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Abstract The development of wearable technology, which enables motion tracking analysis for human movement outside the laboratory, can improve awareness of personal health and performance. This study used a wearable smart sock prototype to track foot–ankle kinematics during gait movement. Multivariable linear regression and two deep learning models, including long short-term memory (LSTM) and convolutional neural networks, were trained to estimate the joint angles in sagittal and frontal planes measured by an optical motion capture system. Participant-specific models were established for ten healthy subjects walking on a treadmill. The prototype was tested at various walking speeds to assess its ability to track movements for multiple speeds and generalize models for estimating joint angles in sagittal and frontal planes. LSTM outperformed other models with lower mean absolute error (MAE), lower root mean squared error, and higher R -squared values. The average MAE score was less than 1.138° and 0.939° in sagittal and frontal planes, respectively, when training models for each speed and 2.15° and 1.14° when trained and evaluated for all speeds. These results indicate wearable smart socks to generalize foot–ankle kinematics over various walking speeds with relatively low error and could consequently be used to measure gait parameters without the need for a lab-constricted motion capture system. 

Standards for the fatigue testing of wearable sensing technologies are lacking. The majority of published fatigue tests for wearable sensors are performed on proof-of-concept stretch sensors fabricated from a variety of materials. Due to their flexibility and stretchability, polymers are often used in the fabrication of wearable sensors. Other materials, including textiles, carbon nanotubes, graphene, and conductive metals or inks, may be used in conjunction with polymers to fabricate wearable sensors. Depending on the combination of the materials used, the fatigue behaviors of wearable sensors can vary. Additionally, fatigue testing methodologies for the sensors also vary, with most tests focusing only on the low-cycle fatigue (LCF) regime, and few sensors are cycled until failure or runout are achieved. Fatigue life predictions of wearable sensors are also lacking. These issues make direct comparisons of wearable sensors difficult. To facilitate direct comparisons of wearable sensors and to move proof-of-concept sensors from “bench to bedside”, fatigue testing standards should be established. Further, both high-cycle fatigue (HCF) and failure data are needed to determine the appropriateness in the use, modification, development, and validation of fatigue life prediction models and to further the understanding of how cracks initiate and propagate in wearable sensing technologies. 

Background: Wearable technology is used by clinicians and researchers and play a critical role in biomechanical assessments and rehabilitation. Objective: The purpose of this research is to validate a soft robotic stretch (SRS) sensor embedded in a compression knee brace (smart knee brace) against a motion capture system focusing on knee joint kinematics. Methods: Sixteen participants donned the smart knee brace and completed three separate tasks: non-weight bearing knee flexion/extension, bodyweight air squats, and gait trials. Adjusted R2 for goodness of fit (R2), root mean square error (RMSE), and mean absolute error (MAE) between the SRS sensor and motion capture kinematic data for all three tasks were assessed. Results: For knee flexion/extension: R2 = 0.799, RMSE = 5.470, MAE = 4.560; for bodyweight air squats: R2 = 0.957, RMSE = 8.127, MAE = 6.870; and for gait trials: R2 = 0.565, RMSE = 9.190, MAE = 7.530 were observed. Conclusions: The smart knee brace demonstrated a higher goodness of fit and accuracy during weight-bearing air squats followed by non-weight bearing knee flexion/extension and a lower goodness of fit and accuracy during gait, which can be attributed to the SRS sensor position and orientation, rather than range of motion achieved in each task.