Department of Medical and Technical Information Technology, Bauman Moscow State Technical University, 105005 Moscow, Russia.
Medical Information Technology, RWTH Aachen University, 52074 Aachen, Germany.
Sensors (Basel). 2021 Dec 27;22(1):152. doi: 10.3390/s22010152.
Creating highly functional prosthetic, orthotic, and rehabilitation devices is a socially relevant scientific and engineering task. Currently, certain constraints hamper the development of such devices. The primary constraint is the lack of an intuitive and reliable control interface working between the organism and the actuator. The critical point in developing these devices and systems is determining the type and parameters of movements based on control signals recorded on an extremity. In the study, we investigate the simultaneous acquisition of electric impedance (EI), electromyography (EMG), and force myography (FMG) signals during basic wrist movements: grasping, flexion/extension, and rotation. For investigation, a laboratory instrumentation and software test setup were made for registering signals and collecting data. The analysis of the acquired signals revealed that the EI signals in conjunction with the analysis of EMG and FMG signals could potentially be highly informative in anthropomorphic control systems. The study results confirm that the comprehensive real-time analysis of EI, EMG, and FMG signals potentially allows implementing the method of anthropomorphic and proportional control with an acceptable delay.
研发高度实用的义肢、矫形器和康复设备是一项具有社会意义的科学和工程任务。目前，某些限制因素阻碍了此类设备的发展。主要限制因素是缺乏在生物体和执行器之间工作的直观、可靠的控制接口。开发这些设备和系统的关键点在于根据记录在肢体上的控制信号来确定运动的类型和参数。在研究中，我们研究了在基本手腕运动（抓握、弯曲/伸展和旋转）期间同时获取电阻抗 (EI)、肌电图 (EMG) 和力肌电图 (FMG) 信号。为此，我们制作了一个实验室仪器和软件测试设置来记录和收集数据。对采集到的信号的分析表明，EI 信号与 EMG 和 FMG 信号的分析相结合，在拟人控制系统中可能具有高度信息性。研究结果证实，EI、EMG 和 FMG 信号的综合实时分析可能允许以可接受的延迟实现拟人化和比例控制的方法。
