Soft Sensing Shirt for Shoulder Kinematics Estimation
Yichu Jin, Christina Glover, Haedo Cho, Oluwaseun A. Araromi, Moritz A. Graule, Na Li, Robert J. Wood, Conor J. Walsh
Abstract
Soft strain sensors have been explored as an unobtrusive approach for wearable motion tracking. However, accurate tracking of multi degree-of-freedom (DOF) noncyclic joint movements remains a challenge. This paper presents a soft sensing shirt for tracking shoulder kinematics of both cyclic and random arm movements in 3 DOFs: adduction/abduction, horizontal flexion/extension, and internal/external rotation. The sensing shirt consists of 8 textile-based capacitive strain sensors sewn around the shoulder joint that communicate to a customized readout electronics board through sewn micro-coaxial cables. An optimized sensor design includes passive shielding and demonstrates high linearity and low hysteresis, making it suitable for wearable motion tracking. In a study with a single human subject, we evaluated the tracking capability of the integrated shirt in comparison with a ground truth optical motion capture system. An ensemble-based regression algorithm was implemented in post-processing to estimate joint angles and angular velocities from the strain sensor data. Results demonstrated root mean square errors (RMSEs) less than 4.5° for joint angle estimation and normalized root mean square errors (NRMSEs) less than 4% for joint velocity estimation. Furthermore, we applied a recursive feature elimination (RFE)-based sensor selection analysis to down select the number of sensors for future shirt designs. This sensor selection analysis found that 5 sensors out of 8 were sufficient to generate comparable accuracies.