Key Laboratory of Ministry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China.
School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China.
Sensors (Basel). 2020 Aug 24;20(17):4775. doi: 10.3390/s20174775.
To improve the reliability and safety of myoelectric prosthetic control, many researchers tend to use multi-modal signals. The combination of electromyography (EMG) and forcemyography (FMG) has been proved to be a practical choice. However, an integrative and compact design of this hybrid sensor is lacking. This paper presents a novel modular EMG-FMG sensor; the sensing module has a novel design that consists of floating electrodes, which act as the sensing probe of both the EMG and FMG. This design improves the integration of the sensor. The whole system contains one data acquisition unit and eight identical sensor modules. Experiments were conducted to evaluate the performance of the sensor system. The results show that the EMG and FMG signals have good consistency under standard conditions; the FMG signal shows a better and more robust performance than the EMG. The average accuracy is 99.07% while using both the EMG and FMG signals for recognition of six hand gestures under standard conditions. Even with two layers of gauze isolated between the sensor and the skin, the average accuracy reaches 90.9% while using only the EMG signal; if we use both the EMG and FMG signals for classification, the average accuracy is 99.42%.
为了提高肌电假肢控制的可靠性和安全性,许多研究人员倾向于使用多模态信号。肌电图(EMG)和力肌电图(FMG)的结合已被证明是一种实用的选择。然而,这种混合传感器的综合和紧凑设计还很缺乏。本文提出了一种新型的模块化 EMG-FMG 传感器;传感模块具有新颖的设计,由浮置电极组成,这些电极作为 EMG 和 FMG 的传感探头。这种设计提高了传感器的集成度。整个系统包含一个数据采集单元和八个相同的传感器模块。进行了实验来评估传感器系统的性能。结果表明,在标准条件下,EMG 和 FMG 信号具有良好的一致性;FMG 信号的性能优于 EMG,且更稳健。在标准条件下,使用 EMG 和 FMG 信号识别六种手势的平均准确率为 99.07%。即使在传感器和皮肤之间隔离两层纱布,仅使用 EMG 信号的平均准确率也达到 90.9%;如果同时使用 EMG 和 FMG 信号进行分类,平均准确率为 99.42%。