Near‐Sensor Reservoir Computing for Gait Recognition via a Multi‐Gate Electrolyte‐Gated Transistor

作者：Xuerong Liu, Cui Sun, Zhecheng Guo, Xiangling Xia, Qian Jiang, Xiaoyu Ye, Jie Shang, Yuejun Zhang, Xiaojian Zhu, Run‐Wei Li · 发表于：Advanced Science · 年份：2023 · DOI：10.1002/advs.202300471 · 被引用次数：60 · 研究领域：Advanced Memory and Neural Computing、Neural Networks and Reservoir Computing、Neural dynamics and brain function

The recent emergence of various smart wearable electronics has furnished the rapid development of human-computer interaction, medical health monitoring technologies, etc. Unfortunately, processing redundant motion and physiological data acquired by multiple wearable sensors using conventional off-site digital computers typically result in serious latency and energy consumption problems. In this work, a multi-gate electrolyte-gated transistor (EGT)-based reservoir device for efficient multi-channel near-sensor computing is reported. The EGT, exhibiting rich short-term dynamics under voltage modulation, can implement nonlinear parallel integration of the time-series signals thus extracting the temporal features such as the synchronization state and collective frequency in the inputs. The flexible EGT integrated with pressure sensors can perform on-site gait information analysis, enabling the identification of motion behaviors and Parkinson's disease. This near-sensor reservoir computing system offers a new route for rapid analysis of the motion and physiological signals with significantly improved efficiency and will lead to robust smart flexible wearable electronics.