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Wearable Sensors in Biomechanics and Human Motion

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Wearables".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 10223

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Special Issue Editors

Dr. Philippe Dixon

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Guest Editor

Department of Kinesiology and Physical Education, McGill University, Montreal, QC H3A 0G4, Canada
Interests: biomechanics; motion capture; gait analysis; machine learning; wearable sensors; running

Dr. Clint Hansen

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Guest Editor

Department of Neurology, University Hospital Schleswig-Holstein Campus Kiel and Kiel University, 24105 Kiel, Germany
Interests: biomechanics; motor control
Special Issues, Collections and Topics in MDPI journals

Dr. Jill Emmerzaal

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Guest Editor

Department of Kinesiology and Physical Education, McGill University, Montreal, QC H3A 0G4, Canada
Interests: rehabilitation; musculoskeletal biomechanics

Special Issue Information

Dear Colleagues,

This Special Issue aims to highlight recent advances in the use of wearable sensors in biomechanics and human motion research across diverse settings and populations. Wearable technologies, such as inertial measurement units (IMUs), electromyography (EMG), and pressure insoles, are transforming how we assess movement biomechanics in real-world environments. We invite contributions that explore methodological innovations, data processing algorithms, and applications of wearable systems in clinical, sports, occupational, or daily-life contexts.

Topics of interest include, but are not limited to, the following: wearable sensor-based gait and posture analysis, motion tracking, joint kinematic and joint kinetic estimation, activity recognition, rehabilitation monitoring, injury prevention, and integration with machine learning or digital health platforms. Both original research and reviews are welcome.

The goal of this issue is to provide a multidisciplinary forum for researchers, clinicians, and engineers to share cutting-edge research developments that advance the understanding and application of wearable sensor technologies in biomechanics and human movement science.

Dr. Philippe Dixon
Dr. Clint Hansen
Dr. Jill Emmerzaal
Guest Editors

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Keywords

biomechanics
wearable sensors
human motion analysis
activity recognition
machine learning
digital health
sport science
gait and posture
inertial measurement units

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Published Papers (8 papers)

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12 pages, 1798 KB

Open AccessArticle

Quantifying Upper Limb Movement During Naturalistic Driving: A Clinically Informed Ecological Approach

by Carly R. Rankin, Dwayne L. Mann, Shamsi Shekari Soleimanloo, Kalina R. Rossa, Karen A. Sullivan, Paul M. Salmon, Cassandra L. Pattinson and Simon S. Smith

Sensors 2026, 26(10), 3121; https://doi.org/10.3390/s26103121 - 15 May 2026

Viewed by 205

Abstract

Limb movement is an important component of control during safety-critical tasks such as driving. Restricted movement, such as limitations associated with an injury or surgery to the upper limb, may impact driving safety. However, the degree of upper limb movement required for driving is not well described outside of traditional laboratory settings. There is a need for new affordable, accessible, reliable and accurate measures of normative limb movement to guide decisions about driving capacity. This feasibility study applied a volume estimation approach to wrist-worn triaxial accelerometry data to quantify upper limb movement during naturalistic driving in a young adult population. A sample of 89 participants wore accelerometers while engaging in daily driving activity over a two-week period. Results demonstrated a distribution of movement volumes, consistent with variation in individual driving behaviour. This volume estimation approach has strong potential for further development as both a research tool and clinical assessment method, particularly in rehabilitation and return-to-driving assessments following upper limb injury or surgery. Full article

(This article belongs to the Special Issue Wearable Sensors in Biomechanics and Human Motion)

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21 pages, 2917 KB

Open AccessArticle

Validity of a Commercially Available Inertial Measurement Unit for Artificial Intelligence-Based Trick Detection and Kinematic Performance Assessment in Skateboarding

by Birte Scholz, Niklas Noth, Maren Witt and Olaf Ueberschär

Sensors 2026, 26(8), 2537; https://doi.org/10.3390/s26082537 - 20 Apr 2026

Viewed by 520

Abstract

Inertial measurement units (IMUs) present promising avenues for performance diagnostics in skateboarding, yet systematic validation of their accuracy and applicability remains limited. This study validates the commercially available Spinnax Freak IMU system in the context of skateboarding, with a focus on selected trick detection and classification, distance measurement, maximal horizontal speed, maximal vertical height of the skateboard and airtime during a jump trick. A total of 23 skateboarders (4 females, 19 males; 27.4 ± 10.9 years) participated in this study. Validation methods included comparisons with established reference systems such as laser ranging for maximal horizontal speed (LAVEG), 2D video analysis for maximal vertical height of the skateboard (Kinovea), light barrier measurements for airtime detection (OptoJump Next), and a fixed metric reference (10 m) for rolling distance measurements. The evaluation was supported by statistical analyses including mean absolute error (MAE), root mean-square error (RMSE), mean absolute percentage error (MAPE), t-tests, Bland–Altman plots, linear regression, and ICC(3,1). The Spinnax Freak system demonstrated high validity in detecting trick events and in providing distance measurements that were statistically equivalent to the reference. Trick classification, maximal horizontal speed, maximal vertical height of the skateboard and airtime showed substantial errors, indicating that these outputs are not reliable for biomechanical interpretation at this point. These findings highlight both the potential and the current constraints of single-sensor setups for field-based motion capture in skateboarding. Future developments should prioritize algorithmic refinement, improved temporal resolution, and optimized event classification to enhance measurement accuracy and expand applicability in biomechanical analysis and automated training documentation in skateboarding. Full article

(This article belongs to the Special Issue Wearable Sensors in Biomechanics and Human Motion)

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16 pages, 704 KB

Open AccessArticle

Biomechanical Analysis of the Breaststroke Kick in Young Swimmers Using Wearable Inertial Sensors: An Exploratory Pilot Study

by Denisa-Iulia Brus, Răzvan Sandu Enoiu and Dorin-Ioan Cătană

Sensors 2026, 26(5), 1691; https://doi.org/10.3390/s26051691 - 7 Mar 2026

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Abstract

Breaststroke performance is highly dependent on lower-limb biomechanics and the coordination of movement during the kick cycle. Recent advances in wearable inertial sensor technology enable objective analysis of human motion in real training environments. This study presents an exploratory pilot investigation aimed at evaluating the feasibility of using wearable inertial sensors for biomechanical analysis of the breaststroke kick in young swimmers. Five male children (aged 8–10 years) with basic breaststroke proficiency participated in a single-group pre–post exploratory study conducted over a three-month period. Lower-limb motion was monitored using wearable inertial measurement units attached bilaterally to the shanks and feet, allowing real-time kinematic feedback and data recording during training sessions. The intervention consisted of five structured training sessions integrating drill-based breaststroke kick exercises with sensor-assisted feedback. Outcome measures included time-based swimming performance tests (40 m breaststroke kick with kickboard and 40 m breaststroke without kickboard) and qualitative biomechanical evaluations of the passive and active phases of the breaststroke kick. Additionally, selected IMU-derived kinematic variables (peak ankle dorsiflexion and external foot rotation angles) were analyzed to provide quantitative biomechanical insight. Following the intervention, improvements were observed across all outcome measures, including reduced swimming times and increased technique scores assigned by two independent evaluators. These findings support the feasibility of integrating wearable IMUs for technique monitoring and simple kinematic quantification of breaststroke kick mechanics in young swimmers; larger controlled studies are required to assess efficacy. Full article

(This article belongs to the Special Issue Wearable Sensors in Biomechanics and Human Motion)

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26 pages, 16690 KB

Open AccessArticle

Effects of Acute Altitude, Speed and Surface on Biomechanical Loading in Distance Running

by Olaf Ueberschär, Marlene Riedl, Daniel Fleckenstein and Roberto Falz

Sensors 2026, 26(1), 276; https://doi.org/10.3390/s26010276 - 1 Jan 2026

Cited by 1 | Viewed by 1129

Abstract

Altitude training camps are a popular measure to enhance endurance performance at sea level. This study elucidates the effects of acute altitude-induced hypoxia, running speed and surface on cadence, peak tibial acceleration (PTA), gait asymmetry and residual shock in distance running. Ten healthy, [...] Read more.

{\dot{V}}_{O_{2, p e a k}}

of 54.9 ± 5.9 mL min⁻¹ kg⁻¹) participated in this study. They ran 1500 m bouts of at 50, 1000 and 2300 m above mean sea level on paved roads and natural trails at three different speeds. Those speeds were chosen to represent the most common training zones and were defined as

v_{1} = 90 % \cdot v_{VT 1}

v_{2} = \frac{1}{2} (v_{VT 1} + v_{VT 2})

and

v_{3} = 100 % \cdot v_{VT 2}

, with

v_{VT 1}

and

v_{VT 2}

denoting the speeds at the ventilatory thresholds 1 and 2. Based on the experimental results, cadence increased by +2.2 spm per +1 km h⁻¹ (p < 0.001) and fell by −1.1. spm per +1000 m of elevation (p < 0.001), whereas surface did not show any significant effect. Likewise, PTA was not affected by surface, but grew by 0.9 g per +1 km h⁻¹ (p < 0.001), and decreased by −0.6 g per +1000 m in elevation, with significant effects particularly at speeds beyond v_VT1 (p < 0.049). Absolute lateral asymmetry was not altered by elevation, surface or running speed. Mean shock attenuation increased with running speed by +2.5 percentage points per +1 km h⁻¹ (p < 0.001) but was independent of elevation and surface. In essence, running speed seems to be the predominant factor defining biomechanical loading, even under acute hypoxia and for varying surface conditions. Full article

(This article belongs to the Special Issue Wearable Sensors in Biomechanics and Human Motion)

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16 pages, 2273 KB

Open AccessArticle

Joint Function and Movement Variability During Daily Living Activities Performed Throughout the Home Setting: A Digital Twin Modeling Study

by Zhou Fang, Mohammad Yavari, Yiqun Chen, Davood Shojaei, Peter Vee Sin Lee, Abbas Rajabifard and David Ackland

Sensors 2025, 25(24), 7409; https://doi.org/10.3390/s25247409 - 5 Dec 2025

Viewed by 1042

Abstract

Human mobility is commonly assessed in the laboratory environment, but accurate and robust joint motion measurement and task classification in the home setting are rarely undertaken. This study aimed to develop a digital twin model of a home to measure, visualize, and classify joint motion during activities of daily living. A fully furnished single-bedroom apartment was digitally reconstructed using 3D photogrammetry. Ten healthy adults performed 19 activities of daily living over a 2 h period throughout the apartment. Each participant’s upper and lower limb joint motion was measured using inertial measurement units, and body spatial location was measured using an ultra-wide band sensor, registered to the digital home model. Supervised machine learning classified tasks with a mean 82.3% accuracy. Hair combing involved the highest range of shoulder elevation (124.2 ± 21.2°), while sit-to-stand exhibited both the largest hip flexion (75.7 ± 10.3°) and knee flexion (91.8 ± 8.6°). Joint motion varied from room to room, even for a given task. For example, subjects walked fastest in the living room (1.0 ± 0.2 m/s) and slowest in the bathroom (0.78 ± 0.10 m/s), while the mean maximum ankle dorsiflexion in the living room was significantly higher than that in the bathroom (mean difference: 4.9°, p = 0.002, Cohen’s d = 1.25). This study highlights the dependency of both upper and lower limb joint motion during activities of daily living on the internal home environment. The digital twin modeling framework reported may be useful in planning home-based rehabilitation, remote monitoring, and for interior design and ergonomics. Full article

(This article belongs to the Special Issue Wearable Sensors in Biomechanics and Human Motion)

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14 pages, 1964 KB

Open AccessArticle

Bridging the Methodological Gap Between Inertial Sensors and Optical Motion Capture: Deep Learning as the Path to Accurate Joint Kinematic Modelling Using Inertial Sensors

by Vaibhav R. Shah and Philippe C. Dixon

Sensors 2025, 25(18), 5728; https://doi.org/10.3390/s25185728 - 14 Sep 2025

Viewed by 1706

Abstract

As advancements in inertial measurement units (IMUs) for motion analysis progress, the inability to directly apply decades of research-based optical motion capture (OMC) methodologies presents a significant challenge. This study aims to bridge this gap by proposing an innovative deep learning approach to predict marker positions from IMU data, allowing traditional OMC-based calculations to estimate joint kinematics. Eighteen participants walked on a treadmill with seven IMUs and retroreflective markers. Trials were divided into normalized gait cycles (101 frames), and an autoencoder network with a custom Biomech loss function was used to predict 16 marker positions from IMU data. The model was validated using the leave-one-subject-out method and assessed using root mean squared error (RMSE). Joint angles in the sagittal plane were calculated using OMC methods, and RMSE was computed with and without alignment using dynamic time warping (DTW). The models were also tested on external datasets. Marker predictions achieved RMSE values of 2–4 cm, enabling joint angle predictions with 4–7° RMSE without alignment and 2–4° RMSE after DTW for sagittal plane joint angles (ankle, knee, hip). Validation using separate and open-source datasets confirmed the model’s generalizability, with similar RMSE values across datasets (4–7° RMSE without DTW and 2–4° with DTW). This study demonstrates the feasibility of applying conventional biomechanical models to IMUs, enabling accurate movement analysis and visualization outside controlled environments. This approach to predicting marker positions helps to bridge the gap between IMUs and OMC systems, enabling decades of research-based biomechanical methodologies to be applied to IMU data. Full article

(This article belongs to the Special Issue Wearable Sensors in Biomechanics and Human Motion)

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20 pages, 927 KB

Open AccessSystematic Review

Towards Continuous Swim Leg Analytics in Olympic Triathlon: A Systematic Review of Sensor-Based Assessment Approaches in Open-Water Sports Contexts

by Jannik Seelhöfer, Jürgen Wick and Maren Witt

Sensors 2026, 26(7), 2151; https://doi.org/10.3390/s26072151 - 31 Mar 2026

Viewed by 449

Abstract

Global Navigation Satellite Systems (GNSS) offer precise movement analyses based on distance and speed in open-water sports. Despite the influence of swimming in triathlon, its performance analysis remains underdeveloped due to methodological limitations in capturing continuous data in aquatic environments. This review aimed to: (1) systematically analyse and compare the sensor-based technologies applied to open-water movement analysis, and (2) propose a framework for continuous GNSS-based assessment of triathlon swim performance. A systematic search was conducted prior to the 14 August 2025 across four databases (Web of Science, SPORTDiscus, PubMed, and SPONET). Studies were eligible if they analysed open-water sports using GNSS-based technologies for continuous movement or performance analysis. Studies limited to indoor swimming, inertial sensors, or non-sporting applications were excluded. Methodological quality and potential sources of bias were evaluated using a custom scheme based on GNSS reporting guidelines, as methodological heterogeneity precluded the application of standardised tools. Following screening and eligibility assessment, articles were analysed qualitatively. In total, 20 articles were included and focused on surfing, sailing, water skiing, windsurfing, kitesurfing, stand-up paddling (SUP), and swimming. Most studies focused on board- and sail-based sports, employed sampling frequencies between 1 and 15 Hz, and demonstrated substantial variability in device specifications and reporting quality. Different sensors and GNSS-derived variables were central to discipline-specific performance analysis. The strength of evidence is limited by the heterogeneous methodologies, and variable reporting quality. The proposed framework provides methodological guidance for implementing high-resolution GNSS-based monitoring in triathlon swimming to improve pacing analysis and race strategy development. Full article

(This article belongs to the Special Issue Wearable Sensors in Biomechanics and Human Motion)

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28 pages, 1658 KB

Open AccessSystematic Review

Wearable Technology and Machine Learning for Prediction of Performance-Based and Patient-Reported Outcome Measures: A Systematic Review

by Eloise Milbourn, Jiaqi Lai, Dale L. Robinson, David C. Ackland and Peter Vee Sin Lee

Sensors 2026, 26(4), 1218; https://doi.org/10.3390/s26041218 - 13 Feb 2026

Cited by 1 | Viewed by 1930

Abstract

Machine learning models informed by patient-generated wearable data can be used to predict patient-reported and performance-based outcome measures. This approach offers a promising alternative to traditional outcome monitoring, which is commonly limited by recall bias, discrete sampling, and healthcare resource constraints. The aims of this systematic review were to identify wearable-derived features strongly associated with performance-based and patient-reported outcome measures, to compare the predictive performance across machine learning approaches, and to consolidate methodological limitations and provide suggestions for future work. Following a systematic search of four databases (PubMed, Scopus, Embase, and IEEE Xplore), 18 eligible studies were identified, published between 2017 and 2024, spanning patients across eight disease categories. Most studies used wrist-worn devices measuring accelerometry, sometimes combined with heart rate, respiratory, or sleep metrics. Random forest and support vector machine models were the most common, while hidden Markov temporal models showed improved performance with access to longitudinal data. Predictive performance ranged from poor to excellent (AUC 0.56–0.92), and non-linear models generally outperformed linear models. Despite promising early results, most studies report similar limitations of small sample sizes, limited external validation, and difficulty achieving acceptable accuracy beyond binary predictions. Overall, these studies highlight the potential of wearable-informed machine learning for continuous and objective outcome assessment, but the consensus calls for further work to apply larger, more diverse longitudinal datasets and interpretable temporal modelling approaches to bridge the gap between the current proof-of-concept state and clinical translation. Full article

(This article belongs to the Special Issue Wearable Sensors in Biomechanics and Human Motion)

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