Abstract
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.
Highlights
O VER the last few decades, surface electromyography has been widely used as the standard myographic modality for recording and decoding human motor intention and muscle activity
We introduce a new method of augmenting the design of wearable MMG systems focusing on enhancing the signal quality and discriminative power based on the variation of the distribution of normal force/skin contact
While the literature on MMG signal capture is becoming more common, studies have relied on the use of straps, tape, or non-conventional methods, which disregard the effect that contact force has on the quality and discriminative power of the MMG signal
Summary
O VER the last few decades, surface electromyography (sEMG) has been widely used as the standard myographic modality for recording and decoding human motor intention and muscle activity. This modality has been used to classify a high number of actions (such as multiple gestures) while maintaining a good level of accuracy [1]. Despite the fact that by the use of sEMG a high number of gestures can be classified with a high level of accuracy, there are some significant challenges that exist with the practical uses of this modality, such as (a) variations due to skin conditions and impedance (for example due to sweating on the sensor site), (b) high sensitivity to sensor positioning, (c) the need for clean electrical electrode-skin contact surface, and (d) expensive hardware requirements for electromagnetic noise cancelation and sophisticated signal amplification [5]–[7]. One result is that many of the current prosthetic devices work only based on sequential state machine rules, which
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