Multi-modality — EMG and Visual Based Hands-Free Control of an Intelligent Wheelchair

Abstract

Abstract. This paper presents a novel human machine interface for people with severe disabilities to control an electric powered wheelchair using face movements. Five face movements including jaw clenching and eye closing movements are identified by extracting movement features from both forehead Electromyography (EMG) signal and facial image information. A real-world indoor environment is setup for evaluating the performance of the new control method. Five subjects participated in the experiment to follow designated routes on a map using the new control method, as well as a traditional joystick control respectively. Comparison of two control methods are made in terms of easiness of control, time duration, wheelchair trajectory and error command rate etc. Wheelchair trajectory and time consumption are recorded for each task and results show the new control method are comparable to a joystick control and can be used as a hands-free control interface for disabled and elderly users.

Fusing Emg and Visual Data for Hands-Free Control of an Intelligent Wheelchair

“…The detailed description of the strategy can be found in our previous paper. 12 As a learning machine for processing complex multi-channel EMG or EEG data, SVM is shown to outperform other classifiers and gives a swift and reliable result in contrast with real-time multi-channel input requirements. As shown in Fig.…”