Navigation-synchronized multimodal control wheelchair from brain to alternative assistive technologies for persons
Dilok Puanhvuan1, Sarawin Khemmachotikun1, Pongsakorn Wechakarn1
1Department of Biomedical Engineering, Faculty of Engineering, Mahidol Unversity, 25/25, Putthamonthol 4 Road, Salaya, Putthamonthol, Nakhon Pathom 73170 Thailand.
Abstract:
Currently, electric wheelchairs are commonly used to improve mobility in disabled people. In severe cases, the user is unable to control the wheelchair by themselves because his/her motor functions are disabled. To restore mobility function, a brain-controlled wheelchair (BCW) would be a promising system that would allow the patient to control the wheelchair by their thoughts. P300 is a reliable brain electrical signal, a component of visual event-related potentials (ERPs), that could be used for interpreting user commands. This research aimed to propose a prototype BCW to allowed severe motor disabled patients to practically control a wheelchair for use in their home environment. The users were able to select from 9 possible destination commands in the automatic mode and from 4 directional commands (forward, backward, turn left and right) in the shared-control mode. These commands were selected via the designed P300 processing system. The wheelchair was steered to the desired location by the implemented navigation system. Safety of the user was ensured during wheelchair navigation due to the included obstacle detection and avoidance features. A combination of P300 and EOG was used as a hybrid BCW system. The user could fully operate the system such as enabling P300 detection system, mode shifting and stop/cancelation command by performing a different consecutive blinks to generate eye blinking patterns. The results revealed that the prototype BCW could be operated in either of the proposed modes. With the new design of the LED-based P300 stimulator, the average accuracies of the P300 detection algorithm in the shared-control and automatic modes were 95.31 and 83.42% with 3.09 and 3.79 bits/min, respectively. The P300 classification error was acceptable, as the user could cancel an incorrect command by blinking 2 times. Moreover, the proposed navigation system had a flexible design that could be interfaced with other assistive technologies. This research developed 3 alternative input modules: an eye tracker module and chin and hand controller modules. The user could select the most suitable assistive technology based on his/her level of disability. Other existing assistive technologies could also be connected to the proposed system in the future using the same protocol.
