Wearable Gait Analysis Research Articles

Quantifying Asymmetric Gait Pattern Changes Using a Hidden Markov Model Similarity Measure (HMM-SM) on Inertial Sensor Signals.

Wearable gait analysis systems using inertial sensors offer the potential for easy-to-use gait assessment in lab and free-living environments. This can enable objective long-term monitoring and decision making for individuals with gait disabilities. This study explores a novel approach that applies a hidden Markov model-based similarity measure (HMM-SM) to assess changes in gait patterns based on the gyroscope and accelerometer signals from just one or two inertial sensors. Eleven able-bodied individuals were equipped with a system which perturbed gait patterns by manipulating stance-time symmetry. Inertial sensor data were collected from various locations on the lower body to train hidden Markov models. The HMM-SM was evaluated to determine whether it corresponded to changes in gait as individuals deviated from their baseline, and whether it could provide a reliable measure of gait similarity. The HMM-SM showed consistent changes in accordance with stance-time symmetry in the following sensor configurations: pelvis, combined upper leg signals, and combined lower leg signals. Additionally, the HMM-SM demonstrated good reliability for the combined upper leg signals (ICC = 0.803) and lower leg signals (ICC = 0.795). These findings provide preliminary evidence that the HMM-SM could be useful in assessing changes in overall gait patterns. This could enable the development of compact, wearable systems for unsupervised gait assessment, without the requirement to pre-identify and measure a set of gait parameters.

Validity of Wearable Gait Analysis System for Measuring Lower-Limb Kinematics during Timed Up and Go Test

Few studies have reported on the validity of a sensor-based lower-limb kinematics evaluation during the timed up and go (TUG) test. This study aimed to determine the validity of a wearable gait sensor system for measuring lower-limb kinematics during the TUG test. Ten young healthy participants were enrolled, and lower-limb kinematics during the TUG test were assessed using a wearable gait sensor system and a standard optical motion analysis system. The angular velocities of the hip, knee, and ankle joints in sit-to-stand and turn-to-sit phases were significantly correlated between the two motion analysis systems (R = 0.612–0.937). The peak angles and ranges of motion of hip, knee, and ankle joints in the walking-out and walking-in phases were also correlated in both systems (R = 0.528–0.924). These results indicate that the wearable gait sensor system is useful for evaluating lower-limb kinematics not only during gait, but also during the TUG test.

A Modified Method of Wearable Gait Analysis for Stroke Patients Based on the Peak Width Threshold and Phase Re-Segmentation

A Modified Method of Wearable Gait Analysis for Stroke Patients Based on the Peak Width Threshold and Phase Re-Segmentation

Wearable Gait Analysis is Highly Sensitive in Detection of Fatigue-Induced Exercise in Low Intensity Training among University Football Players

Wearable Gait Analysis is Highly Sensitive in Detection of Fatigue-Induced Exercise in Low Intensity Training among University Football Players

Combined diagnosis for Parkinson's disease via gait and eye movement disorders

Background and objectivesWith the discovery of the potential role of gait and eye movement disorders in Parkinson's disease (PD) recognition, we intend to investigate the combined diagnostic value of gait and eye movement disorders for PD. MethodsWe enrolled some Chinese PD patients and healthy controls and separated them into the training and validation sets based on enrollment time. Performance in five oculomotor paradigms and in one gait paradigm was examined using an infrared eye tracking device and a wearable gait analysis device. We developed and validated a combined model for PD diagnosis via multivariate stepwise logistic regression analysis. Furthermore, subgroup comparisons and multi-model comparison were performed to assess its applicability and advantages. ResultsA total of 145 PD patients and 80 healthy controls in China were recruited. The pro-saccade velocity, the trunk-sway max, and the turn mean angular velocity were finally screened out for the model development. Incorporating age factor, the ternary model demonstrated more satisfactory performance on ROC (AUC of 0.953 in the training set and AUC of 0.972 in the validation set), calibration curve, and decision curve. A nomogram was drawn to visualize the model. The combined model outperforms individual models with a broad application and the unique diagnostic value for early detection of PD patients, especially TD-PD patients. ConclusionWe demonstrated the presence of gait and eye movement disorders, as well as the feasibility, applicability, and superiority of employing them together to diagnose PD.

Optimizing wearable motion tracking by assessing sagittal joint angle accuracy with minimal sensor use

Introduction
 Wearable motion tracking technology often focuses on reducing the number of sensors to simplify design and lower costs. Research has shown that single IMUs can reconstruct leg kinematics (Gholami et al., 2020; Hossain et al., 2022; Lim et al., 2020) and ground reaction forces (Jiang et al., 2020) effectively. Additionally, model-based methods have demonstrated the feasibility of using fewer gyroscopes to estimate stride length and motion range in healthy individuals and patients with coxarthritis (Salarian et al., 2013). In this study, we aim to assess the precision of sagittal joint angle estimations using strain sensors while minimizing sensor count.
 Methods
 We conducted a study with ten participants based on our previous work that involved collecting single-leg treadmill running data to monitor lower limb joint angles with piezoresistive strain sensors. Subjects ran on an instrumented treadmill at 8-10 km/h, wearing athletic pants embedded with nine strain sensors located on the hip, knee, and ankle. Optical motion capture provided reference kinematics. Our prior research achieved less than 1.5° error in the sagittal plane using a machine-learning approach. The current study explores the extent to which sensor reduction is possible without meaningful loss of accuracy. Three evaluation measures were used for assessment: Pearson correlation, dynamic time warping, and root-mean-squared error.
 Results
 The results from our correlation analysis will be used to develop a model that optimally balances between accuracy and minimizing the number of sensors. This has practical implications in sports science, where athletes could benefit from less intrusive and more comfortable performance monitoring, and in healthcare, for remote monitoring of patients with mobility issues.
 References
 Gholami, M., Napier, C., & Menon, C. (2020). Estimating lower extremity running gait kinematics with a single accelerometer: A deep learning approach. Sensors, 20(10), Article 2939. https://doi.org/10.3390/s20102939
 Hossain, M. S., Bin, Dranetz, J., Choi, H., & Guo, Z. (2022). DeepBBWAE-Net: A CNN-RNN based deep superlearner for estimating lower extremity sagittal plane joint kinematics using shoe-mounted IMU sensors in daily living. IEEE Journal of Biomedical and Health Informatics, 26(8), 3906-3917. https://doi.org/10.1109/jbhi.2022.3165383
 Jiang, X., Napier, C., Hannigan, B., Eng, J. J., & Menon, C. (2020). Estimating vertical ground reaction force during walking using a single inertial sensor. Sensors, 20(15), Article 4345. https://doi.org/10.3390/s20154345
 Lim, H., Kim, B., & Park, S. (2020). Prediction of lower limb kinetics and kinematics during walking by a single IMU on the lower back using machine learning. Sensors, 20(1), Article 130. https://doi.org/10.3390/s20010130
 Salarian, A., Burkhard, P. R., Vingerhoets, F. J. G., Jolles, B. M., & Aminian, K. (2013). A novel approach to reducing number of sensing units for wearable gait analysis systems. IEEE Transactions on Biomedical Engineering, 60(1), 72–77. https://doi.org/10.1109/TBME.2012.2223465

Early lameness detection in dairy cattle based on wearable gait analysis using semi-supervised LSTM-Autoencoder

Early lameness detection is crucial to ensure the welfare and productivity of dairy cows. However, current research on early lameness identification using wearable analysis relies on the limited robustness of indirect behavioral measures, which are susceptible to individual variations and imbalances in lameness samples. In this study, we propose a semi-supervised Long short-term memory (LSTM)-Autoencoder algorithm for early lameness detection in dairy cows through time series data reconstruction. We collected gait data from all four limbs of 30 dairy cows using four IMUs. A LSTM-Autoencoder with three LSTM hidden layers was trained to learn the time series features of healthy gaits. Each gait was reconstructed, and anomaly gaits exceeding a threshold were identified by comparing reconstructed gaits with actual gaits. The gait symmetry was measured by comparing the percentage of anomaly gait between opposite limbs as an indicator of lameness severity. With a high accuracy of 97.78% and a true negative rate of 98.33%, our integrated approach outperforms traditional methods in early lameness detection and lame limb identification, enabling real-time monitoring and timely identification of lameness. The study is the first attempt at using a time series anomaly detection framework with deep learning-based gait reconstruction for lameness detection. Wearable gait analysis offers portability and real-time capabilities, providing continuous, accurate, and comprehensive gait information unaffected by lighting and field-of-view limitations. This approach holds promise for enhancing animal welfare and optimizing management practices in the dairy industry through timely identification and continuous monitoring of lameness.

Automated assessment of foot elevation in adults with hereditary spastic paraplegia using inertial measurements and machine learning

BackgroundHereditary spastic paraplegias (HSPs) cause characteristic gait impairment leading to an increased risk of stumbling or even falling. Biomechanically, gait deficits are characterized by reduced ranges of motion in lower body joints, limiting foot clearance and ankle range of motion. To date, there is no standardized approach to continuously and objectively track the degree of dysfunction in foot elevation since established clinical rating scales require an experienced investigator and are considered to be rather subjective. Therefore, digital disease-specific biomarkers for foot elevation are needed.MethodsThis study investigated the performance of machine learning classifiers for the automated detection and classification of reduced foot dorsiflexion and clearance using wearable sensors. Wearable inertial sensors were used to record gait patterns of 50 patients during standardized 4 times 10 m walking tests at the hospital. Three movement disorder specialists independently annotated symptom severity. The majority vote of these annotations and the wearable sensor data were used to train and evaluate machine learning classifiers in a nested cross-validation scheme.ResultsThe results showed that automated detection of reduced range of motion and foot clearance was possible with an accuracy of 87%. This accuracy is in the range of individual annotators, reaching an average accuracy of 88% compared to the ground truth majority vote. For classifying symptom severity, the algorithm reached an accuracy of 74%.ConclusionHere, we show that the present wearable gait analysis system is able to objectively assess foot elevation patterns in HSP. Future studies will aim to improve the granularity for continuous tracking of disease severity and monitoring therapy response of HSP patients in a real-world environment.

Cognitive impairment is associated with gait variability and fall risk in Amyotrophic Lateral Sclerosis.

In amyotrophic lateral sclerosis (ALS), gait abnormalities contribute to poor mobility and represent a relevant risk for falls. To date, gait studies in ALS patients focused on the motor dimension of the disease, underestimating the cognitive aspects. Using a wearable gait analysis device, we compared gait patterns in ambulatory ALS patients with Mild Cognitive Impairment (ALS MCI+; n=18), and without MCI (ALS MCI-; n=24), and healthy individuals (HS; n=16) under two conditions: (1) normal gait (single task), (2) walking while counting backward (dual task). Finally, we examined if the occurrence and number of falls in the three months following the baseline test were related to cognition. In the single task condition, ALS patients, regardless of cognition, displayed higher gait variability than HS, especially for stance and swing time (p< 0.001). The dual task condition revealed additional differences in gait variability parameters between ALS MCI+ and ALS MCI- for cadence (p=0.005), stance time (p=0.04), swing time (p=0.04) and stability index (p=0.02). Moreover, ALS MCI+ showed a higher occurrence (p=0.001) and number of falls (p<0.001) at the follow-up. Regression analyses demonstrated that MCI condition predicted the occurrence of future falls (β= 3.649 p= 0.01) and, together with executive dysfunction, was associated with the number of falls (cognitive impairment: β= 0.63; p < 0.001; executive dysfunction: β= 0.39; p= 0.03), regardless of motor impairment at clinical examination. In ALS, MCI is associated with exaggerated gait variability and predicts the occurrence and number of short-term falls. This article is protected by copyright. All rights reserved.

Key points of development of motor skills in childhood embodied in gait parameters

ObjectiveTo observe changes of correlations of gait parameters of four sets of body in children aged 3–6. DesignA cross-sectional observational study. SettingDong Gang kindergarten in Suzhou, China. ParticipantsA total of 89 children between 3 and 6 years old. Main outcome measuresA total of 37 three-dimensional (3-D) gait parameters assessed with a wearable gait analysis system in 2-min walking test, for 3 times. ResultsThere were significant differences in gait speed, stride length and sagittal range of motion (ROM) of trunk among children of 3–6 years old (P < 0.05). The left and right toe out angle, sagittal ROM of waist, coronal ROM of trunk and arm swing velocity of male were significantly greater than those of female children (P < 0.05). Most gait parameters were symmetrical (P < 0.01). Canonical correlations of Upper Limbs Set vs. Trunk and Waist Sets increased by ages (P < 0.05). Canonical correlation of Trunk Set vs. Waist Set decreased by ages. Canonical correlations of Lower Limbs Set to any other sets were not significant (P > 0.05). ConclusionsValues and symmetry of gait parameters cannot reflect the development of motor skill during ages of 3–6. Proper trunk movement coordinating with upper limbs and isolating from waist is the key point of development of motor skill in walking. It is built during preschool period and girls develop better. Before the preschool period, lower limbs’ isolating movements from the other segments have already developed well. These key points of motor skills in walking should be considered when motor tasks for segment isolation and coordination are given to children with motor dysfunction.

Classifying Changes in Amputee Gait following Physiotherapy Using Machine Learning and Continuous Inertial Sensor Signals.

Wearable sensors allow for the objective analysis of gait and motion both in and outside the clinical setting. However, it remains a challenge to apply such systems to highly diverse patient populations, including individuals with lower-limb amputations (LLA) that present with unique gait deviations and rehabilitation goals. This paper presents the development of a novel method using continuous gyroscope data from a single inertial sensor for person-specific classification of gait changes from a physiotherapist-led gait training session. Gyroscope data at the thigh were collected using a wearable gait analysis system for five LLA before, during, and after completing a gait training session. Data from able-bodied participants receiving no intervention were also collected. Models using dynamic time warping (DTW) and Euclidean distance in combination with the nearest neighbor classifier were applied to the gyroscope data to classify the pre- and post-training gait. The model achieved an accuracy of 98.65% ± 0.69 (Euclidean) and 98.98% ± 0.83 (DTW) on pre-training and 95.45% ± 6.20 (Euclidean) and 94.18% ± 5.77 (DTW) on post-training data across the participants whose gait changed significantly during their session. This study provides preliminary evidence that continuous angular velocity data from a single gyroscope could be used to assess changes in amputee gait. This supports future research and the development of wearable gait analysis and feedback systems that are adaptable to a broad range of mobility impairments.

Validation of an Ear-Worn Wearable Gait Analysis Device.

Wearable devices capable of measuring gait parameters may provide a means to more economical gait analysis compared to conventional equipment comprising of a motion capture system and a forced treadmill. Beflex Coach (Beflex, Republic of Korea) is one such device but worn on the ear as Bluetooth earphones, unlike other wearables worn on the wrist, feet, or torso. In this study, the validity of the device was examined against a motion capture system and a forced treadmill for walking and running parameters. Five walking parameters (cadence, single support time, double support time, vertical oscillation (VO), and instantaneous vertical loading rate (IVLR)) and six running parameters (cadence, stance time, flight time, peak force, VO, and IVLR) were studied. Twenty young adults participated in walking or running on a forced treadmill at different speeds (walking: 0.8, 1.25, and 1.7 m/s for walking; running: 2, 2.5, and 3 m/s) while the two systems operated simultaneously. As a result, all parameters showed excellent associations (ICC > 0.75) and good agreements in Bland-Altman plots. The results of the study support the potential use of the ear-worn device as an inexpensive gait analysis equipment.

Parkinson's Disease Wearable Gait Analysis: Kinematic and Dynamic Markers for Diagnosis.

Introduction: Gait features differ between Parkinson's disease (PD) and healthy subjects (HS). Kinematic alterations of gait include reduced gait speed, swing time, and stride length between PD patients and HS. Stride time and swing time variability are increased in PD patients with respect to HS. Additionally, dynamic parameters of asymmetry of gait are significantly different among the two groups. The aim of the present study is to evaluate which kind of gait analysis (dynamic or kinematic) is more informative to discriminate PD and HS gait features. Methods: In the present study, we analyzed gait dynamic and kinematic features of 108 PD patients and 88 HS from four cohorts of two datasets. Results: Kinematic features showed statistically significant differences among PD patients and HS for gait speed and time Up and Go test and for selected kinematic dispersion indices (standard deviation and interquartile range of swing, stance, and double support time). Dynamic features did not show any statistically significant difference between PD patients and HS. Discussion: Despite kinematics features like acceleration being directly proportional to dynamic features like ground reaction force, the results of this study showed the so-called force/rhythm dichotomy since kinematic features were more informative than dynamic ones.

A Wearable Gait Analysis and Recognition Method for Parkinson's Disease Based on Error State Kalman Filter.

For the purpose of quantitative analysis, this paper proposes a wearable gait analysis method for Parkinson's disease (PD) to evaluates the motor ability. The error state Kalman filter (ESKF) is used for attitude estimation, and the gait parameters are modified by phase segmentation and zero velocity update (ZUPT) algorithm. In addition, this study uses gait parameters as classifier features to recognize abnormal gait, and compares the recognition effect with statistical features. The effect of our gait system is verified by comparison with the OptiTrack system, and the mean absolute error (MAE) of step length and foot clearance are 2.52 ±3.61 cm and 0.96 ±1.24 cm respectively. Forty Parkinson's patients and forty age-matched healthy people are recruited for gait comparison, the analysis results showed significant differences between the two groups. The abnormal gait recognition results show that gait features have stronger generalization ability than statistical features in leave-one-subject-out (LOSO) validation. The method proposed in this study can be applied to the gait analysis and objective evaluation of PD.

Ground Contact Time Estimating Wearable Sensor to Measure Spatio-Temporal Aspects of Gait.

Inpatient gait analysis is an essential part of rehabilitation for foot amputees and includes the ground contact time (GCT) difference of both legs as an essential component. Doctors communicate improvement advice to patients regarding their gait pattern based on a few steps taken at the doctor’s visit. A wearable sensor system, called Suralis, consisting of an inertial measurement unit (IMU) and a pressure measuring sock, including algorithms calculating GCT, is presented. Two data acquisitions were conducted to implement and validate initial contact (IC) and toe-off (TO) event detection algorithms as the basis for the GCT difference determination for able-bodied and prosthesis wearers. The results of the algorithms show a median GCT error of −51.7 ms (IMU) and 14.7 ms (sensor sock) compared to the ground truth and thus represent a suitable possibility for wearable gait analysis. The wearable system presented, therefore, enables a continuous feedback system for patients and, above all, a remote diagnosis of spatio-temporal aspects of gait behaviour based on reliable data collected in everyday life.

Comprehensive validation of a wearable foot sensor system for estimating spatiotemporal gait parameters by simultaneous three-dimensional optical motion analysis

BackgroundUse of a wearable gait analysis system (WGAS) is becoming common when conducting gait analysis studies due to its versatility. At the same time, its versatility raises a concern about its accuracy, because its calculations rely on assumptions embedded in its algorithms. The purpose of the present study was to validate twenty spatiotemporal gait parameters calculated by the WGAS by comparison with simultaneous measurements taken with an optical motion capture system (OMCS).MethodsTen young healthy volunteers wore two inertial sensors of the commercially available WGAS, Physilog®, on their feet and 23 markers for the OMCS on the lower part of the body. The participants performed at least three sets of 10-m walk tests at their self-paced speed in the laboratory equipped with 12 high-speed digital cameras with embedded force plates. To measure repeatability, all participants returned for a second day of testing within two weeks.ResultsTwenty gait parameters calculated by the WGAS had a significant correlation with the ones determined by the OMCS. Bland and Altman analysis showed that the between-device agreement for twenty gait parameters was within clinically acceptable limits. The validity of the gait parameters generated by the WGAS was found to be excellent except for two parameters, swing width and maximal heel clearance. The repeatability of the WGAS was excellent when measured between sessions.ConclusionThe present study showed that spatiotemporal gait parameters estimated by the WGAS were reasonably accurate and repeatable in healthy young adults, providing a scientific basis for applying this system to clinical studies.

Recent Trends and Practices Toward Assessment and Rehabilitation of Neurodegenerative Disorders: Insights From Human Gait.

The study of human movement and biomechanics forms an integral part of various clinical assessments and provides valuable information toward diagnosing neurodegenerative disorders where the motor symptoms predominate. Conventional gait and postural balance analysis techniques like force platforms, motion cameras, etc., are complex, expensive equipment requiring specialist operators, thereby posing a significant challenge toward translation to the clinics. The current manuscript presents an overview and relevant literature summarizing the umbrella of factors associated with neurodegenerative disorder management: from the pathogenesis and motor symptoms of commonly occurring disorders to current alternate practices toward its quantification and mitigation. This article reviews recent advances in technologies and methodologies for managing important neurodegenerative gait and balance disorders, emphasizing assessment and rehabilitation/assistance. The review predominantly focuses on the application of inertial sensors toward various facets of gait analysis, including event detection, spatiotemporal gait parameter measurement, estimation of joint kinematics, and postural balance analysis. In addition, the use of other sensing principles such as foot-force interaction measurement, electromyography techniques, electrogoniometers, force-myography, ultrasonic, piezoelectric, and microphone sensors has also been explored. The review also examined the commercially available wearable gait analysis systems. Additionally, a summary of recent progress in therapeutic approaches, viz., wearables, virtual reality (VR), and phytochemical compounds, has also been presented, explicitly targeting the neuro-motor and functional impairments associated with these disorders. Efforts toward therapeutic and functional rehabilitation through VR, wearables, and different phytochemical compounds are presented using recent examples of research across the commonly occurring neurodegenerative conditions [viz., Parkinson’s disease (PD), Alzheimer’s disease (AD), multiple sclerosis, Huntington’s disease (HD), and amyotrophic lateral sclerosis (ALS)]. Studies exploring the potential role of Phyto compounds in mitigating commonly associated neurodegenerative pathologies such as mitochondrial dysfunction, α-synuclein accumulation, imbalance of free radicals, etc., are also discussed in breadth. Parameters such as joint angles, plantar pressure, and muscle force can be measured using portable and wearable sensors like accelerometers, gyroscopes, footswitches, force sensors, etc. Kinetic foot insoles and inertial measurement tools are widely explored for studying kinematic and kinetic parameters associated with gait. With advanced correlation algorithms and extensive RCTs, such measurement techniques can be an effective clinical and home-based monitoring and rehabilitation tool for neuro-impaired gait. As evident from the present literature, although the vast majority of works reported are not clinically and extensively validated to derive a firm conclusion about the effectiveness of such techniques, wearable sensors present a promising impact toward dealing with neurodegenerative motor disorders.

Walking orientation randomness metric (WORM) score: pilot study of a novel gait parameter to assess walking stability and discriminate fallers from non-fallers using wearable sensors

BackgroundMusculoskeletal disorders can contribute to injurious falls and incur significant societal and healthcare burdens. Identification of fallers from non-fallers through wearable-based gait analysis can facilitate timely intervention to assist mobility and prevent falls whilst improving care and attention for high fall-risk patients. In this study, we use wearable sensor-based gait analysis to introduce a novel variable to assess walking stability in fallers and non-fallers – the Walking Orientation Randomness Metric. The WORM score quantifies the stability, or ‘figure-of-eight’ motion of a subject’s trunk during walking as an indicator of a falls-predictive (pathological) gait.MethodsWORM is calculated as the ‘figure-of-eight’ oscillation mapped out in the transverse-plane by the upper body’s centre-point during a walking bout. A sample of patients presenting to the Prince of Wales Hospital (Sydney, Australia) with a primary diagnosis of “falls for investigation” and age-matched healthy controls (non-fallers) from the community were recruited. Participants were fitted at the sternal angle with the wearable accelerometer, MetaMotionC (Mbientlab Inc., USA) and walked unobserved (at self-selected pace) for 5-50 m along an obstacle-free, carpeted hospital corridor.ResultsParticipants comprised of 16 fallers (mean age: 70 + 17) and 16 non-fallers (mean age: 70 + 9) based on a recent fall(s) history. The (median) WORM score was 17-fold higher (p < 0.001) in fallers (3.64 cm) compared to non-fallers (0.21 cm). ROC curve analyses demonstrate WORM can discriminate fallers from non-fallers (AUC = 0.97). Diagnostic analyses (cut-off > 0.51 cm) show high sensitivity (88%) and specificity (94%).ConclusionIn this pilot study we have introduced the WORM score, demonstrating its discriminative performance in a preliminary sample size of 16 fallers. WORM is a novel gait metric assessing walking stability as measured by truncal way during ambulation and shows promise for objective and clinical evaluation of fallers.

Wearable gait analysis systems: ready to be used by medical practitioners in geriatric wards?

Key summary pointsAimTo investigate the feasibility of wearable gait analysis in geriatric wards by testing the effectiveness and acceptance of the system.FindingsWearable gait analysis can be implemented into geriatric wards, showing its readiness for a transformation from a pure research tool to a practically usable gait analysis system.MessageDespite good transferability into clinical practice, future research should aim to increase functionality and applicability of wearable gait analysis systems in clinical contexts.

A Low-Cost Three-Axis Force Sensor for Wearable Gait Analysis Systems

Abstract This paper presents the design, analysis, and fabrication of a capacitive-based three-axis force sensor as the building block of a wearable sensing system to directly measure all the components of three-dimensional (3D) ground reaction forces (GRFs) during walking. The proposed sensor is low-cost and easy to fabricate with high accuracy, which promotes its accessibility and usability for gait analysis in clinical and research settings. The sensor is comprised of three parallel capacitors that enable three-axial force measurement while significantly reducing the complexity of fabrication and maintenance associated with three-axis force sensors. Comprehensive experiments were conducted to rigorously quantify different aspects of the sensor's performance. The static and dynamic errors along the three axes were less than 2.28%, which is well within the acceptable range for the intended application. The force sensor could decouple three-axial forces with a cross-sensitivity of less than 2%. The developed sensor also demonstrated desirable repeatability and hysteresis behaviors with almost no drift over long periods of usage.