An Ionoelastomer-Based Bioinspired Wearable Electronics with Tele-Perception and Tactile Sensation for Machine Learning-Assisted Rehabilitation Management

• • The SAER achieves remote hand approaching detection within a 9 cm range, enabling pre-contact kinematic capture that conventional contact-receptor systems cannot provide; this threshold is critical for early-stage neuromuscular synergy assessment in home settings. • • Grip strength quantification is performed upon contact and grasping, with the ionoelastomer receptor transducing mechanical deformation into electrical signals; this dual-mode operation eliminates the need for separate force and proximity sensors, reducing device complexity and cost. • • Machine learning classification of rehabilitation efficiency into six levels demonstrates high accuracy, providing a data-driven metric that outperforms subjective clinical observation; this supports objective training program adjustment without frequent clinical visits. • • The untethered detection device integrated on a rehabilitation-training ball enables home-based active rehabilitation, potentially reducing clinical supervision dependency by enabling remote monitoring; this addresses the scalability bottleneck of conventional rehabilitation assessments.