Hierarchically Porous Iontronic Fiber‐Based Sensor Array with Ultrahigh Sensitivity for Stroke Rehabilitation Assistance

Abstract Stroke‐induced hemiplegia is a major cause of long‐term disability, with recovery relying on repetitive joint training. However, conventional methods often require assistance from professionals or the use of bulky, nonconformal devices, hindering efficient therapy. In this paper, a simple strategy is presented to construct intricate microstructures on individual fibers. By controlling the solvent evaporation behavior during the solution blow spinning process, hierarchically porous iontronic fiber crossbar (HPIFC) pressure sensors are fabricated. These sensors exhibit exceptional sensitivity that exceeds that of most existing capacitive fiber crossbar sensors by more than two orders of magnitude. Leveraging sensor arrays integrated with machine learning algorithms, subtle joint motions are successfully detected as small as 1.27° and achieve accurate recognition of multiple angular degrees‐of‐freedom (multiple angular DoF) joint movements. On the basis of these findings, a passive rehabilitation training system is developed for hemiplegic patients, as well as an automated system for the quantitative assessment of rehabilitation progress, offering a comprehensive and efficient solution for poststroke recovery.