痉挛
材料科学
信号(编程语言)
计算机科学
生物医学工程
物理医学与康复
定量评估
康复
肌电图
灵敏度(控制系统)
人工智能
质量评定
摩擦电效应
改良阿什沃思量表
传感器
加速度计
医学
触觉传感器
外骨骼
作者
Zhixin Wang,Yuanpeng Fu,Tianzhao Bu,Yuqi Qiu,Bo Pang,Qingyang Zheng,Sen Zhou,Yangyang Li,Xiling Xiao,Zhouping Yin,Hao Wu
标识
DOI:10.1002/adfm.202523580
摘要
Abstract Quantitative and objective assessment of muscle spasticity grades in post‐stroke patients plays a pivotal role for physicians and patients in adjusting rehabilitation plans and preventing secondary complications. However, the development of such methods for spasticity detection and assessment is hindered by the subjectivity of conventional assessment methods, the limited clinical applicability of digital‐devices, and the structural complexity of sensors used for monitoring muscle strength. Here, a multimodal on‐skin sensor is developed, enabling simultaneous acquisition of surface electromyography (sEMG) and triboelectric signals, through wavelet analysis, the signal from the sensor can comprehensively reflect the bioelectrical and biomechanical characteristics of human motion. The high‐performance triboelectric materials (BPA NPs) with metal‐organic–inorganic core‐shell structures are synthesized to enhance the signal quality of the multimodal on‐skin sensor by leveraging the self‐polymerization ability and weak reducibility of dopamine. The sensitivity of the multimodal on‐skin sensor is improved by designing a radially arranged micro‐cone array with gradient heights. Additionally, a quantitative spasticity assessment strategy is developed by integrating muscle co‐activation coefficients (Index 1) and antagonistic efficacy metrics (Index 2), which are strongly correlated with the Modified Ashworth Scale scores. The multimodal on‐skin sensor with the proposed assessment strategy enables quantitative assessment of spasticity in 9 spasticity patients.
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