A Pilot Study of Mechanomyography-Based Hand Movements Recognition Emphasizing on the Influence of Fabrics Between Sensor and Skin

Abstract

Multi-channel mechanomyography (MMG) signals were acquired from the forearm when the subjects were performing eight classes of hand movements related to rehabilitation training. Ten time domain (TD) features and wavelet packet node energy (WPNE) features were extracted from each channel of MMG, and the hand movements were classified by support vector machine (SVM), extreme learning machine (ELM), linear discriminant analysis (LDA) and [Formula: see text]-nearest neighborhood (KNN) and the classifying results of three methods of collecting MMG (sensors directly on skin, sensors on cotton fabric and sensors on acrylic fiber) were compared. When all TD features were selected and SVM was adopted as the classifier, the total recognition rates of hand movements were 94.0%, 93.9% and 93.6%, respectively, of three collection methods. Using ELM can obtain similar results as SVM, with the recognition rates of 94.3%, 94.3% and 94.1%, respectively, better than using LDA (88.5%, 88.6% and 88.0%) or KNN (88.9%, 89.4% and 89.0%). For each algorithm, using TD features can acquire the highest recognition rates. Once the feature set and the classifier were selected, the total recognition rates were almost equally among three collection methods (especially for some feature sets, the differences are smaller than 1%). The results confirmed that satisfactory effects could be acquired even when the MMG was collected from sensors on fabrics with specific material, thus indicating that MMG has a unique potential value for developing wearable devices.

Mechanomyography Signal Pattern Recognition of Knee and Ankle Movements Using Swarm Intelligence Algorithm-Based Feature Selection Methods

“…In contrast to sEMG, mechanomyography (MMG) is a low-frequency mechanical signal generated from muscle’s lateral oscillation and has application value in human machine interaction technology. Compared with sEMG, MMG is more convenient for collecting since it is unaffected by sweating and can be collected through specific cloth material [ 8 , 9 ]. Yu et al [ 10 ] collected 4-channel MMG from the thigh and adopted a hidden Markov model to achieve recognition of gait movements.…”