Volitional Control of Lower-limb Prosthesis with Vision-assisted Environmental Awareness

Abstract

Early and reliable prediction of user’s intention to change locomotion mode or speed is critical for a smooth and natural lower limb prosthesis. Meanwhile, incorporation of explicit environmental feedback can facilitate context aware intelligent prosthesis which allows seamless operation in a variety of gait demands. This dissertation introduces environmental awareness through computer vision and enables early and accurate prediction of intention to start, stop or change speeds while walking. Electromyography (EMG), Electroencephalography (EEG), Inertial Measurement Unit (IMU), and Ground Reaction Force (GRF) sensors were used to predict intention to start, stop or increase walking speed. Furthermore, it was investigated whether external emotional music stimuli could enhance the predictive capability of intention prediction methodologies. Application of advanced machine learning and signal processing techniques on pre-movement EEG resulted in an intention prediction system with low latency, high sensitivity and low false positive detection. Affective analysis of EEG suggested that happy music stimuli significantly (