Hand Gesture Recognition With Acoustic Myography and Wavelet Scattering Transform

In the past decade, improving upper limb prostheses control methods with pattern recognition (PR) has been the focus of an extended amount of research. However, several challenges associated with the processing of the Electromyogram (EMG) signals still need to be tackled to enable widespread and clinical implementation of upper limb prostheses with PR. As a result, alternative modalities functioning as promising control signals have been proposed as source of control input rather than the surface EMG, such as Acoustic myography (AMG) and Force myography. In this paper, eight high sensitivity array microphones were utilized to acquire the AMG signals, with 8 custom-built 3D printed microphone housing developed for the purpose of this research. Twenty subjects were recruited for data collection in this paper with the hardware design developed specifically by our research team, making our database the largest open-access dataset in the AMG literature. We proposed a novel feature extraction (FE) method based on the Wavelet Scattering Transform (WST) to tackle the challenge of extracting the relevant information from AMG to classify 14 hand and finger movement classes. The WST is a translation-invariant non-linear signal representation that has a strong theoretical support as it maintains stability to time-warping deformations, while preserving a high degree of discriminability. The performance results showed that WST outperformed all state-of-the-art FE methods with an accuracy of 88% on average across 20 subjects when classified with Quadratic Discriminant Analysis (QDA) classifier for a large dataset of AMG signals. These results suggest that the AMG signals can be utilized as a reliable source of control, especially when the windows sizes and number of channels are carefully selected. The AMG dataset is available from the link
https://drive.google.com/drive/folders/1r0rBnrNG5c8qegffKUhAYYHQUT8cGVHD?usp=sharing
.