Closing the Wearable Gap: Foot-ankle kinematic modeling via deep learning models based on a smart sock wearable

Samaneh Davarzani; David Saucier; Purva Talegaonkar; Erin Parker; Alana Turner; Carver Middleton; Will Carroll; John E Ball; Ali Gurbuz; Harish Chander; Reuben F Burch; Brian K Smith; Adam Knight; Charles Freeman

doi:10.1017/wtc.2023.3

Closing the Wearable Gap: Foot-ankle kinematic modeling via deep learning models based on a smart sock wearable

Wearable Technol. 2023 Feb 20:4:e4. doi: 10.1017/wtc.2023.3. eCollection 2023.

Authors

Samaneh Davarzani^{1

2}, David Saucier², Purva Talegaonkar^{1

2}, Erin Parker^{1

2

3}, Alana Turner^{2

4}, Carver Middleton^{2

3}, Will Carroll^{2

3}, John E Ball^{2

3}, Ali Gurbuz³, Harish Chander^{2

4}, Reuben F Burch^{1

2}, Brian K Smith^{1

2}, Adam Knight^{2

4}, Charles Freeman^{2

5}

Affiliations

¹ Department of Industrial and Systems Engineering, Mississippi State University, Mississippi State, MS, USA.
² Human Factors and Athlete Engineering, Center for Advanced Vehicular Systems, Mississippi State University, Mississippi State, MS, USA.
³ Department of Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS, USA.
⁴ Department of Kinesiology, Mississippi State University, Mississippi State, MS, USA.
⁵ School of Human Sciences, Mississippi State University, Mississippi State, MS, USA.

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.

Keywords: biomechanics; embedded electronics; performance characterization; sensors; soft wearable robotics.