Voice Control System for Upper Limb Rehabilitation Robots using Machine Learning

摘要

Motor dysfunction is a common outcome of strokes, spinal cord injuries, head injuries and multiple sclerosis. Their occupational therapies bring a lot of difficulties as they are labor-intensive, time-consuming and expensive. Robots play a major role in rehabilitation by replacing traditional therapies and offer ideal customized therapies. Further, wearable robots such as exoskeletons make the rehabilitation process simpler. Most of the existing rehabilitation robots use joysticks as their control method, which requires hand movement form the patient or a helper. However, introducing voice control mechanisms to these rehabilitation robots would raise the independence of individuals in robot controlling. This paper introduces a model to control robotic devices using voice commands which is based on Recurrent Neural Networks (RNN). Here, Long Short-Term Memory (LSTM) machine learning technique is implemented on “RehaBot” exoskeleton robot which is used for upper-limb rehabilitation with two-Degree of Freedom (2-DOF). Ten different voice commands are used to design the voice control system contemplating the movements of the upper limb. As the voice commands could be affected by the background noise, gender and data input source (microphone), their effects on voice commands are analyzed and discussed here.