Spike Decoders for Regressing Arm Angle for Autonomous Stroke Rehabilitation System

Low latency inference is a highly desirable metric to deploy any autonomous control systems. To aid in this field, we propose and investigate multiple spike decoders for Spiking Neural Networks (SNN) to perform forearm angle prediction by taking Surface Electromyography (sEMG) signal as input, as part of an autonomous post stroke motor rehabilitation system. The proposed regression decoders are compared with the existing implementations and conventional ANNs in terms of accuracy and relative efficiency. The models introduced here exhibited an accuracy of 90% compared to the baseline’s 82% while also offering 10× to 12× of higher energy efficiency and EDP gains 24×. These models also exhibited relatively lower loss in performance with varied input data parameters compared to the baselines. These SNNs are a low latency alternatives for implementing complex regression models like the reference rehabilitation system with potential usability in other Reinforcement Learning (RL) problems.