Towards providing efficient human-robot interaction, surface electromyogram (EMG) signals have been widely adopted for the identification of different limb movement intentions. Since the available EMG signal sensors are highly susceptible to external interferences such as electromagnetic artifacts and muscle fatigues, the quality of EMG recordings would be mostly corrupted, which may decay the performance of EMG-based control systems. Given the fact that the muscle shape changes (MSC) would be different when doing various limb movements, the MSC signal would be nonsensitive to electromagnetic artifacts and muscle fatigues and maybe promising for movement intention recognition. In this study, a novel nanogold flexible and stretchable sensor was developed for the acquisition of MSC signals utilized for decoding multiple classes of limb movement intents. More precisely, four sensors were used to measure the MSC signals from the right forearm of each subject when they performed seven classes of movements. Also, six different features were extracted from the measured MSC signals, and a linear discriminant analysis- (LDA-) based classifier was built for movement classification tasks. The experimental results showed that using MSC signals could achieve an average recognition rate of about 96.06 ± 1.84% by properly placing the four flexible and stretchable sensors on the forearm. Additionally, when the MSC sampling rate was greater than 100 Hz and the analysis window length was greater than 20 ms, the movement recognition accuracy would be only slightly increased. These pilot results suggest that the MSC-based method should be feasible in movement identifications for human-robot interaction, and at the same time, they provide a systematic reference for the use of the flexible and stretchable sensors in human-robot interaction systems.

中文翻译：

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基于人机交互作用的肌肉形状变化信号识别上肢运动。

为了提供有效的人机交互，表面肌电图（EMG）信号已被广泛用于识别不同肢体运动意图。由于可用的EMG信号传感器极易受到外部干扰（例如电磁伪影和肌肉疲劳）的影响，因此EMG记录的质量通常会受到破坏，这可能会使基于EMG的控制系统的性能下降。考虑到在进行各种肢体运动时肌肉形状变化（MSC）会有所不同的事实，因此MSC信号对电磁伪影和肌肉疲劳不敏感，可能有望识别运动意图。在这个研究中，开发了一种新颖的纳米金柔性可拉伸传感器，用于采集用于解码多种类别肢体运动意图的MSC信号。更准确地说，当四个传感器执行七类运动时，使用四个传感器来测量来自每个受试者右前臂的MSC信号。同样，从测量的MSC信号中提取了六个不同的特征，并建立了基于线性判别分析（LDA）的分类器来进行运动分类任务。实验结果表明，通过在前臂上正确放置四个柔性和可伸展的传感器，使用MSC信号可实现约96.06±1.84％的平均识别率。此外，当MSC采样率大于100 Hz并且分析窗口长度大于20 ms时，运动识别精度只会略微提高。这些试验结果表明，基于MSC的方法在人机交互的运动识别中应该是可行的，同时，它们为在人机交互系统中使用可伸缩传感器提供了系统的参考。