静力预测:表面肌电图与电阻抗肌电图的比较分析与融合策略。

IF 5.2 2区 医学 Q2 ENGINEERING, BIOMEDICAL
Pan Xu;Junwei Zhou;Yuandong Zhuang;Xinyu Li;Zeljka Lucev Vasic;Mario Cifrek;Yuqing Liu;Yueming Gao
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引用次数: 0

摘要

力预测是上肢功能康复的关键。表面肌电图(sEMG)信号在肌力研究中起着关键作用,但其非平稳性挑战了表面肌电图驱动模型的可靠性。这个问题可以通过与电阻抗肌图(EIM)的融合来缓解,EIM是一种结合组织形态信息的主动传感技术。本研究设计了一种可穿戴式多模态生理测量系统,可同时采集肌电信号和脑电信号。定义特征量化指标,定量分析EIM和sEMG在静力预测中的效果。最后,我们提出了自注意卷积长短期记忆(SACLSTM)网络来捕获EIM和sEMG特征之间的时空信息,用于跨模态特征融合。结果表明,与表面肌电信号相比,EIM对静力的变化表现出更大的敏感性,尤其是在低肌肉激活水平时。此外,所提出的SACLSTM网络明显优于LSTM、ConvLSTM和其他几种基线方法。与LSTM和ConvLSTM网络相比,SACLSTM模型的R2分别提高了12.4%和3%,均方根误差降低了63%和29%。特别是对于上肢功能障碍患者,与仅使用EIM或sEMG单峰特征相比,特征融合后的多模态模型的准确性和稳定性显著提高。本研究强调了EIM和sEMG特征融合在肌力预测方面的巨大潜力,为功能性运动康复领域开辟了新的实践路径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Static Force Prediction: Comparative Analysis and Fusion Strategies Between Surface Electromyography and Electrical Impedance Myography
Force prediction is crucial for functional rehabilitation of the upper limb. Surface electromyography (sEMG) signals play a pivotal role in muscle force studies, but its non-stationarity challenges the reliability of sEMG-driven models. This problem may be alleviated by fusion with electrical impedance myography (EIM), an active sensing technique incorporating tissue morphology information. This study designed a wearable multimodal physiological measurement system to acquire sEMG and EIM signals simultaneously. The feature quantification indexes were defined for quantitative analysis of the efficacy of EIM and sEMG in static force prediction. We finally proposed Self-Attention Convolutional Long Short-Term Memory (SACLSTM) network to capture the spatio-temporal information among EIM and sEMG features for cross-modal feature fusion. The results indicated that EIM exhibited greater sensitivity to variations in static force compared to sEMG, especially at low muscle activation levels. Furthermore, the proposed SACLSTM network is significantly superior to LSTM, ConvLSTM, and several other baseline methods. Compared to the LSTM and ConvLSTM networks, the SACLSTM model exhibits an ${R}^{{2}}$ improvement of 12.4% and 3%, respectively, and an root mean square error reduction of 63% and 29%. Especially for patients with upper limb dysfunction, the accuracy and stability of the multimodal model were significantly improved after feature fusion compared with using only EIM or sEMG unimodal features. This study emphasised the great potential of fusing EIM and sEMG features to improve performance in the muscle force prediction, opening up new practice paths in the field of functional motor rehabilitation.
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来源期刊
CiteScore
8.60
自引率
8.20%
发文量
479
审稿时长
6-12 weeks
期刊介绍: Rehabilitative and neural aspects of biomedical engineering, including functional electrical stimulation, acoustic dynamics, human performance measurement and analysis, nerve stimulation, electromyography, motor control and stimulation; and hardware and software applications for rehabilitation engineering and assistive devices.
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