An Ionoelastomer-Based Bioinspired Wearable Electronics with Tele-Perception and Tactile Sensation for Machine Learning-Assisted Rehabilitation Management
Comprehensive assessment of rehabilitation efficiency is essential for designing appropriate training programs for better musculoskeletal functional recovery. Existing contact-receptor-dependent rehabilitation assessment systems mostly focus on assessing the restoration of muscle function by evaluating grip strength or joint flexion angle; however, parameters reflecting neuromuscular synergistic function are always overlooked. Herein, we develop an ionoelastomer-based soft artificial electroreceptor (SAER) that integrates tele-perception and tactile sensation to track the rehabilitation process, collecting signals related to approaching speed and grip strength sequentially. The SAER uses polyurethane ionoelastomer incorporated with quasi-solid conductive salt as the electric field receptor, and is integrated on a rehabilitation-training ball after assembly to establish an untethered detection device; this enables the remote capture of hand approaching parameter within a 9 cm range, followed by the quantification of grip strength when contacting and grasping. Furthermore, a data-driven assessment system is established by integrating machine learning, which accurately classifies rehabilitation efficiency into six levels; it supports for rehabilitation evaluation and training programs adjustment. Overall, the SAER-based rehabilitation management system establishes a paradigm that synergistically evaluating parameters corresponding to neuromuscular functional restoration and holds strong potential for home-based active rehabilitation for minimizing dependence on frequent clinical supervision.