Editorial: Biomechatronics: Harmonizing Mechatronic Systems With Human Beings

• Published in: Frontiers in neuroscience Vol. 12; p. 768
• Main Authors: Zhang, Dingguo, Dubey, Venketesh N., Yu, Wenwei, Low, Kin Huat
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 25.10.2018; Frontiers Media S.A
• Subjects: biomechatronics; Brain research; brain-machine interface; Conflicts of interest; E-books; Editorials; EEG; Elbow; Electroencephalography; Electromyography; Exoskeleton; Gait; Gravity; human-machine interface; Interfaces; Kinematics; Learning algorithms; Motor skill learning; Neural networks; Neuroscience; Prostheses; prosthesis; Rehabilitation; rehabilitation robot; Robotics; Sensors; Singapore; electroencephalography; biomechatronics; electromyography; prosthesis; exoskeleton; brain-machine interface; human-machine interface; rehabilitation robot
• ISSN: 1662-453X; 1662-4548; 1662-453X
• DOI: 10.3389/fnins.2018.00768

There are a total of 19 papers looking into various aspects of human-machine interfaces (HMIs) using electromyography (EMG) and electroencephalography (EEG), tactile feedback, external devices such as exoskeletons and prosthetic devices for assistance and rehabilitation, novel techniques like machine learning and intelligent computation, and experimental evaluation or validation. Since all the above reported work employed EMG sensors, there must be a better way of acquiring stable signals. In order to improve challenge/skill ratio in a multi-modal interface for human-robot interaction, Rodriguez-Guerrero et al. have investigated simultaneous adaptation of game difficulty and haptic assistance through psychophysiological signals (heart rate, skin conductance level, and skin conductance response frequency) and performance feedback. The experiments were conducted on 8 healthy subjects performing a series of movements, including five actions and five hovering postures, and the results showed that the satisfactory data could be achieved with suitable precision for upper limb motion tasks without the need for platform based systems. Since motor learning is a