MMG-based classification of muscle activity for prosthesis control

Abstract

We have previously proposed the use of "muscle sounds" or mechanomyography (MMG) as a reliable alternative measure of muscle activity with the main objective of facilitating the use of more comfortable and functional soft silicone sockets with below-elbow externally powered prosthesis. This work describes an integrated strategy where data and sensor fusion algorithms are combined to provide MMG-based detection, estimation and classification of muscle activity. The proposed strategy represents the first ever attempt to generate multiple output signals for practical prosthesis control using a MMG multisensor array embedded distally within a silicon soft socket. This multisensor fusion strategy consists of two stages. The first is the detection stage which determines the presence or absence of muscle contractions in the acquired signals. Upon detection of a contraction, the second stage, that of classification, specifies the nature of the contraction and determines the corresponding control output. Tests with real amputees indicate that with the simple detection and classification algorithms proposed, MMG is indeed comparable to and may exceed EMG functionally.