School of Art, Design and Media, East China University of Science and Technology, Shanghai 200237, China.
College of Mechanical Engineering, Donghua University, Shanghai 201620, China.
Sensors (Basel). 2021 Jan 15;21(2):578. doi: 10.3390/s21020578.
Traditional rigid exoskeletons can be challenging to the comfort of wearers and can have large pressure, which can even alter natural hand motion patterns. In this paper, we propose a low-cost soft exoskeleton glove (SExoG) system driven by surface electromyography (sEMG) signals from non-paretic hand for bilateral training. A customization method of geometrical parameters of soft actuators was presented, and their structure was redesigned. Then, the corresponding pressure values of air-pump to generate different angles of actuators were determined to support four hand motions (extension, rest, spherical grip, and fist). A two-step hybrid model combining the neural network and the state exclusion algorithm was proposed to recognize four hand motions via sEMG signals from the healthy limb. Four subjects were recruited to participate in the experiments. The experimental results show that the pressure values for the four hand motions were about -2, 0, 40, and 70 KPa, and the hybrid model can yield a mean accuracy of 98.7% across four hand motions. It can be concluded that the novel SExoG system can mirror the hand motions of non-paretic hand with good performance.
传统的刚性外骨骼对于佩戴者的舒适度来说可能是一个挑战，而且外骨骼的压力较大，甚至可能改变自然手部运动模式。在本文中，我们提出了一种由非瘫痪手的表面肌电图（sEMG）信号驱动的低成本软外骨骼手套（SExoG）系统，用于双侧训练。提出了一种软执行器几何参数的定制方法，并对其结构进行了重新设计。然后，确定气泵的相应压力值以产生不同角度的执行器，以支持四种手部运动（伸展、休息、球形握持和握拳）。提出了一种两步混合模型，结合神经网络和状态排除算法，通过健康肢体的 sEMG 信号识别四种手部运动。招募了四名受试者参加实验。实验结果表明，四种手部运动的压力值约为-2、0、40 和 70kPa，混合模型在四种手部运动中的平均准确率为 98.7%。可以得出结论，新型 SExoG 系统可以很好地模拟非瘫痪手的手部运动。
