of muscle fibers on the skin surface. During muscle contraction, expansion of the muscle belly or increased tendon tension induces slight stretching or bending of the optical fiber. As illustrated in Figure 10, this mechanical deformation alters the internal light transmission characteristics, manifesting as optical power loss, wavelength shift, or mode coupling. Quantification of muscle contraction magnitude and dynamics is achieved by demodulating these optical variations [105]. Fiber-Optic Vibration Sensing (Based on Acceleration Principle): Muscle contraction or fatigue can generate subtle vibrations, such as muscle sound or mechanomyographic signals. Optical fiber sensors exhibit high acceleration sensitivity and can detect these minute vibrations when fixed on the muscle surface. Vibratory accelerations induce corresponding strain or phase modulation within the optical fiber, which is subsequently demodulated. This signal enables analysis of muscle activation patterns, coordination, and potential fatigue states.

• Photoplethysmography (PPG): PPG utilizes infrared light directed into subcutaneous tissue and detects variations in transmitted or reflected light intensity to assess changes in local microvascular blood volume, thereby indirectly evaluating muscle activation status. Given the strong correlation between muscular activity and localized blood flow, multiplexed PPG systems enable synchronous monitoring of activation status, timing, and relative intensity across multiple muscle groups. This provides valuable information for assessing neuromuscular function [83].

This technology is predominantly applied in research focused on muscle fatigue monitoring and functional assessment.

• Real-Time Fatigue Metabolite Monitoring: Fiber-optic sensing technology has been extended to the monitoring of exercise-related biochemical markers. For instance, D-shaped optical fiber SPR sensors or tilted fiber Bragg grating (TFBG) sensors can be employed for real-time in situ detection of sweat pH. Furthermore, the integration of optical sensing elements with microfluidic and electrochemical units—such as in sweat lactate sensing patches—enables dynamic and continuous monitoring of key fatigue-related metabolic indicators including glucose, lactate, and electrolytes during exercise [106]. Real-time changes in these biochemical parameters provide direct evidence for evaluating metabolic load, fatigue accumulation, and energy metabolism status under physical exertion.
• Neuromuscular Coordination Assessment: The high sensitivity and flexible nature of optical fiber sensing render it particularly suitable for capturing minute strain generated by fine muscle activities. For instance, flexible patches based on PDMS-encapsulated optical fibers have been developed and attached to the neck to unobtrusively and accurately detect micro-strain, typically less than 5%, induced by laryngeal muscle movement during speech or swallowing. The analysis of such micro-strain dynamics provides a novel methodological framework for quantitatively assessing task-specific neuromuscular control and coordination [105].
• Comprehensive Assessment: Multimodal optical fiber systems, which integrate fiber-optic strain and/or vibration sensing, PPG, and biochemical sensing, are emerging as powerful tools for investigating muscle fatigue mechanisms and conducting functional assessments. By simultaneously acquiring data on mechanical muscle activity—including contraction amplitude, frequency, and vibration spectrum—local hemodynamic responses, and changes in key metabolite concentrations, these systems enable the construction of highly detailed muscular state models. Such integrated capability facilitates accurate identification of fatigue thresholds, evaluation of the efficacy of various training interventions, and early warning of the risk of sports-related injuries.

3.2.2. Joint Angle Monitoring

Joint angle monitoring represents one of the core strengths of fiber-optic sensing technology [107]. By attaching wearable optical fiber sensors to multiple human joints, such as the interphalangeal, metacarpophalangeal, wrist, elbow, knee, and ankle, this approach enables continuous monitoring of both fundamental movements and complex motion patterns during specific activities [90] (Figure 11).

Representative Studies and Technologies: Notable research includes a knee joint monitoring system developed by Pant et al. based on FBGs, which was validated against a Polhemus sensor for consistency. The FBG array demonstrated the capability to reconstruct three-dimensional knee motion trajectories. The Tavares team subsequently developed an FBG array system for wheelchair pressure monitoring, capable of tracking pressure variations in key anatomical regions including the scapula, ischium, and heel. The FBG-based goniometer enables high-resolution dynamic joint angle measurement—with resolutions as fine as 0.06$°$—in joints including the knee, elbow, wrist, and ankle [107]. This technology is widely applied in gait analysis, golf swing monitoring, and shooting posture recognition [19,23]. Alternatively, hetero-core POFs or elastic optical fibers can be directly integrated into garments via sewing [16]. These sensors track joint angles and limb rotation in real time through mechanisms such as bending-induced optical loss, achieving a sensitivity of up to 0.63 Rad⁻¹ [108], thereby enabling flexible joint motion tracking. In ice sports applications, such systems have demonstrated a joint angle measurement accuracy of $\pm$0.3$°$ [109]. Figure 12 shows selected examples of joint motion sensors.

3.2.3. Whole-Body Motion and Gait Analysis

Beyond single-joint monitoring, optical fiber sensing demonstrates strong capabilities in capturing whole-body movements and gait patterns [110].

• Trunk Motion: A dual-plane biomechanical model integrated with a sparse array of optical fiber sensors can effectively detect trunk movements including forward flexion, lateral bending, and axial rotation [16]. A more advanced approach, developed by researchers from Chongqing Normal University, combines human pose tracking algorithms with simulated fiber-optic sensor data to achieve real-time three-dimensional reconstruction of body motion [110]. By tracking strain at key skeletal locations, the system translates optical signals into biomechanical parameters such as joint angles and accelerations, achieving an error rate below 2.5% and significantly outperforming conventional image-based analysis methods [110].
• Plantar Pressure Monitoring: Smart insoles integrated with high-density FBG or POF sensor arrays, such as an 8 × 8 grid, enable dynamic pressure distribution mapping with high spatial resolution up to the millimeter level and a sampling rate of 100 Hz [111], as visually demonstrated in Figure 13 [83]. Embedding POF sensors within insoles facilitates dynamic gait analysis with a sensitivity of 0.5 N/cm² [19].

The value of this technology is reflected in several aspects:

• Accurate identification of all gait phases including initial contact, stance, and push-off;
• Detection of abnormal gait patterns such as overpronation, supination, and limping;
• Analysis of foot strike patterns and center of pressure trajectory to optimize running form and prevent injuries, for example, issuing alerts when peak pressure exceeds 2.5 times body weight [89];
• Integration with intelligent algorithms and machine learning to analyze spatiotemporal pressure characteristics for early warning of foot pathologies, such as plantar fasciitis and diabetic foot, with recognition rates exceeding 90% [111].

A successful case was reported by Leal-Junior et al., who embedded commercial PMMA polymer optical fiber sensors into 3D-printed instrumented insoles to enable both static and dynamic assessment of plantar pressure and ground reaction forces (GRF). Through wavelength-division multiplexing, only two photodetectors were required to support 15 intensity-modulated sensor elements within a single optical fiber, allowing simultaneous monitoring of 15 distinct plantar pressure regions. Tests conducted on a cohort of 20 volunteers under static and dynamic conditions demonstrated strong agreement between the sensing system and a commercial force platform. The system exhibited an error of less than 3.4% in detecting gait events and estimating user body weight.

Table 6. Performance Comparison: Optical Fiber Smart Insoles vs. Conventional Piezoresistive Technology.

Technical Indicator	Conventional Piezoresistive	Optical Fiber Smart Insoles	Advantage	References
Number of Sensors	$<$10	22 (equivalent)	3-fold enhancement in spatial resolution	[112]
Pressure Mapping Accuracy	$\pm$15%	$\pm$5%	Clear differentiation of pressure dynamics between walking and running	[113]
EMI Immunity	Weak	Excellent (>60 dB rejection)	Compatibility with electromagnetic therapy environments	[3,18]
Power Supply Method	Wired/battery replacement	Self-powered via photovoltaic textiles	No performance degradation after 180,000 test cycles	[2,8]

compares the performance of optical fiber smart insoles with traditional piezoresistive technology, highlighting the advantages of optical fiber sensing in pressure mapping accuracy, immunity to electromagnetic interference, and power supply modality.

3.3. Integration into Intelligent Sports Equipment and Protective Gear

Rapid economic and technological advancement has significantly increased focus on physical activity and its monitoring [113]. This trend is especially prominent in professional sports and medical rehabilitation, where wearable motion-sensing technologies are increasingly employed for rehabilitation, injury prevention, training optimization, and stress assessment. Monitoring physiological parameters during physical activity is highly challenging due to movement artifacts and environmental interference. Optical fiber sensing systems must therefore possess robust motion noise rejection capabilities [105]. An effective strategy is the integration of fiber optic sensors into sports textiles, enabling the development of wearable smart sports systems with closed-loop “sensing–feedback” functionality [17], as demonstrated in Figure 14.

Conventional silica optical fiber sensors are inherently brittle and exhibit low strain tolerance, making them poorly suited for monitoring high-amplitude human movements such as finger joint flexion, which can induce large deformations. This fundamental limitation long hindered the adoption of fiber-optic technology in motion sensing applications. However, the rapid development of wearable sensors in recent years has accelerated research into novel flexible materials [114,115].

Addressing this challenge, a research team from Tsinghua University [10] developed a stretchable optical fiber capable of withstanding over 30% strain by using PDMS to replace conventional glass and doping it with the fluorescent dye Rhodamine B (Figure 15). The working principle relies on the variation in absorbed light intensity as the fiber is stretched: joint bending angles are quantified by measuring the attenuation of transmitted light through a beam splitter. Experimental results demonstrated that the sensor achieves a strain measurement accuracy of 36% in monitoring finger joint motion and maintains mechanical stability after 500 stretching cycles.

Furthermore, Fan Chen et al. from Northeast Petroleum University proposed a pre-bent U-shaped microfiber structure embedded within multilayer PDMS films, which significantly enhanced the sensitivity to large-angle bending (0–120$°$) with a linear response of 0.996, enabling precise tracking of finger flexion-extension trajectories [116]. Additionally, optimization studies on 3D-printed strain sensors for wearable respiration and heartbeat monitoring provide critical insights into integrating optical fiber sensors with additive manufacturing technologies to develop customized, high-performance motion monitoring devices [98,117].

Textiles embedded with FBG or elastic optical fiber arrays enable real-time monitoring of cardiopulmonary function [17,84,86,87] and recognition of basic motion patterns [16,17]. For instance, Li Hongqiang’s team developed an aramid-protected optical fiber configuration that achieved real-time cardiopulmonary function tracking in marathon runners [84] (Figure 16). Looking ahead, current applications of optical sensors in medical wearable devices demonstrate substantial potential for fiber optic sensing in achieving medical-grade motion and health monitoring [118].

Integrating optical fiber sensors into sports protective equipment enables the early warning of muscle fatigue or injury risks by monitoring physiological changes such as respiratory rhythm variation (CV) or reduced range of motion [91,119]. For example, an FBG-based lumbar posture monitoring system can alert users to potential sports injury risks. Embedding FBGs into knee braces allows real-time monitoring of patellar tracking, ligament stress, and varus angle [82,90], while vibration feedback prompts posture correction to mitigate abnormal movements and reduce the risk of anterior cruciate ligament (ACL) injury [6]. Fiber-based gait analysis systems identify insufficient landing absorption—characterized by a knee flexion angle of less than 30$°$—and incorporate feedback mechanisms for movement correction [120]. Among these applications, a novel wearable sensing system based on FBG, reported by Zaltieri et al., has been successfully used to monitor the flexion/extension (F/E) of the wearer’s lumbar spine to correct sitting postures. Meanwhile, the application of optical fiber sensing can enhance safety in sports activities; for instance, optical fiber jackets integrated with light-emitting elements (LEDs) improve visibility during nighttime exercise.

3.4. Equipment and Environmental Monitoring

The application of optical fiber sensing has also been extended to sports equipment itself. The underlying principle involves embedding FBGs or distributed optical fiber sensors into critical components, such as bicycle frames or rowing oar shafts. Specific applications include:

• Stress Monitoring: Real-time assessment of load distribution and torsional loads to evaluate equipment performance and structural health [24].
• Damage Warning: Detection of abnormal strain concentrations caused by microcracks—with high localization accuracy via distributed optical sensing—enabling warnings long before damage becomes visually detectable [24].
• Simultaneous Environmental Monitoring: Integration with temperature and humidity sensors to assess environmental risks during training [38] (Figure 17). For instance, distributed optical fibers have been used to monitor structural safety in low-temperature environments such as ski resorts.
• Performance Optimization: Analysis of force distribution during rowing via FBG data from oar shafts to guide improvements in carbon fiber layup design, thereby enhancing efficiency and service life [23].

Based on the above analysis, optical fiber sensing technology has successfully overcome the limitations of traditional motion monitoring techniques, such as capturing physiological parameters, analyzing biomechanics, and adapting to complex environments, by leveraging its core advantages including unobtrusive integration, multi-modal monitoring, high-precision sensing, and strong environmental robustness. This technology provides a real-time, accurate, and multi-dimensional data foundation for sports science research and training practice, demonstrating profound and multi-faceted application value in the field of athletic monitoring. In monitoring human physiological parameters, it accurately captures key metrics such as respiratory rate, heart rate, blood pressure, temperature, and humidity, offering reliable data for endurance training and health assessment. In the domain of biomechanical monitoring, optical fiber sensing excels in applications ranging from muscle activity to full-body gait analysis. It interprets muscle contraction and fatigue status through strain and vibration monitoring, captures intricate movement details via high-resolution joint angle measurement, and enables 3D motion reconstruction and plantar pressure distribution mapping through full-body sensor networks, thereby supplying quantitative support for movement optimization and injury prevention. Furthermore, by empowering smart equipment and protective gear, this technology is facilitating a paradigm shift toward intelligent, personalized, and preventive approaches in both competitive sports and public health. It delivers innovative technological support for training practice, health management, and rehabilitation assistance, marking a significant advancement in sports science and engineering applications.

Table 7. Comparison of performance parameters for different motion monitoring sensing technologies.

Technology Type	Monitoring Parameters	Precision/Sensitivity	Response Time	Cost-Effectiveness	Primary Application Scenarios	References
Flexible Optical Fiber Sensors	Joint angle, gait, muscle activity	$\mathrm{Angle} \mathrm{resolution}: 0.01°$ Stress resolution: 10−4 N Sensitivity: 2.99 V/N	$<$0.3 s 35 ms end-to-end delay	Medium (Higher equipment cost but strong durability)	Neurodegenerative disease detection Gesture recognition	[81,121,122,123,124]
6G Optical Sensing Network	Biomechanical parameters, joint angle, displacement	$\mathrm{Spatial} \mathrm{resolution}: \pm$$1 \mathrm{mm} \mathrm{Position} \mathrm{accuracy}: \pm$0.01 m	$<$100 ms	High (Complex system but multi-user shared)	Real-time feedback for athletic training Multi-athlete synchronous monitoring	[125,126]
Inertial Measurement Unit (IMU)	Acceleration, angular velocity, motion trajectory	$\pm$16 g (Accelerometer)	200–300 ms	Low (Widely used but limited precision)	Basic motion analysis Daily activity monitoring	[127,128,129]
Optical Motion Capture System	Motion trajectory, joint angle	$\mathrm{Angular} \mathrm{displacement} \mathrm{error}: \pm$$0.5°$	Limited by sampling rate	High (High precision but environmentally constrained)	Laboratory motion analysis Biomechanics research	[130,131,132]
Surface Electromyography Sensors (sEMG)	Muscle electrical activity, muscle fatigue	Sensitivity threshold: ~100 mN	Dependent on signal processing	Medium	Muscle activation pattern analysis Rehabilitation assessment	[133,134]
Dual-mode Optical Fiber Strain Sensor	Strain, joint bending	Strain range: 0–80% Sensitivity: 1.90	Capable of rapid response	Medium	Full-body motion detection Human–computer interaction	[135,136,137]

provides a comprehensive comparison of the key performance indicators of various motion monitoring technologies.

The advantages of optical fiber sensing technology are particularly evident in specific motion monitoring scenarios.

Table 8. Comparison of technology applicability in different motion monitoring scenarios.

Application Scenario	Technology Type	Precision Performance	Response Speed	Environmental Adaptability	System Cost	References
Fine Gesture Recognition	Optical Fiber FMG System	87.67% accuracy for 12 gestures	35 ms delay	EMI resistant, sweat resistant	Medium	[138,139]
	Traditional Resistive FMG	~100 mN threshold precision	Slower response	Susceptible to sweat	Low
Athletic Training Feedback	6G-OPS System	$\mathrm{Position} \mathrm{accuracy} \pm$0.01 m	$<$100 ms	Suitable for outdoor environments	High	[140,141]
	GPS/IMU System	Limited precision	200–300 ms delay	Suitable for outdoors	Medium
Joint Angle Monitoring	Optical Fiber Curvature Sensor	Curvature range 0–8.43 m−1	Rapid response	Flexible, wearable	Medium	[142,143,144]
	Silica Optical Fiber Sensor	$\mathrm{High} \mathrm{sensitivity} \mathrm{but} \mathrm{limited} \mathrm{range} (<$5 m−1)	Depends on system configuration	Relatively rigid	High
Plantar Pressure Distribution	Flexible Optical Fiber Composite Sensing	97% offline recognition rate 93% online recognition rate	$<$0.3 s	Can be embedded in insoles	Medium	[19,145,146,147]
	Pressure Sensor Array	Limited spatial resolution	Medium	Susceptible to temperature	Low-Medium
Full-Body Motion Capture	Dual-mode Optical Fiber Sensor	Strain range 0–80%	Rapid response	Flexible, stretchable	Medium	[148,149]
	Optical Motion Capture	High precision but marker-dependent	Limited by camera frame rate	Laboratory environment	High

compares the performance of different technologies across various sports applications, further demonstrating the significant advantages of optical fiber sensing technology in motion monitoring, particularly its high sensitivity and precision, rapid response characteristics, strong anti-interference capability, and multi-parameter integration capacity. Regarding cost-effectiveness, although the initial equipment cost of optical fiber sensing technology may be higher than that of traditional sensors (e.g., IMUs or sEMG), its long lifespan, low maintenance requirements, and multi-parameter integration capability confer a prominent long-term cost advantage. This is especially true in elite sports and scientific research fields requiring high-precision monitoring, where the accurate data provided by optical fiber sensing technology can assist athletes in precisely refining technical movements and improving training efficiency, thereby leading to significant performance gains at critical moments. Furthermore, the corrosion resistance and fatigue resistance of optical fiber sensors reduce the frequency of equipment replacement, lowering the total cost of ownership in the long term.

4. Integrated Applications of Optical Fiber Sensing Technology in Athletic Training and Health Monitoring

Parameters captured by optical fiber sensing technology can be utilized to monitor athletic performance. In some specialized sports domains, integration and comprehensive analysis of these parameters have already been achieved. For instance, range of motion (ROM) and joint angles provide meaningful feedback for defining athletic movement strategies or ensuring proper execution of techniques in specific sports [150,151,152]. The intensity and trajectory of body movements, as well as cardiopulmonary parameters—such as heart rate (HR), blood oxygen saturation (SpO₂), and respiratory rate (RR)—during prolonged physical activity carry information about athlete fatigue and declining physical capacity [153]. These parameters offer critical insights into an athlete’s health status. Additional relevant metrics commonly monitored during athletic activity tracking include proper execution of running or jumping [154,155], striking force in combat sports [156], reaction time [157], and acceleration during movement [158,159,160,161,162].

4.1. Sport-Specific Motion Posture Analysis

Optical fiber sensing technology, leveraging its core advantages, occupies a unique position in the high-precision measurement of sports biomechanical parameters. It is particularly suited for scenarios requiring resilience under complex environmental interference and synchronous multi-parameter monitoring, offering substantial value in sport-specific posture analysis by enabling accurate reconstruction of complex motion sequences. In terrestrial sports such as ball games, optical fiber sensor networks embedded into athletic garments facilitate high spatiotemporal resolution monitoring of joint coordination, movement trajectory, and force application patterns during actions such as swinging, throwing, or kicking. This capability enables the identification of technical deficiencies—including improper actuation sequences—and provides quantitative feedback for kinematic optimization [10]. Integration with artificial intelligence architectures, such as Long Short-Term Memory (LSTM) networks, further supports prediction of movement intent [163]. For aquatic sports including swimming, waterproof configurations employing graphene-coated optical fibers integrated into swimwear have been successfully deployed to monitor freestyle S-shaped pull trajectory, kick amplitude, and trunk roll angle. These systems derive propulsive efficiency indices to guide technical refinement and reduce energy expenditure [117]. In winter sports and among special populations, optical fiber systems functionalized with advanced materials—such as supramolecular polymers and triboelectric nanogenerators (TENG)—enable high-fidelity gait characteristic extraction [7,164]. These systems have been deployed in foot arch health assessment (with an accuracy of 97.5%) and monitoring of walking states in visually impaired individuals (99.6% accuracy), thereby enhancing safety during physical activities [113,165].

Specifically, in shooting sports, the scientific and reliable monitoring of grip posture and arm angles is critical for athletic training and competition performance. Gao et al. innovatively integrated a FBG sensor array into key force-bearing points of shooters, such as the stock and grip [23]. As shown in Figure 18, by continuously monitoring minute strain variations associated with finger and arm muscle movements, their system enables non-invasive, real-time, and highly sensitive wearable motion recognition. Combined with algorithmic analysis, this approach provides coaches with an objective “stability index” to identify technical deficiencies—such as increased tremor due to fatigue in specific muscle groups—and facilitates personalized training adjustments, significantly enhancing the athlete’s performance consistency under competitive pressure.

Owing to their corrosion resistance, moisture immunity, and embeddability, FBG sensors have also been successfully deployed in monitoring critical stress points on rowing oar shafts, thereby guiding the layup design of carbon fiber blades [24]. By integrating multiple FBGs in both the handle and blade regions of carbon fiber oars, the system captures dynamic bending moments and torsional loads experienced by the shaft during different phases of the rowing cycle—entry, drive, exit, and recovery. The FBG-based sensing system delivers high-precision mechanical data that are difficult to acquire reliably using conventional strain gauges under wet and high-vibration conditions. This optical fiber-based blade pressure monitoring system comprises a processor, wireless transceiver, optical sensing array distributed along the blade, and a light source module. This approach resolves issues inherent in traditional measurement techniques, providing stable and accurate data without the need for underwater cameras, thereby reducing overall system cost and complexity. The acquired data are used not only to evaluate technical aspects of athlete performance—such as peak force location and power output profile—but also, more importantly, to provide direct experimental validation for structural design optimizations of carbon fiber blades. These include ply stacking sequence adjustment and localized reinforcement, ultimately enhancing propulsion efficiency and service life [166].

The golf swing constitutes a complex dynamic process involving coordinated multi-joint movement throughout the body. Leveraging the multiplexing capability of FBG sensors, researchers distributed a sensor network across the athlete’s spinal support belt, key upper limb segments, and the club grip [22]. The system simultaneously captured dynamic changes in trunk rotation angle, shoulder–elbow joint torque transmission, and grip force distribution during the swing, enabling the construction of a comprehensive “body–club” kinetic model. Through comparative analysis of data from athletes of varying skill levels, key technical parameters—including backswing top position, downswing initiation timing, and wrist release angle at impact—were accurately quantified. This approach provides unprecedented high-precision, multi-dimensional data support for biomechanical diagnosis and optimization of the golf swing [16].

The golf swing motion consists of the following process: (i) taking a back swing, (ii) leading up to an impact, and (iii) following through, as shown in the upper inset in Figure 19. Figure 19 and Figure 20 present real-time monitoring results of a golf swing motion. By analyzing the ratio and time differential of ΔD₁ and ΔD₂, the system identifies distinct phases of the swing, such as backswing, impact, and follow-through. The results demonstrate that the proposed system effectively analyzes complex athletic motions.

A research team from Nanchang University developed an FBG-based pressure sensing unit for push-up monitoring [18]. The unit employs a simply supported beam structure for mechanical sensitivity enhancement, achieving a theoretical sensitivity of 186.5 pm/N. Through finite element simulation and experimental calibration, excellent linearity (goodness-of-fit > 99%) and low hysteresis error (1.49%) between the applied pressure and the FBG wavelength shift were validated, with repeatability error constrained within 8.94%. The finalized sensitivity was calibrated at 177.55 pm/N, deviating only 2.53% from the theoretical value. A monitoring system developed on the LabVIEW platform, integrated with a GRMOPC wireless communication module, successfully realized real-time acquisition and wireless transmission of hand pressure distribution, repetition count (with an error of only 1–2 repetitions), and exercise frequency during push-ups. The successful application of FBG sensors in monitoring push-up performance demonstrates their practical value in quantifying basic training load, assessing movement standardization, and providing immediate feedback.

In swimming, optical fiber sensors embedded in the shoulder area of swimsuits analyze stroke phase and efficiency, enabling the quantification of movement symmetry and the correction of technique degradation due to athlete fatigue [164]. In long jump, plantar pressure monitoring using optical fiber arrays assists in optimizing take-off technique [89]. FBG-equipped smart garments help shooting athletes stabilize heart rate and maintain posture [23]. Additionally, optical fiber sensors capture wrist pressure during basketball jump shots and ankle torque during soccer kicks to refine movement patterns and enhance technical standardization [10,117], as illustrated in Figure 21.

4.2. Monitoring of Athletic Training Load

The core of scientific training lies in the precise control of training load intensity and volume, for which optical fiber sensing technology offers an objective quantification tool.

• In strength training, FBG-based pressure transducers not only achieve accurate repetition counting (e.g., with an error margin of only 1–2 counts in push-up repetitions) [18], but also enable fine-grained analysis of deep metrics such as movement velocity, range-of-motion consistency, pressure distribution, and concentric-to-eccentric velocity ratios. This provides comprehensive feedback for strength training optimization [6].
• For endurance training, elastic optical fiber chest bands accurately monitor respiratory frequency, depth, and rhythm [86,87], revealing correlations between breathing patterns and aerobic capacity or fatigue thresholds. These metrics offer novel insights for endurance assessment and training intensity prescription [86].
• In group training settings, multi-parameter synchronous monitoring systems track exercise intensity—including acceleration, deceleration, and change-of-direction force or frequency—across multiple athletes. When combined with physiological data such as heart rate, these systems generate individualized load indices. This supports coaches in designing scientifically grounded training programs that avoid both excessive fatigue and inadequate stimulation [6].

4.3. Athletic Health and Injury Prevention

Sports injuries resulting from overtraining significantly impact athletic careers, and optical fiber sensing technology offers novel means for early risk identification.

• Foot Health Screening: Optical fiber-based plantar pressure monitoring, combined with artificial intelligence analysis, enables efficient identification of structural abnormalities such as high arches and flat feet (accuracy > 97.5%) [111]. This supports the selection of appropriate footwear and the configuration of orthopedic devices.
• Sports Injury Warning: Flexible sensors (e.g., Ti₃C₂-based devices) attached to joints, along with machine learning algorithms, continuously record range-of-motion improvement curves [115]. These systems enable real-time monitoring of athletes’ physical status—such as joint load and muscle fatigue—facilitating the early detection of potential injury risks. Targeted preventive rehabilitation training can then be applied, helping athletes avoid training and competition interruptions due to injury, thereby improving training efficiency and competitive performance [111,119]. Optical fiber plantar pressure systems analyze real-time gait parameters including foot strike pattern, stance time, and center of pressure trajectory. By establishing individual baselines, they alert to abnormal patterns (e.g., unilateral overload or center of pressure deviation), prompting adjustments in running technique or rest scheduling to prevent injuries such as stress fractures and plantar fasciitis [89]. Knee and ankle joint angles are critical in injury prevention; specifically, the initial contact angle (IC), the maximum angle during mid-stance (MAX), and the delay between IC and MAX undergo significant changes during long-distance running and are also influenced by the type of footwear worn [167]. Furthermore, analysis of kinetic data allows for the calculation of elastic properties of ligament structures in knee extensors and plantar flexors [168].
• Anti-Doping Applications: Optical fiber biosensor technology is also being explored for non-invasive or minimally invasive detection of prohibited substances (doping) in sports, thereby ensuring competition integrity and athlete health [169,170].

4.4. Sports Rehabilitation and Assistive Technology Optimization

Optical fiber sensing technology has opened new pathways in the field of sports rehabilitation, where its biocompatibility and precise quantification capabilities provide reliable tools for assessing recovery progress. The integration of optical fibers with sEMG enables high-accuracy (greater than 95%) classification and evaluation of rehabilitation exercises through algorithms such as support vector machines (SVM) [6]. Wearable optical fiber networks continuously monitor key parameters including joint ROM, muscle activation timing, and weight-bearing symmetry, generating quantitative recovery curves. This approach overcomes limitations inherent in traditional instantaneous assessments, such as manual ROM measurements, by eliminating observer bias and providing data-driven support for the dynamic adjustment of rehabilitation protocols [113,171,172]. Figure 22 depicts a soft rehabilitation glove based on an optical fiber sensor, which is capable of performing a range of motions including fist closure, hand opening, and precision grip.

Specifically, in joint functional rehabilitation, continuous monitoring of ROM and activities of daily living (ADL) is performed to accurately reflect functional recovery progress, identify rehabilitation plateaus, and guide physical therapists in adjusting treatment plans [113]. In neurological rehabilitation, optical fiber sensing integrated with exoskeletons detects patients’ voluntary movement intent and provides timely, proportional assistance. This forms a closed-loop human–machine interaction system that promotes neural pathway remodeling, enhances motor control, and ensures high operational safety [113]. In the context of visual impairment assistance, an optical fiber system incorporating visual analysis technology and vibration sensing [165] analyzes gait and environmental vibration information to identify walking conditions—such as level ground, stairs, or obstacles—with high accuracy (99.6%) [113]. When integrated with voice guidance, it assists visually impaired individuals in engaging in safer physical activities. Schematic illustrations of the primary lower-limb angles targeted for rehabilitation monitoring and motion tracking are provided in Figure 23.

As mentioned above, optical fiber sensing technology has deeply penetrated multiple dimensions of sports science—from the analysis of movement biomechanics and quantification of training load to early health risk warnings and assistance in sports rehabilitation. Its applications encompass biomechanical movement analysis, quantitative training load assessment, early health risk detection, and motor rehabilitation assistance. In motion capture and posture analysis, optical fibers embedded in athletic garments or equipment accurately acquire critical biomechanical parameters—such as joint coordination, force exertion patterns, and movement trajectories—in sports including ball games, swimming, shooting, rowing, and golf, providing data-driven insights for technique optimization. For training load monitoring, the technology delivers comprehensive feedback on intensity and volume metrics during strength and endurance exercises, enabling precise control over training regimens. In health and injury prevention, it allows real-time detection of abnormal movement patterns, assists in foot health screening, and supports anti-doping efforts. Within rehabilitation and assistive technology, it aids motor recovery and special populations through high-accuracy motion classification, joint function tracking, and closed-loop human–machine interaction control. These implementations demonstrate that fiber optic sensing not only addresses limitations of conventional measurement systems under dynamic conditions but also promotes a transition from subjective training methodologies to scientifically grounded and precision-oriented practices. By supplying quantifiable performance and safety indicators, it introduces innovative technological pathways for enhancing athletic outcomes, reducing injury risks, and improving rehabilitation efficacy. The proliferation of this technology underscores its practical utility and future potential within sports engineering and human performance science, serving as a critical enabler for both elite sports advancement and public health initiatives.

5. Challenges and Future Directions of Optical Fiber Sensing Technology in Sports Monitoring Applications

5.1. Technical Bottlenecks and Current Challenges

Despite its significant potential in sports monitoring, the broad adoption of fiber optic sensing technology still faces multiple challenges. These include the need for enhanced sensitivity and improved signal-to-noise ratio, advances in flexible materials and encapsulation technologies, as well as difficulties in system integration and miniaturization.

Specifically, Motion Artifacts and Signal Processing: Intense physical activity may cause sensor displacement or introduce additional noise, compromising signal quality [3,11]. It is necessary to integrate data from IMUs and other motion sensors to develop models that suppress motion-induced interference, thereby enhancing system robustness in dynamic environments [6]. Particularly in distributed optical fiber vibration sensing, motion interference significantly reduces the signal-to-noise ratio, necessitating advanced signal processing techniques such as wavelet denoising and moving average filtering. Recent research indicates that denoising algorithms based on Empirical Mode Decomposition (EMD) and Butterworth filters can effectively suppress noise in raw signal data, improving the signal-to-noise ratio for artificial impact signals and mechanical vibration signals by 3.01 dB and 5.12 dB, respectively. This enhancement increases the sensitivity of $\mathsf{\Phi }$-OTDR systems and significantly improves monitoring performance in dynamic environments [173,174,175,176,177].

Cross-Sensitivity to Multiple Parameters: A single sensor, particularly FBG, may respond simultaneously to multiple physical quantities such as strain and temperature, leading to signal crosstalk and reduced measurement accuracy. The issue of multi-parameter cross-sensitivity remains unresolved and requires further investigation. Promising strategies include differential sensor design, temperature compensation models (e.g., reference grating method), and multi-parameter decoupling algorithms [25]. Innovations in packaging technology offer new approaches to address cross-sensitivity. Differential packaging designs, such as using metal-ceramic packages with high thermal conductivity for temperature sensing and flexible substrate packages for strain sensing, can achieve selective response to specific physical quantities, reducing cross-sensitivity [178,179,180].

Data Overload and Intelligent Analysis: Continuous monitoring of multiple parameters across the body at high sampling rates generates massive volumes of data [3,7]. There is a growing need to develop more efficient and intelligent feature extraction and pattern recognition algorithms—such as those based on deep learning—to uncover deeper insights (e.g., technical diagnostics, injury prediction) beyond basic parameter statistics [163]. To address the massive data problem generated by distributed systems like $\mathsf{\Psi }$-OTDR, compressed sensing algorithms can effectively reduce the amount of data acquisition, enabling simultaneous acquisition and processing, thus solving data storage and transmission challenges in long-distance monitoring [181].

System Cost and Complexity: High-performance interrogation systems, especially those for FBG and distributed sensing, remain costly and lack portability. Operation and maintenance often require specialized personnel, limiting their adoption in grassroots sports teams and general fitness applications [4]. Strategies to reduce cost include simplifying system architecture, developing application-specific integrated photonic chips, and adopting modular designs. Recent research has made substantial progress in reducing costs. For instance, Gallium Nitride (GaN)-based photonic integration chip technology integrates optical emitters and detectors on a single chip, replacing bulky external light sources and detectors, significantly reducing system size and cost [182]. This monolithic integration design not only facilitates mass production and process compatibility but also extends the system’s curvature measurement range to 0~8.43 m⁻¹, meeting the demands of human motion monitoring [183]. Furthermore, combining POF with LED light sources, instead of traditional laser sources and silica fiber, further reduces system costs [184].

Wearability and Comfort: Long-term wearing comfort, moisture permeability, sweat management, and skin compatibility of materials still require improvement. For example, some encapsulation materials such as PDMS may cause heat buildup or allergic reactions [11,118]. Miniaturization and weight reduction in sensors represent critical directions for future development [4]. Micro/nano optical fiber technology has significantly advanced sensor miniaturization by creating optical waveguides with diameters at the micron or even nanoscale, enabling the miniaturization and integration of optical devices [185,186]. Innovations in flexible encapsulation have also improved wearing comfort. Using flexible, transparent organic elastomer films to encapsulate micro/nano fiber couplers maintains sensitivity, expands the sensing range, and enhances both device resistance to interference and wearer comfort [187]. Studies show that the combination of all-glass optical fibers and flexible substrate encapsulation provides good skin conformity and protects against electromagnetic interference, making it suitable for real-time monitoring of physiological parameters like pulse and heartbeat [188].

Furthermore, system integration level and energy consumption are also significant challenges in practical applications. Traditional optical fiber sensing systems often require multiple discrete components, increasing system complexity and power consumption. Monolithic integration technology, which combines light sources, detectors, and sensing units on a single chip, can significantly reduce system power consumption and size. Recent research shows that multi-quantum well structures based on GaN platforms possess dual functions of light emission and reception, offering the potential for building chip-level integrated devices and substantially reducing system energy consumption and volume [182]. Regarding energy management, using optical fiber remote energy transmission technology to input optical energy externally and perform local photoelectric conversion can achieve localized power supply, addressing the energy needs of wearable devices [189,190].

As an innovative technology in sports science, optical fiber sensing demonstrates significant application potential in professional and high-end sports training. However, its commercialization and widespread adoption in amateur and grassroots sports environments face multiple challenges, including cost constraints, technical complexity, data security, and privacy concerns, as detailed in

Table 9. Major obstacles to the commercialization and promotion of optical fiber sensing technology in amateur/grassroots sports environments.

Obstacle Category	Specific Manifestations	References
Cost Constraints	High initial investment cost (light source, modulator, specialized optical fiber, interrogation equipment, etc.) High maintenance and update costs, requiring regular calibration and professional support Limited budgets of amateur sports organizations and users make high costs difficult to bear	[10,191]
Technical Complexity and Integration	High technical requirements for installation and operation; lack of professionals at the grassroots level Difficult integration with existing sports facilities and data processing systems Unfriendly user interface (UI) and experience (UX), inconvenient for amateur users	[189,192]
Data Security and Privacy Concerns	Continuous monitoring involves sensitive biometric data (e.g., heart rate, muscle status, fatigue level) Insufficient data protection measures and potential non-compliance in amateur settings User concerns regarding the collection and use of personal health data	[193,194,195,196]
Infrastructure and Standardization	Potential lack of infrastructure (network connectivity, power supply, etc.) in amateur and grassroots sports venues Lack of industry standards leads to poor interoperability between devices from different manufacturers and difficulties in data sharing	[197,198]
Market Awareness and Acceptance	Limited understanding of the value of OFS technology among amateur athletes, coaches, and administrators Difficulty changing traditional training habits; low trust in data-driven training methods	[199,200,201]
Business Model and Sustainability	Viable business models targeting the amateur sports market are not yet mature Incomplete after-sales service system; difficult for grassroots users to obtain continuous technical support	[201,202]
Product Design and User Experience	The durability and comfort of the equipment do not fully meet the needs of amateur athletes for high-intensity, long-term use	[203,204]

.

5.2. Emerging Research Trends

In recent years, advancements in optical fiber fabrication, micro-nano processing, and artificial intelligence algorithms have profoundly transformed the development of optical fiber sensing technology in sports monitoring. The field is shifting from single-parameter, offline, and general-equipment-based approaches toward multimodal, real-time, personalized, and intelligent solutions. These emerging trends are becoming increasingly distinct and demonstrate strong interdisciplinary characteristics.

• Multi-modal Sensor Fusion: Limitations of current research, such as the inability of single physical parameters to fully reflect complex physiological and biomechanical states, are driving the field toward multi-modal information fusion. The core rationale is to enhance monitoring accuracy and robustness through complementary data. Cutting-edge research is progressively integrating optical fiber sensors—capable of monitoring pressure, strain, and acceleration—with IMUs, EMG sensors, and electrocardiography (ECG) electrodes into a unified wearable platform [6]. Such integrated systems can simultaneously capture an athlete’s external mechanical load (e.g., ground impact forces, joint angles) and internal physiological load (e.g., muscle activation level, heart rate), thereby enabling comprehensive and objective assessment of athletic performance, technique, and fatigue state. For instance, Presti et al. [117] developed a 3D-printed wearable device incorporating optical fiber strain sensors, capable of simultaneously monitoring respiration and heartbeat, demonstrating the feasibility of synchronous multi-parameter physiological acquisition.
• New Materials and Structural Innovation: From a technological evolution perspective, a prominent trend involves the deep integration of novel functional materials and microstructured optical fibers, aimed at improving sensor sensitivity, durability, comfort, and conformability to the environment or human body. Two-dimensional materials such as MXene, known for their excellent electrical conductivity and mechanical properties, are being incorporated into flexible substrates to create highly sensitive strain sensors. This significantly expands their potential for monitoring large-deformation human movements, such as joint flexion and muscle contraction [115]. Concurrently, innovations in optical fiber microstructure design, including photonic crystal fibers and fiber Bragg grating arrays, continue to enhance their unique advantages for distributed strain and temperature field monitoring [164].
• AI Deep Empowerment: The maturation of artificial intelligence (AI) and machine learning (ML) presents transformative opportunities for processing and analyzing the massive, high-dimensional, and nonlinear data generated by optical fiber sensing. Future applications will evolve beyond simple signal filtering and feature extraction toward intelligent diagnosis, prediction, and decision-making, specifically addressing bottlenecks in converting data into actionable knowledge. For example, Sun et al. [205] employed unsupervised learning to effectively enhance the signal-to-noise ratio in phase-sensitive optical time-domain reflectometry ($\mathsf{\Phi }$-OTDR) systems, improving performance in distributed vibration sensing. More advanced work utilizes deep learning algorithms for movement pattern recognition and sports injury risk prediction. Lv et al. [206] designed an immersive basketball tactical training system based on digital twin and federated learning, providing a paradigm for AI-driven advanced tactical analysis.
• Miniaturization and Integration: Advances in micro-electromechanical systems (MEMS) and flexible electronics are expanding the research boundaries of optical fiber sensors, facilitating a transition from “theoretically feasible” to “practically deployable” in wearable devices. Research focuses on the high-level integration of sensing units, demodulation modules, and wireless transmission units to develop ultra-thin, flexible, and even skin-adhered monitoring devices that minimize interference with athlete movement [6,118]. A review by Erik et al. [118] extensively discusses the current state of medical wearable optical sensors, with many conclusions, such as those regarding biocompatibility and wearing comfort, being equally applicable to sports science.
• Energy Self-Supply: Current research progress indicates that achieving long-term, unobtrusive, and maintenance-free monitoring—crucial for comprehensive sports data capture—necessitates addressing energy supply challenges. Although the direct application of energy harvesting in optical fiber-based sports monitoring is still nascent, advancements in technologies such as triboelectric nanogenerators (TENGs) and photovoltaic cells are laying the groundwork for future integrated applications [207,208,209].
• Closed-loop Biofeedback: Driven by scenarios demanding personalized training and precise rehabilitation, frontier research is increasingly oriented toward constructing closed-loop systems embodying “perception–analysis–intervention.” The core concept involves using optical fiber sensors for real-time data acquisition. After analysis by AI models, immediate feedback is delivered to athletes or coaches via visual, auditory, or haptic interfaces, such as augmented reality (AR)/virtual reality (VR) devices or haptic feedback devices, to correct technical movements or adjust training load [206].
• AIoT and Digital Twin: Recent breakthroughs in 5G/6G communication, edge computing, and cloud computing have propelled the evolution of optical fiber sensing monitoring systems from isolated, localized data acquisition toward an integrated intelligent ecosystem characterized by cloud-edge-device synergy and the Internet of Everything. Under the impetus of smart sports scenarios, cutting-edge research is progressively leaning toward an integrated “device-edge-cloud” architecture. The core logic is: optical fiber sensing nodes act as perceptual endpoints at the device level, capturing ultra-high-precision data from athletes and the environment in real time. Data are wirelessly transmitted to edge computing devices (e.g., courtside gateways, smart terminals) for preliminary processing and real-time feedback. Ultimately, massive datasets converge on the cloud platform, integrating deeply with AI and digital twin technologies for in-depth mining, model training, and global decision-making. This architecture establishes optical fiber sensing systems as critical data sources within the broader AIoT [70]. Existing research suggests its evolution can be summarized along two main directions. First, global situational awareness based on optical fiber sensor networks, enabling synchronous monitoring of multiple athletes, equipment, and even venues to construct a macro-view of training and competition for tactical analysis, team load management, and scientific talent selection [163]. Second, deep integration with digital twin technology. The digital twin, a high-fidelity virtual model residing in the cloud, is dynamically updated and simulated using continuous real-time data streams from IoT sensors like optical fibers. This enables the optimization and verification of athlete state prediction, tactical simulation, and injury prevention strategies within the virtual space, ultimately feeding the optimal strategies back to athletes in the real world, forming a “perception–cognition–decision–execution” closed loop [206,210]. Future research in this area will further focus on seamless fusion of multi-source heterogeneous data, low-latency high-concurrency communication protocols, and the construction of integrated cloud-edge simulation platforms. Key breakthroughs will hinge on addressing challenges such as data security, system interoperability, and model fidelity.

In summary, future research on optical fiber sensing technology in sports monitoring will increasingly focus on system practicality, reliability, and accessibility. Core breakthroughs will necessitate solving common technical challenges like multi-modal data standardization, long-term sensor stability, system power consumption, and cost. As the aforementioned frontier trends converge and develop, optical fiber sensing technology is poised to become a foundational technology for constructing the next generation of intelligent sports ecosystems.

5.3. Interdisciplinary Integration Prospects

The further application of fiber optic sensing technology in sports science is highly dependent on multidisciplinary integration. The combination of materials science and biomedical engineering is expected to give rise to a new generation of smart sports textiles with enhanced biocompatibility, moisture permeability, and even therapeutic functions [11]. Researchers are exploring the seamless integration of fiber optic sensors into functional fabrics, making physiological and motion monitoring systems as comfortable and natural as ordinary sportswear [17]. Such smart garments can continuously record physiological parameters including ECG, respiratory rate, and muscle tension, while accurately capturing technical movement details, thereby providing comprehensive feedback for scientific training [169,211].

Empowered by IoT and cloud computing technologies, distributed fiber optic monitoring systems can be upgraded into the “nervous system” of smart sports venues [5,24,171]. By deploying fiber sensing networks in critical structural components (e.g., floors, grandstands), sports equipment, and environmental systems (air conditioning, lighting), and combining them with 5G transmission and edge computing, these systems enable real-time monitoring of venue conditions, optimized equipment usage, energy management, and safety early-warning [24]. The temperature monitoring system implemented at the Beijing Winter Olympics curling venue has preliminarily demonstrated this concept, with future applications expected to expand to more parameters (e.g., structural health, crowd density, air quality) and broader scales.

In summary, the broader deployment of fiber optic sensing technology in sports monitoring must address critical challenges related to sensitivity, cross-talk, wearability, cost, and data processing. Future advancements rely on the deep integration of artificial intelligence and big data analytics, together with innovations in new materials (such as flexible-stretchable, self-healing, and biodegradable substances), micro-nano integration, multi-modal sensor fusion, and self-powering technology. By establishing closed-loop perception–analysis–feedback systems and smart IoT platforms, this technology can bridge individualized training guidance and holistic smart venue management. Ultimately, through convergence among materials science, biomedical engineering, and information technology, it will help build an integrated ecosystem for smart sports.

Table 10. Correspondence Between Major Technical Challenges and Future Integrated Solutions.

Technical Challenge	Future Development Trend	Integrated Solution	Practical Guidance	References
Environmental Interference and Signal Noise	AI-Enhanced Signal Processing	Deep Learning-Based Noise Suppression Algorithms	Utilize Generative Adversarial Networks (GANs) to simulate various environmental noises and enhance recognition robustness	[212,213,214]
System Integration and Compatibility	IoT Platform Integration	6G-Enabled Transparent Optical Sensing Networks	Establish industry-standard interface protocols to support multi-vendor device access	[215]
Data Processing Complexity	Edge Computing Architecture	DPS Chip-Based Embedded Processing	Perform preliminary data preprocessing at the sensor end to reduce the central system load	[216,217]
Sensor Invasiveness	New Materials and Miniaturization	Silica-Based Micro-Optical Devices	Develop flexible, wearable optical fiber sensors to minimize movement obstruction	[218,219,220]
Energy Management Issues	Self-Powering Technology	Passive Sensing and Energy Harvesting	Utilize the optical fiber itself as an energy harvesting medium to achieve self-sustained monitoring	[209,221]

summarizes the major technical challenges faced by optical fiber sensing technology in sports monitoring and their corresponding future innovative solutions.

Looking ahead, as technical bottlenecks are gradually overcome and interdisciplinary collaboration continues to deepen, fiber optic sensing technology will serve scientific training, injury prevention, public fitness, and smart facility management with greater accuracy, efficiency, and ease. It will further promote the transition of public health initiatives toward intelligent and personalized approaches, fostering deeper synergy and coordinated development between technology and the sports industry.

6. Conclusions

Owing to its unique advantages such as high precision, immunity to electromagnetic interference, strong environmental adaptability, and wearable integration, fiber optic sensing technology is profoundly transforming both the scientific foundations and practical methodologies of sports monitoring. From the non-invasive acquisition of physiological parameters to millimeter-level kinematic analysis of movement patterns, and from single-point measurements to full-body distributed monitoring—even when moving from laboratory settings to complex, real-world training environments—innovative outcomes have consistently demonstrated the technology’s value in enhancing training precision, optimizing athletic performance, preventing injuries, and supporting rehabilitation. Current research confirms that fiber optic sensors can effectively capture a wide range of critical signals, from macroscopic body motions to subtle physiological changes, thereby providing robust data support for technical refinement, precise quantification of training load, fatigue management, and early warning of injury risks in athletes.

These efforts are essential to construct a transparent optical sensing network based on 6G communication, which will enable real-time monitoring with low latency and high precision. Concurrently, it is crucial to strengthen interdisciplinary collaboration among materials scientists, optical engineers, biomedical experts, sports scientists, and computer scientists, and to establish unified technical standards, systematic performance evaluation frameworks, and robust data-sharing platforms.

Driven by rapid advances and deepening integration in materials science—including flexible polymers, elastomers, and nanomaterials—artificial intelligence, such as deep learning and pattern recognition, and Internet of Things technologies like 5G, edge computing, and digital twins, fiber optic sensing systems are rapidly evolving toward multifunctional integration, intelligent analysis, imperceptible wearability, and energy autonomy. Future research must prioritize overcoming key technical bottlenecks, including enhancing wearer comfort, significantly reducing system costs, optimizing intelligent algorithms for complex signal processing, and developing adaptive learning capabilities. These advancements are essential for establishing a transparent optical sensing network utilizing 6G communication to achieve real-time monitoring with low latency and high precision. It is also essential to strengthen interdisciplinary collaboration among materials scientists, optical engineers, biomedical specialists, sports scientists, and computer scientists, while establishing unified technical standards, performance evaluation frameworks, and data-sharing platforms.

Moving forward, addressing existing technical bottlenecks will necessitate a systematic merger of emerging advances with practical application requirements. To enhance comfort and durability, novel nanocomposite materials such as graphene–polymer hybrid fibers—along with bio-inspired structural designs—deliver improved skin conformity and mechanical resilience, in addition to enabling extended device lifetime through self-healing functionality. These attributes are essential for long-term, high-intensity monitoring in competitive athletics. On the cost front, AI-assisted placement strategies can minimize the number of physical sensors by employing digital twin techniques to reconstruct full-body kinematic models, thereby reducing system overhead. For real-time data processing, the incorporation of lightweight neural networks into edge-computing frameworks offers a viable path for handling large-scale data streams efficiently. Complementary efforts should focus on building standardized data platforms and secure architectures; transfer learning, for example, may help account for physiological variability across individuals, thereby improving both generalizability and interpretability of fatigue assessment models. Energy autonomy will depend on co-optimizing energy harvesting mechanisms—such as fiber-based biomechanical converters—with low-power optical sensor design. Ultimately, successful technology integration will rely on interdisciplinary collaboration and standardized ecosystems, supported by open-source algorithms, shared datasets, and validated evaluation protocols to accelerate adoption in real-world scenarios.

Looking ahead, with anticipated improvements in the stability of novel flexible optoelectronic materials, the maturation of lightweight edge intelligence algorithms, and breakthroughs in high-performance self-sustaining energy systems, it is reasonable to anticipate that fiber optic sensing technology will emerge as a core component of next-generation smart sports equipment and health monitoring systems. This technology will not only support elite sports through refined technical optimization, intelligent enhancement of equipment performance, and scientific management of athlete health, but will also significantly advance the scientific, personalized, and intelligent development of public fitness.

From wearable devices to smart venues, and from elite athletes to rehabilitation patients and general fitness enthusiasts, fiber optic sensing is expected to enable a comprehensive ecosystem for lifelong sports and health monitoring. It will propel both competitive sports and public fitness into a new era characterized by precision and intelligence, ultimately realizing the vision of technology-driven advancement in sports and health promotion for all.