Development of a Wearable Sleeve-Based System Combining Polymer Optical Fiber Sensors and an LSTM Network for Estimating Knee Kinematics - B. L. Pugliese; A. Angelucci; F. Parisi; S. Sapienza; E. Fabara; G. Corniani; A. S. Tenforde; A. Aliverti; D. Demarchi; P. Bonato (IEEE Transactions on Neural Systems and Rehabilitation Engineering)
This study presents a novel wearable solution integrating Polymer Optical Fiber (POF) sensors into a knee sleeve to monitor knee flexion/extension (F/E) patterns during walking. POF sensors offer advantages such as flexibility, light weight, and robustness to electromagnetic interference, making them ideal for wearable applications. However, when one integrates these sensors into a knee sleeve, they exhibit non-linearities, including hysteresis and mode coupling, which complicate signal interpretation. To address this issue, a Long Short-Term Memory (LSTM) network was implemented to model temporal dependencies in sensor output, hence providing accurate knee angle estimates. Data were collected from 31 participants walking at different speeds on a treadmill, using a camera-based motion capture system for validation. Configurations with multiple (up to five) sensors were considered. The best performance was achieved using three sensors, yielding a median root mean square error (RMSE) of 3.41° (interquartile range: 2.50° – $5.19^{° }\text {)}$ . Whereas using multiple sensors generally improved robustness, the inclusion of data from sub-optimally placed sensors negatively affected performance. The technology holds potential for clinical application in knee osteoarthritis (OA) management. Future work should focus on optimizing signal calibration and expanding the dataset to facilitate accounting for the different ways in which the knee sleeve conforms to the anatomy of different individuals.