Development of a Pressure Sensing System Coupled With Deployable Machine Learning Models for Prosthetic Applications

Abstract

Lower-limb amputations pose significant challenges, with over 150000 cases annually in U.S., leading to a high demand for effective prosthetics. However, only 43% of lower-limb prosthetic users report satisfaction, primarily due to issues with socket fit, which is critical for comfort, stability, and preventing injury. This study presents a novel and deployable sensing system for potentially real-time monitoring of prosthetic socket fit by using pressure sensors and convolutional neural networks (CNNs) to analyze the pressure distribution within the socket. Two CNN-based strategies were implemented, namely, a long-term time series analysis and a single time step representation. The system was designed for edge deployment on the Sony Spresense microcontroller, maintaining a small model size while achieving high accuracy. Results show that the CNN models, particularly those optimized with the stochastic gradient descent (SGD), demonstrated robustness and high transferability. This system provides a cost-effective, portable solution to improve prosthetic fit, enhancing patient care and preventing gait-related injuries.