Mechanomyography Assisted Myoeletric Sensing for Upper-Extremity Prostheses: A Hybrid Approach

• Published in: IEEE sensors journal Vol. 17; no. 10; pp. 3100 - 3108
• Main Authors: Guo, Weichao, Sheng, Xinjun, Liu, Honghai, Zhu, Xiangyang
• Format: Journal Article
• Language: English
• Published: New York IEEE 15-05-2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accelerometers; Electromyography; fault sensor; Hybrid systems; mechanomyography; Microphones; Muscles; pattern recognition; sensor fusion; Sensors; Surface electromyography; Time-frequency analysis; Vibrations
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2017.2679806

The myoelectric upper-limb prosthetic manipulation is inherently limited by the unreliable sensor-skin interface. This paper presents a hybrid approach to overcome the limitation of electromyography (EMG) through mechanomyography (MMG) assisted myoelectric sensing. An integrated hybrid sensor system was developed for simultaneous EMG and MMG measurement. The hybrid system formed a platform to capture muscular activations in different frequencies. To evaluate the effectiveness of hybrid EMG-MMG sensing, hand motion experiments have been carried out on seven able-bodied and two transradial amputee subjects. It convincingly demonstrated, a significantly (p <; 0.01) improved classification accuracy (CA). Furthermore, the CA was compensated by 8.7% ~ 33.7% in the presence of 2 ~ 3 fault EMG channels. These results suggest that MMG assisted myoelectric sensing can improve the control performance and robustness. It has great potential to promote the clinical application of multi-functional prosthetic hand with hybrid EMG-MMG sensor system.