Development of a Human Machine Interface for Control of Robotic Wheelchair and Smart Environment*. Issue 19 (2015)

• Record Type: Journal Article
• Authors: Tello, Richard J.M.G.; Bissoli, Alexandre L.C.; Ferrara, Flavio; Müller, Sandra; Ferreira, Andre; Bastos-Filho, Teodiano F.
• Is Part Of: IFAC-PapersOnLine. Volume 48:Issue 19(2015)
• Page Start: 136
• Page End: 141
• Publication Date: 2015
• Subjects: SSVEP-BCI -- Robotic Wheelchair -- EEG -- sEMG -- EOG -- Smart Environment; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2015.12.023
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: In this work, we address the problem of integrating a robotic wheelchair into a smart environment. This approach allows people with disabilities to control home appliances of the environment using a Human Computer Interface (HCI) based on different biological signals. The home appliances includes TV, radio, lights/lamp and fan. Three control paradigms using surface Electromyography (sEMG), Electrooculography (EOG) and Electroencephalography (EEG) signals were used. These signals are captured through a biosignal acquisition system. Three sub-paradigms for sEMG/EOG analyzes were defined: moving eyes horizontally (left/right), raising brow and prolonged clench. On the other hand, the navigation of the wheelchair is executed through an Steady-State Visually Evoked Potentials (SSVEP)-BCI. Each stage of our proposed system showed a good performance for most subjects. Therefore, volunteers were recruited to participate of the study and were distributed in two groups (subjects for home appliances and subjects for SSVEP-BCI). The average accuracy for prolonged clench approach was of 95%, the raising brow was 85% and moving eyes achieved 93%. Multivariate Synchronization Index (MSI) was used for feature extraction from EEG signals. The flickering frequencies were 8.0 Hz (top), 11.0 Hz (right), 13.0 Hz (bottom) and 15.0 Hz (left). Results from this approach showed that classification varies in the range of 45-77% among subjects using window length of 1 s.