Hand Movement Recognition with Long Short-Term Memory based Pattern Recognition of Acoustic Myography signals

Upper limb prosthesis control with pattern recognition (PR)- based on surface Electromyogram (EMG) signals has been heavily investigated in the literature. However, challenges related to signal non-stationarity and its impact by changing force levels, limb positions and many other factors all lead to poor system stability, prevent the widespread adoption of this technology. In this study, we propose an alternative modality based on Acoustic Myography (AMG), a recording of muscle vibrations at low frequencies, as a control signal to decipher the intended hand movements. A custom-built AMG armband, consisting of 4 microphones, has been developed in this study and evaluated on intact-limbed subjects performing six classes of hand and finger movements. Time domain and auto-regression (TD-AR) features, in addition to Bi long-short term memory (BiLSTM) deep learning classifier were utilized to perform the classification. An average classification accuracy of 85% was obtained, which shows the potential of using the developed AMG armband, with PR system for prostheses control.