Artificial Intelligence for Wearable Devices : With the Case Study of the Control Interface for a Myoelectric Hand Prosthesis

Artificial neural networks have popularized problem-solving with algorithms, which rely on input-output examples instead of causal models to operate. The deep learning (DL) variety has notably been shown to efficiently solve complex classification and prediction problems when large training sets and big computational resources are available. However, these constraints are not always met in science and engineering, where adapted DL solutions are necessary. A case in point is hand prostheses where the training data is scarce, and the computational resources limited. The tutorial review explores these challenges when applying AI to such devices. Using the example of a surface electromyography-driven hand prosthesis control system, the sensing, signal processing, and machine learning pipelines of a wearable system are detailed.