Abstract
Brain-computer interface (BCI) technology shows potential for application to motor rehabilitation therapies that use neural plasticity to restore motor function and improve quality of life of stroke survivors. However, it is often difficult for BCI systems to provide the variety of control commands necessary for multi-task real-time control of soft robot naturally. In this study, a novel multimodal human-machine interface system (mHMI) is developed using combinations of electrooculography (EOG), electroencephalography (EEG), and electromyogram (EMG) to generate numerous control instructions. Moreover, we also explore subject acceptance of an affordable wearable soft robot to move basic hand actions during robot-assisted movement. Six healthy subjects separately perform left and right hand motor imagery, looking-left and looking-right eye movements, and different hand gestures in different modes to control a soft robot in a variety of actions. The results indicate that the number of mHMI control instructions is significantly greater than achievable with any individual mode. Furthermore, the mHMI can achieve an average classification accuracy of 93.83% with the average information transfer rate of 47.41 bits/min, which is entirely equivalent to a control speed of 17 actions per minute. The study is expected to construct a more user-friendly mHMI for real-time control of soft robot to help healthy or disabled persons perform basic hand movements in friendly and convenient way.
Highlights
Stroke is ranked as the third most common cause of disability worldwide and seriously affects the quality of life of survivors in terms of their daily functioning (Lim et al, 2012)
Our system translates three signals into 11 classes of control commands to control a soft robot with an accuracy of 93.83% which is outperformed the above mentioned system and its mean information transfer ratio (ITR) is 47.41 bits/min, and it can be used to assist both healthy and disabled persons with high efficiency in classification performance compared with existing multimodal human-machine interface system (mHMI)
We have proposed a task-oriented approach to assistance and motor function training with the activities of daily living using the mHMI with robust real-time control of a soft robot through motor imagery (MI), eye movements, and hand gestures
Summary
Stroke is ranked as the third most common cause of disability worldwide and seriously affects the quality of life of survivors in terms of their daily functioning (Lim et al, 2012). A rehabilitation program involving repetitive movements of the activities of daily living can allow stroke survivors. In the traditional therapeutic approach, physical therapists teach stroke survivors how to guide their movements with the aim of regaining basic physical skills. This approach is highly labor-intensive, inefficient, and requires a good deal of physical effort on the part of patients, who may sometimes refuse to actively cooperate with the regime. Patients may need to be hospitalized for some of their rehabilitation Another problem is that many physical therapists may not have received the necessary training to prepare them to administer such stroke rehabilitation programs (Curtis and Martin, 1993). The above factors have severely restricted the clinical effectiveness of rehabilitation training
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