A novel wearable solution using soft robotic sensors (SRS) has been investigated to model foot-ankle kinematics during gait cycles. The capacitance of SRS related to foot-ankle basic movements was quantified during the gait movements of 20 participants on a flat surface as well as a cross-sloped surface. In order to evaluate the power of SRS in modeling foot-ankle kinematics, three-dimensional (3D) motion capture data was also collected for analyzing gait movement. Three different approaches were employed to quantify the relationship between the SRS and the 3D motion capture system, including multivariable linear regression, an artificial neural network (ANN), and a time-series long short-term memory (LSTM) network. Models were compared based on the root mean squared error (RMSE) of the prediction of the joint angle of the foot in the sagittal and frontal plane, collected from the motion capture system. There was not a significant difference between the error rates of the three different models. The ANN resulted in an average RMSE of 3.63, being slightly more successful in comparison to the average RMSE values of 3.94 and 3.98 resulting from multivariable linear regression and LSTM, respectively. The low error rate of the models revealed the high performance of SRS in capturing foot-ankle kinematics during the human gait cycle.
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Closing the Wearable Gap: Foot–ankle kinematic modeling via deep learning models based on a smart sock wearable
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.
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- Award ID(s):
- 1827652
- NSF-PAR ID:
- 10437667
- Date Published:
- Journal Name:
- Wearable Technologies
- Volume:
- 4
- ISSN:
- 2631-7176
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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The purpose of this study was to use 3D motion capture and stretchable soft robotic sensors (SRS) to collect foot-ankle movement on participants performing walking gait cycles on flat and sloped surfaces. The primary aim was to assess differences between 3D motion capture and a new SRS-based wearable solution. Given the complex nature of using a linear solution to accurately quantify the movement of triaxial joints during a dynamic gait movement, 20 participants performing multiple walking trials were measured. The participant gait data was then upscaled (for the SRS), time-aligned (based on right heel strikes), and smoothed using filtering methods. A multivariate linear model was developed to assess goodness-of-fit based on mean absolute error (MAE; 1.54), root mean square error (RMSE; 1.96), and absolute R2 (R2; 0.854). Two and three SRS combinations were evaluated to determine if similar fit scores could be achieved using fewer sensors. Inversion (based on MAE and RMSE) and plantar flexion (based on R2) sensor removal provided second-best fit scores. Given that the scores indicate a high level of fit, with further development, an SRS-based wearable solution has the potential to measure motion during gait- based tasks with the accuracy of a 3D motion capture system.more » « less
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null (Ed.)Estimating center of mass (COM) through sensor measurements is done to maintain walking and standing stability with exoskeletons. The authors present a method for estimating COM kinematics through an artificial neural network, which was trained by minimizing the mean squared error between COM displacements measured by a gold-standard motion capture system and recorded acceleration signals from body-mounted accelerometers. A total of 5 able-bodied participants were destabilized during standing through: (1) unexpected perturbations caused by 4 linear actuators pulling on the waist and (2) volitionally moving weighted jars on a shelf. Each movement type was averaged across all participants. The algorithm’s performance was quantified by the root mean square error and coefficient of determination ( R 2 ) calculated from both the entire trial and during each perturbation type. Throughout the trials and movement types, the average coefficient of determination was 0.83, with 89% of the movements with R 2 > .70, while the average root mean square error ranged between 7.3% and 22.0%, corresponding to 0.5- and 0.94-cm error in both the coronal and sagittal planes. COM can be estimated in real time for balance control of exoskeletons for individuals with a spinal cord injury, and the procedure can be generalized for other gait studies.more » « less
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null (Ed.)For the controller of wearable lower-limb assistive devices, quantitative understanding of human locomotion serves as the basis for human motion intent recognition and joint-level motion control. Traditionally, the required gait data are obtained in gait research laboratories, utilizing marker-based optical motion capture systems. Despite the high accuracy of measurement, marker-based systems are largely limited to laboratory environments, making it nearly impossible to collect the desired gait data in real-world daily-living scenarios. To address this problem, the authors propose a novel exoskeleton-based gait data collection system, which provides the capability of conducting independent measurement of lower limb movement without the need for stationary instrumentation. The basis of the system is a lightweight exoskeleton with articulated knee and ankle joints. To minimize the interference to a wearer’s natural lower-limb movement, a unique two-degrees-of-freedom joint design is incorporated, integrating a primary degree of freedom for joint motion measurement with a passive degree of freedom to allow natural joint movement and improve the comfort of use. In addition to the joint-embedded goniometers, the exoskeleton also features multiple positions for the mounting of inertia measurement units (IMUs) as well as foot-plate-embedded force sensing resistors to measure the foot plantar pressure. All sensor signals are routed to a microcontroller for data logging and storage. To validate the exoskeleton-provided joint angle measurement, a comparison study on three healthy participants was conducted, which involves locomotion experiments in various modes, including overground walking, treadmill walking, and sit-to-stand and stand-to-sit transitions. Joint angle trajectories measured with an eight-camera motion capture system served as the benchmark for comparison. Experimental results indicate that the exoskeleton-measured joint angle trajectories closely match those obtained through the optical motion capture system in all modes of locomotion (correlation coefficients of 0.97 and 0.96 for knee and ankle measurements, respectively), clearly demonstrating the accuracy and reliability of the proposed gait measurement system.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1017/wtc.2023.3
