Automatic Exercise Assistance for the Elderly Using Real-Time Adaptation to Performance and Affect

Abstract

This work presents the design of a system and methodology for reducing risk of locomotive syndrome among the elderly through the delivery of real-time at-home exercise assistance via intensity modulation of a worn soft exoskeleton. An Adaptive Neural Network (ANN) is proposed for the prediction of locomotive risk based on squat exercise performance. A preliminary pilot evaluation was conducted to determine how well these two performance metrics relate by training the ANN to predict test scores among three standard tests for locomotive risk with features from joint tracking data. The promising initial results of this evaluation are presented with discussions for future implementation of affective classification and a combined adaptation strategy.