Design of a Smart Foot–Ankle Brace for Tele-Rehabilitation and Foot Drop Monitoring

Abstract

Foot drop, a form of paralysis affecting ankle and foot control, impairs walking and increases the risk of falls. Effective rehabilitation requires monitoring gait to guide personalized interventions. This study presents a proof-of-concept smart foot–ankle brace integrating low-cost sensors, including gyroscopes, accelerometers, and a Fiber Bragg Grating (FBG) array, with an Arduino-based processing platform. The system captures, in real time, the key locomotion parameters, namely, angular rotation, acceleration, and sole deformation. Experiments using a 3D-printed insole demonstrated that the device detects foot-drop-related gait deviations, with toe acceleration approximately twice that of normal walking. It also precisely detects foot deformation through FBG sensing. These results demonstrate the feasibility of the proposed system for monitoring gait abnormalities. Unlike commercial gait analysis devices, this work focuses on proof-of-concept development, providing a foundation for future improvements, including wireless integration, AI-based gait classification, and mobile application support for home-based or tele-rehabilitation applications.

1. Introduction

Stroke and other neurological disorders are among the most common health issues worldwide, particularly in the elderly population. Each year, over 16 million people experience some form of stroke [1], often resulting in some degree of paralysis, which can be temporary or permanent. Paralysis can cause partial or complete loss of motor control, leading to abnormal gait patterns such as foot drop, loss of balance, and compensatory movements that slow the swing phase of gait [2]. Furthermore, conditions such as diabetic peripheral neuropathy (DPN), idiopathic normal pressure hydrocephalus (iNPH) [3,4], and Parkinson [5] can impair balance, thus, increasing the risk of falls and associated injuries. Falls are a significant concern for elderly individuals, often occurring during weight-bearing activities. However, with early detection and monitoring, many fall incidents are preventable. Precise gait assessment can improve diagnosis of Parkinson and support evaluation for surgical candidacy in iNPH by quantifying gait impairment [5].

Traditional diagnostic methods, including clinical observation and standard gait analysis, often lack the precision required for personalized rehabilitation planning [6,7]. Over the past two decades, a variety of ankle–foot wearable devices, particularly insoles, have been developed aimed to monitor and mitigate impaired locomotion and enhance gait [8,9]. However, most existing wearable devices target general gait issues and often do not address the individual-specific patterns of foot drop, which vary from patient to patient [10].

Despite significant advancements in wearable gait monitoring technologies, several limitations persist in current smart insole systems.

Limited Measurement Parameters: Many commercially available smart insoles primarily measure plantar pressure distribution, often neglecting other critical gait parameters such as angular rotation, acceleration, and joint misalignment [11]. Furthermore, there are some concerns regarding the accuracy of the measurement [12,13]. These issues lead to an incomplete characterization of a patient’s gait pattern, particularly in conditions like foot drop, where abnormal dorsiflexion, inversion/eversion, and slight changes in foot dynamics are important health indicators.

Lack of Real-Time Monitoring: Several systems do not support continuous, real-time data transmission, limiting their utility in immediate clinical decision-making or home-based monitoring [14]. Without live feedback, clinicians cannot adjust interventions as needed. In addition, patients cannot receive timely guidance during rehabilitation exercises.

Inadequate Accuracy: Existing smart insoles often use localized conventional strain sensors that do not provide sufficient accuracy for biomedical application [15].

Recent advances in sensor technology, wireless communications, and miniaturized electronics have opened new possibilities for developing smart wearable devices capable of precise gait monitoring. Smart insoles, embedded inside shoes, collect detailed data on movement, such as plantar pressure and acceleration without compromising mobility [16,17,18,19]. Most recently, sequential network models have been developed to enhance the capability of smart insoles in detecting abnormal gait [20]. These devices provide valuable insights into foot and ankle function during gait, which are critical for weight-bearing and weight-shifting activities.

In addition, most existing smart insole designs rely on discrete strain sensors, such as piezoelectric elements or strain gauges, positioned at specific locations on the sole. However, this configuration requires separate wiring for each sensor, which increases system complexity and reduces overall efficiency.

Unlike conventional strain sensors, Fiber Bragg Grating (FBG) sensors enable simultaneous, real-time measurement of strain at multiple points along a single fiber, using wavelength-encoded signals and only one external connection. This multiplexing capability reduces wiring complexity while maintaining high accuracy and sensitivity.

Recently, Fiber Bragg Grating (FBG) sensors have also been successfully applied in biomechanics to detect sole deformation and monitor joint movement [21,22,23,24]. Therefore, a combination of microcontrollers, micro sensors, and FBG sensors leads to a low-cost and accessible yet accurate system for real-time gait analysis and personalized rehabilitation.

This study presents a proof-of-concept smart foot–ankle brace integrating accelerometers, gyroscopes, and FBG sensors with an Arduino-based processing platform. The device measures angular rotation, translational displacement, acceleration, joint misalignment, and sole deformation, transmitting data via Bluetooth for real-time monitoring. A 3D-printed insole prototype was developed and evaluated through multiple walking simulations and human testing, including subjects with different foot drop patterns. Preliminary results demonstrated the system’s ability to detect gait deviations associated with foot drop, such as variations in toe acceleration and sole deformation.

The study was performed in a controlled environment for the purpose of verifying our hypothesis on utilizing accelerometers and gyroscopes to detect gait deviations in real time. The results also provided insights on how to analyze these measurements for detecting such deviations. Note that gait behaviors can vary significantly between different individuals. Thus, the monitoring system will be extended in our future work that will involve a learning phase to address these variations in different individuals and to function in uncontrolled settings.

The motivation for this study is threefold: (i) to demonstrate the feasibility of a simple, low-cost wearable system for detecting foot drop, (ii) to integrate multiple sensor modalities, including FBG technology, for comprehensive gait assessment, and (iii) to provide a platform that can be further developed for home-based rehabilitation and tele-monitoring, bridging the gap between research prototypes and practical clinical tools. These results suggest that the smart foot–ankle brace has the potential to enhance diagnosis, facilitate personalized rehabilitation strategies, and support real-time monitoring in paralyzed or at-risk patients.

2. Materials and Methods

This study employs a primarily experimental methodology to investigate the relationships between sensor measurements and physical activities during both normal and abnormal gait, with a specific focus on foot drop. The methodology begins with a concise overview of fundamental gait terminologies and definitions, followed by a detailed description of the locomotion parameters considered critical for analysis. It then outlines the sensor technologies and measurement systems used to detect, quantify, and monitor abnormal walking patterns, highlighting their integration within the smart foot–ankle brace. This structured approach ensures that both the design of the experimental protocol and the interpretation of sensor data are clearly aligned with the study’s objectives of accurate gait assessment and foot drop detection.

2.1. Fundamental Terminologies

Plantar flexion: Decreasing the angle between the sole of the foot and the back of the leg, pointing the toes downward. This action is commonly associated with motions like standing on tiptoes, pressing the gas pedal in a car, or pushing off the ground when jumping.

Dorsiflexion: Decreasing the angle between the top of the foot and the shin, pulling the toes upward toward the leg. This action is important for activities such as walking, running, and climbing, as it allows the foot to clear the ground during the swing phase of gait.

Inversion: Turning the sole of the foot inward toward the body’s midline. This action results in the sole facing the opposite foot. Inversion plays a critical role in stabilizing the foot during walking, running, and balancing on uneven surfaces.

Eversion: Turning the sole of the foot outward, away from the body’s midline. This action causes the sole to face away from the opposite foot. Eversion is crucial for balance and stability, especially when navigating uneven surfaces or shifting weight laterally.

2.2. Simulation of a Gait Cycle

A normal gait cycle is defined as the interval from the heel strike of one leg to the subsequent heel strike of the same leg and consists of two main phases: stance and swing. The stance phase can be further divided into sub-phases: initial contact, loading response (or flat foot), mid-stance, and terminal stance (heel-off/toe-off). The swing phase includes pre-swing (acceleration), mid-swing, and terminal swing (deceleration) [25].

Figure 1 shows a typical gait cycle for a normal locomotion by a healthy person. It is worth noting that each phase is developed by an action of specific muscles in a rhythmic performance.

A clear picture of these rhythmic contributions of each muscle is crucial for diagnosis of any unnormal issue such as footdrop. To determine the required sensors and their range of action in footdrop diagnosis, the key factors involved in normal walking are described as the flowing [26]:

a. 

Plantar flexion begins at initial contact as the anterior tibialis and posterior tibialis muscles contract eccentrically to slow the foot’s movement. This phase develops when the foot moves in a range of 0–55 degrees, preventing a sudden foot slap. As the body moves forward into mid-stance, the plantar flexor muscles contract eccentrically to provide front-to-back (anterior–posterior) stability. As the body continues forward, these muscles shift to concentric contraction to help accelerate the body. At this phase, the dorsiflexors continue to provide support through eccentric contraction.

b. 

Dorsiflexion usually happens after the toe-off and during the swing phase which in a range of 0–25 degrees. At this point, the anterior tibialis and posterior tibialis muscles contract concentrically to lift the foot and prevent footdrop and drag toes. At the same time, hip and knee flexion increase foot clearance during the swing.

c. 

Inversion and Eversion occur during walking when the foot moves approximately 20 degrees and 10 degrees, respectively. From initial contact to loading response, a few degrees of eversion allow the foot to fully contact the ground. The posterior tibialis contracts eccentrically to stabilize the foot during mid-stance and terminal stance. The peroneal muscles drive heel movement during eversion, with their distinct nerve supply causing the motion to start slowly and then speed up, which usually completes in about one second. Inversion, controlled mainly by the posterior tibialis, may happen more quickly, in about 0.2 s. As the foot prepares for terminal swing, it increases stability and begins acceleration for the next step.

The previous investigation of the problematic walking gait of stroke survivors suggested that reflex muscle responses from a paretic limb during rhythmic, bilateral exercise are ungainly and spasmodic yet relate to beneficial adaptations from high-repetition training [27]. Earlier biopsies identified the rationale for this observed improvement, which demonstrated improvements in expression of the stress proteins ubiquitin and myofibrillar protein content after passive exercise of paralyzed limbs [28].

It is noted that such joint movements and muscle actions vary depending on an individual’s walking patterns, namely casually, cautiously, or urgently. Therefore, the precise diagnosis of footdrop issues should be personalized. Knowing the performance and the corresponding key parameters in gait analysis, it is realized that sensor technology should be capable of determining angles, muscle strains, foot deformation, and foot acceleration. The following subsection briefly describes the mathematical relationship between the foot (here simplified as a flat plate) deformation, acceleration, and stain.

The gait cycle was tracked using a combination of accelerometers and a gyroscope integrated into the smart foot–ankle device. The accelerometers captured linear accelerations along three axes, enabling detection of heel-strike and toe-off events through sharp changes in vertical and anterior–posterior acceleration. The gyroscope measured angular velocities about the ankle joint, allowing precise quantification of plantar flexion and dorsiflexion angles during the stance and swing phases. Together, these measurements differentiated key gait phases, namely heel-strike, mid-stance, toe-off, and swing. By measuring dorsiflexion angle and elevated toe acceleration during the swing phase, a clear digital picture of foot drop can be captured.

2.3. Foot–Ankle Model

To determine the optimum locations of the gyroscopes and accelerometers in the smart brace, a schematic model of the foot–ankle is presented, as shown in Figure 2. Three points, at the heel, at the toe, and at the fibula, are located on the brace for mounting the accelerometers, controller, and gyroscopes. It is also worth noting that rotation about the Z₁ axis is not a normal motion.

Rotation of the foot about the X₁-axis results in either inversion, where the toe moves toward the body’s midline, or eversion, where the toe moves away from it. To evaluate the deformation of the sole, strain can be measured at several points along the line connecting the heel to the toe. Additional key parameters for identifying foot drop include the rotations and accelerations at both the heel and toe. It should be noted that deformation can also be estimated indirectly from acceleration data by performing a double integration with respect to time. Meanwhile, the magnitude of rotation is directly obtained using gyroscope sensors. In this study, real-time strain magnitudes are precisely measured using embedded Fiber Bragg Grating (FBG) sensors. The fundamental principles of strain measurement with FBG sensors are briefly described in the following subsection.

2.4. Monitoring Sole Deformation

Most of the existing smart insoles use Inertial Measurement Unit (IMU)-based systems to measure sole deformation. However, IMU-based systems rely on indirect deformation measures using accelerometers and gyroscopes. IMU systems often suffer from accumulated integration errors, particularly during slow or irregular walking patterns, leading to reduced accuracy in detecting abnormal gait deviations such as foot drop. Fiber Bragg Grating (FBG) sensors are optical sensors that are used to measure and monitor changes in temperature and strain. They provide advantages such as multi-sensing and continuous sensing using only a single fiber line, ease of placing and embedding the sensor in the structure, electromagnetic interference immunity, high sensitivity, and multiplexing capability. The proposed configuration employs a network of multiplexed FBG sensors embedded directly within the insole material. This design enables simultaneous multi-point strain monitoring with high spatial resolution and real-time feedback. The approach enhances the ability to identify localized deformation patterns associated with abnormal walking conditions, such as foot drop, providing a more robust and precise sensing platform for gait analysis and rehabilitation applications.

The principle of a FBG sensor is based on the wavelength shifting of the reflected spectrum when strain or temperature change arises in the element, as given by the following:

$${\lambda }_{FBG}=2n_{eff}{\mathsf{\tau }}_{FBG}$$

(1)

where n_eff is the effective refractive index of the mode propagating in the fiber and ${\mathsf{\tau }}_{FBG}$ indicates the FBG period. Equation (3) implies that the reflected wavelength λ is affected by any variation in the physical or mechanical properties of the grating region. Similarly, changes in temperature lead to change in n_eff via the thermo-optic effect and in unconstrained fiber; FBG is influenced by thermal expansion or contraction. Introducing the effect of the variation of mechanical properties as k_T, and that of the temperature as k_ε, Equation (1) can be written as follows:

$${\displaystyle \frac{{\mathsf{\Delta }\lambda }_{FBG}}{{\lambda }_{FBG}}}=k_T\mathsf{\Delta }T+k_{\epsilon }\mathsf{\Delta }\epsilon$$

(2)

Considering the constant temperature of the structure during testing, the induced strain on the element can be estimated as follows:

$$∆\epsilon ={\displaystyle \frac{\mathsf{\Delta }{\lambda }_{FBG}}{k_{\epsilon }}}$$

(3)

For more detail on modelling FBG for strain measurement, one may consult the article written by Sarkandi and Zabihollah [29].

3. Experimental Works

3.1. Validation Experimental Tests

First, several preliminary experiments have been conducted to observe the functionality of the hypothesis.

In this work, FBG sensors are used to measure the strain. The performance of the FBG sensors in measuring foot deformation is validated by surface mounting an FBG sensor array at the bottom of a normal insole as shown in Figure 3a.

Further, a micro controller with embedded accelerometers and gyroscopes is considered for measuring rotation and acceleration at desired locations.

The performance and functionality of the controller in measuring the acceleration and rotation of a foot is demonstrated by mounting a controller at the heel and an auxiliary accelerometer at the toe as shown in Figure 3b.

Table 1. Sensors used in the experimentation and their model details.

Measure Parameter	Sensor/Device	Model/Manufacturer
Strain/stress	FBG Sensors	FBG-MR0010, an array of 4 FBG sensors located 10 mm apart from (Micronor Sensors, Inc., Ventura, CA, USA). FBG sensors have wavelength of 850 nm and 300 nm grating period.
	FBG Interrogator	FBGX100 with a wavelength range of 808–880 nm (FISENS®, Braunschweig, Germany)
Acceleration	Accelerometer	Arduino Nano 33 BLE Sense Lite, Model: Nina-B306
Rotational angle	Gyroscope	Arduino Nano 33 BLE Sense Lite, Model: Nina-B306
Acceleration	Accelerometer	HiLetgo 3pcs GY-521, Model: MPU-6050

provides information on the sensors used for experimental validation, as well as prototype fabrication of the smart foot–ankle brace.

3.2. Experimental Testing

This study is designed as a proof-of-concept investigation to compare gait patterns between normal walking and foot drop, rather than as a clinical trial or finalized device validation. As such, detailed sensor calibration procedures, hardware specifications (e.g., sampling rate, resolution, power consumption), and communication protocol evaluations were not the primary focus. All trials were conducted using the same device and setup, ensuring consistency for relative comparisons between healthy and abnormal gait. Several experimental tests were performed to demonstrate the functionality of the system, and future work will extend this study toward clinical applications, incorporating rigorous calibration, specification reporting, and systematic evaluation of wireless performance.

Simulation

Before testing on human subject, the system was tested using a foot–ankle model, as shown in Figure 4. In the second phase, the performance of the system when worn by a healthy man was tested for various locomotion pattern, as shown in Figure 5.

3.3. Prototype of the Smart Foot–Ankle Brace

After completing preliminary investigations, a practical prototype of the smart insole has been designed and fabricated.

Figure 6 shows the three-dimensional modelling of the insole. This model as then converted into STL file, which was used for 3D printer to print the insole. The final prototype of the smart insole was printed using a Replicator+ (Model: 3D-MP07825) 3D printer manufactured by MakerBot (New York, NY, USA). The insole was printed in two sections to provide sufficient flexibility by adding/replacing control boards in different configurations. Figure 7 shows the completed smart insole integrated with multiple sensors.

3.4. Practical Demonstration

The proof-of-concept evaluation focused on demonstrating the feasibility of the proposed smart foot–ankle brace for detecting foot drop and measuring foot sole deformation. Participants walked on a treadmill at a comfortable self-selected speed, which was defined as “normal walking” for individuals without foot drop. Walking speeds were approximately 1.0–1.2 m/s and each trial lasted 2–3 min, allowing multiple gait cycles to be recorded. Only one speed per participant was used in this preliminary study.

The 3D-printed insole, embedded with accelerometers, gyroscopes, and Fiber Bragg Grating (FBG) sensors, was worn by each participant. Data, including toe acceleration, ankle rotation, and sole deformation, were recorded in real time and transmitted via Bluetooth to the processing platform for visualization and analysis. This controlled protocol allowed assessment of relative differences in gait parameters between participants with and without foot drop. Future studies will expand to include multiple walking speeds, overground trials, and larger participant cohorts to evaluate clinical applicability. Figure 8 and Figure 9 demonstrate the application of the smart insole for patients with foot drop issues.

4. Results and Discussions

This study aimed to demonstrate the feasibility of a low-cost wearable smart foot–ankle brace for monitoring foot drop and foot deformation. Experiments were conducted on a treadmill at self-selected comfortable walking speeds (~1.0–1.2 m/s) to standardize gait cycles and enable controlled measurement of locomotion parameters. The term “normal walking” refers to treadmill walking performed by healthy participants without foot drop. Each trial lasted approximately 2–3 min, providing multiple gait cycles for analysis.

The results presented here focus on relative differences in gait parameters between normal walking and foot drop conditions. While the system demonstrated the capability to detect deviations in toe acceleration, ankle rotation, inversion/eversion cycles, and foot deformation, formal validation against gold-standard gait analysis systems was not performed in this proof-of-concept study. Future work will include comparative studies, repeated trials, and calibration to quantify absolute accuracy. This staged approach ensures that the current study highlights the feasibility and functionality of the wearable system while laying the groundwork for rigorous clinical validation.

4.1. Foot Deformation Detection

Figure 10 shows the deformation of the foot during normal walking, measured by four FBG sensor chains embedded in the insole. The strain follows a periodic pattern representing foot deformation during the walking cycle. It was observed that the maximum strain oscillates between approximately 100–1000 µm/m per cycle. Additionally, it should be noted that the three colors represent three FBG sensors positioned 10 mm apart. These measurements confirm that the smart insole can detect subtle changes in foot mechanics in real time, demonstrating the potential of FBG sensors for gait monitoring.

4.2. Monitoring Normal Walking

Acceleration and rotational angles were measured to capture a comprehensive view of the participant’s gait pattern. Figure 11 shows the acceleration and rotation of a healthy individual during normal walking. It should be noted that at 2 s, the foot was lifted abruptly to illustrate system performance, whereas normal acceleration values are typically around 1 m/s². Between 4–7 s, accelerations remained relatively stable with minor fluctuations. To improve precision, an additional accelerometer was embedded on the dorsal surface of the foot.

Figure 12 illustrates accelerations measured at the heel and toe when the participant walks on a treadmill at 1.0–1.2 m/s.

4.3. Detecting Inversion and Eversion Cycles

Inversion (inward rolling of the foot) and eversion (outward rolling) were monitored using accelerometers mounted on the heel and dorsum. Figure 13 shows acceleration data from the heel and dorsum during an eversion cycle in a healthy participant. The system captures outward foot rolling accurately in real time. It is realized that accelerations in the x and z directions are approximately 1 m/s² at each step.

Figure 14 depicts heel acceleration during inversion, illustrating the system’s ability to detect these complex foot movements. Figure 15 focuses on heel movement for detailed monitoring of the eversion and inversion cycles.

4.4. Foot Drop Monitoring

The smart foot–ankle brace was used to monitor participants with foot drop. Figure 16 shows acceleration data recorded by sensors mounted at the side and dorsal surface of the foot for a participant with foot drop. The maximum toe acceleration measured along two directions was approximately 3.7 and 4.7 m/s², compared to 0.5–0.8 m/s² during normal walking. The system successfully captured these differences in gait parameters, demonstrating its potential to identify abnormal gait events in real time.

Overall, the discussion demonstrates that the smart insole can measure multiple locomotion parameters and detect foot drop-related gait deviations in real time. While this proof-of-concept study did not include formal validation against gold-standard systems, the observed differences between normal and foot-drop walking provide clear evidence of feasibility. Future studies will include calibration, repeated trials, multiple walking speeds, overground walking, and comparison with validated measurement systems to establish absolute accuracy and clinical applicability.

5. Conclusions

This study presented the development of a wearable smart foot–ankle brace integrated with multiple sensors to monitor locomotion in individuals with foot drop. The system successfully demonstrated the ability to capture key gait parameters in real time, including rotation angle, ground reaction forces, and acceleration. While the proof-of-concept experiments confirm the feasibility of the approach, further work is needed to validate performance in real-world conditions and across a broader range of subjects. Future research will focus on refining sensor integration, enhancing signal processing and data transmission capabilities, and ultimately designing a shoe-based system suitable for long-term use in clinical and daily-life settings.