Institute of Robotics and Intelligent Systems, Xi'an Jiaotong University, Xi'an, People's Republic of China.
J Neural Eng. 2021 Feb 22;18(1). doi: 10.1088/1741-2552/abbece.
At present, sEMG-based gesture recognition requires vast amounts of training data; otherwise it is limited to a few gestures.. This paper presents a novel dynamic energy model that decodes continuous hand actions by training small amounts of sEMG data.. The activation of forearm muscles can set the corresponding fingers in motion or state with movement trends. The moving fingers store kinetic energy, and the fingers with movement trends store potential energy. The kinetic energy and potential energy in each finger are dynamically allocated due to the adaptive-coupling mechanism of five-fingers in actual motion. Meanwhile, the sum of the two energies remains constant at a certain muscle activation. We regarded hand movements with the same direction of acceleration for five-finger as the same in energy mode and divided hand movements into ten energy modes. Independent component analysis and machine learning methods were used to model associations between sEMG signals and energy modes and expressed gestures by energy form adaptively. This theory imitates the self-adapting mechanism in actual tasks. Thus, ten healthy subjects were recruited, and three experiments mimicking activities of daily living were designed to evaluate the interface: (1) the expression of untrained gestures, (2) the decoding of the amount of single-finger energy, and (3) real-time control.. (1) Participants completed the untrained hand movements (100/100,p< 0.0001). (2) The interface performed better than chance in the experiment where participants pricked balloons with a needle tip (779/1000,p< 0.0001). (3) In the experiment where participants punched a hole in the plasticine on the balloon, the success rate was over 95% (97.67 ± 5.04%,p< 0.01).. The model can achieve continuous hand actions with speed or force information by training small amounts of sEMG data, which reduces learning task complexity.
目前，基于 sEMG 的手势识别需要大量的训练数据；否则，它仅限于少数几个手势。本文提出了一种新的动态能量模型，通过训练少量 sEMG 数据来解码连续的手部动作。前臂肌肉的激活可以使相应的手指以运动趋势移动或处于运动状态。运动中的手指储存动能，有运动趋势的手指储存势能。由于实际运动中五指的自适应耦合机制，每个手指的动能和势能会动态分配。同时，在一定的肌肉激活下，两种能量的总和保持不变。我们将具有相同加速度方向的手指运动视为能量模式相同，并将手运动分为十种能量模式。采用独立成分分析和机器学习方法对 sEMG 信号与能量模式之间的关系进行建模，并以能量形式自适应地表示手势。该理论模仿了实际任务中的自适应机制。因此，招募了 10 名健康受试者，并设计了三个模拟日常生活活动的实验来评估该接口：（1）未训练手势的表达，（2）单指能量的解码，（3）实时控制。（1）参与者完成了未训练的手部运动（100/100，p<0.0001）。（2）在参与者用针尖刺破气球的实验中，该接口的表现优于随机（779/1000，p<0.0001）。（3）在参与者在气球上的橡皮泥上打孔的实验中，成功率超过 95%（97.67±5.04%，p<0.01）。该模型可以通过训练少量 sEMG 数据实现具有速度或力信息的连续手部动作，从而降低学习任务的复杂性。
