Wearable MMG-Plus-One Armband: Evaluation of Normal Force on Mechanomyography (MMG) to Enhance Human-Machine Interfacing

• Published in: IEEE transactions on neural systems and rehabilitation engineering 2021, Vol.29, p.196-205
• Main Authors: Castillo, C. Sebastian Mancero, Wilson, Samuel, Vaidyanathan, Ravi, Atashzar, S. Farokh
• Format: Article
• Language: English
• Subjects: Classification; Contact force; Contacts; Electromyography; Embedding; Feature extraction; Force; Forearm; Human performance; Human-machine interfacing (HMI); Interfaces; Man-machine interfaces; mechanomyography (MMG); Microphones; motor intention prediction (MIP); Muscles; neurorobotics; Pressure; Prostheses; Sensitivity; Sensors; Skin; smart wearable technologies (SMT); Support vector machines; Viscoelasticity
• ISSN: 1534-4320; 1558-0210
• DOI: 10.1109/TNSRE.2020.3043368

In this paper, we introduce a new mode of mechanomyography (MMG) signal capture for enhancing the performance of human-machine interfaces (HMIs) through modulation of normal pressure at the sensor location. Utilizing this novel approach, increased MMG signal resolution is enabled by a tunable degree of freedom normal to the sensor-skin contact area. We detail the mechatronic design, experimental validation, and user study of an armband with embedded acoustic sensors demonstrating this capacity. The design is motivated by the nonlinear viscoelasticity of the tissue, which increases with the normal surface pressure. This, in theory, results in higher conductivity of mechanical waves and hypothetically allows to interface with deeper muscle; thus, enhancing the discriminative information context of the signal space. Ten subjects (seven able-bodied and three trans-radial amputees) participated in a study consisting of the classification of hand gestures through MMG while increasing levels of contact force were administered. Four MMG channels were positioned around the forearm and placed over the flexor carpi radialis, brachioradialis, extensor digitorum communis, and flexor carpi ulnaris muscles. A total of 852 spectrotemporal features were extracted (213 features per each channel) and passed through a Neighborhood Component Analysis (NCA) technique to select the most informative neurophysiological subspace of the features for classification. A linear support vector machine (SVM) then classified the intended motion of the user. The results indicate that increasing the normal force level between the MMG sensor and the skin can improve the discriminative power of the classifier, and the corresponding pattern can be user-specific. These results have significant implications enabling embedding MMG sensors in sockets for prosthetic limb control and HMI.