A Wearable Sensing and Training System: Towards Gait Rehabilitation for Elderly Patients With Knee Osteoarthritis

Abstract

Traditional gait modification for knee osteoarthritis (KOA) patients relies on a lab-based system to sense the variation in the kinematic parameter and calculate the knee adduction moment (KAM) for rehabilitation evaluation. These lab-based systems are generally not portable or easy to implement in daily life. The wearable system in this paper depends on its sensing function to train the gait type and estimate the KAM by using a motion sensor and pressure-sensitive electric conductive rubber sensors. This paper describes the design of the wearable sensing and training system, the evaluation of its training effect, and predicted accuracy for the KAM curve. Six elderly patients with medial compartment KOA performed gait training trials in a motion capture laboratory. All subjects altered their foot progression angle from a natural gait to a toe-in gait by an average of 23.59° (p < 0.01). Five out of six participants reduced the first peak of the KAM by an average of 24.63% (p < 0.01). Meanwhile, the estimated KAM curves were similar to the reference ones with a median correlation coefficient of over 0.90, and the average root means square error did not exceed 0.37 N*m/(kg*m). The results show that the gait of the subject was altered to reduce the KAM with the wearable sensing and training system. In addition, the system was used to determine the variation in KAM before and after gait training. Overall, the wearable sensing and training system holds potential for treating symptoms and monitoring KOA progression in an outdoor environment.