A comparative optimization procedure to evaluate pattern recognition algorithms on hannes prosthesis

Stability and repeatability of Pattern Recognition (PR) myoelectric control for upper limb prosthetic devices remain unresolved challenges in multi-DoFs systems. In this study, we tested several state-of-the-art classifiers to compare their offline performance in different configurations. Parameters such as realization costs, overall encumbrance, and algorithm complexity were considered for the analysis. The results showed that NLR performed comparably to LDA but with fewer EMG sensors. This study demonstrated that sensor numbers can be reduced to a few units for various algorithms, with NLR being the most tolerant due to its non-linearity. In conclusion, NLR can effectively control the multi-DoFs Hannes system in real-time, offering similar performances to other algorithms while reducing the system's complexity and encumbrance as compared to LDA. It also offers improved tolerance to reduced available information and lower implementation costs.