Energy-Efficient On-Chip Training for Customized Home-based Rehabilitation Systems

Rehabilitation is an essential process for patients suffering from motor disorders. It is generally performed by experts in a clinical environment. Home-based rehabilitation systems allow patients to perform rehabilitation without going to clinics, thus, reducing the commute and healthcare costs. Human joint estimation allows visualization of body movements required for rehabilitation. However, the estimations can be inaccurate if they are not customized for the specific patient. Therefore, we propose a personalized rehabilitation system customized to new patients utilizing energy-efficient on-chip training with in-memory acceleration. Experiments show that it customizes to new patients successfully with an average 28.01% lower error and provides energy-efficient estimates of human joint coordinates with 611.1× lower inference energy and 14.0× faster training.