Research status of elderly-care robots and safe human-robot interaction methods.

Faced with the increasingly severe global aging population with fewer children, the research, development, and application of elderly-care robots are expected to provide some technical means to solve the problems of elderly care, disability and semi-disability nursing, and rehabilitation. Elderly-care robots involve biomechanics, computer science, automatic control, ethics, and other fields of knowledge, which is one of the most challenging and most concerned research fields of robotics. Unlike other robots, elderly-care robots work for the frail elderly. There is information exchange and energy exchange between people and robots, and the safe human-robot interaction methods are the research core and key technology. The states of the art of elderly-care robots and their various nursing modes and safe interaction methods are introduced and discussed in this paper. To conclude, considering the disparity between current elderly care robots and their anticipated objectives, we offer a comprehensive overview of the critical technologies and research trends that impact and enhance the feasibility and acceptance of elderly care robots. These areas encompass the collaborative assistance of diverse assistive robots, the establishment of a novel smart home care model for elderly individuals using sensor networks, the optimization of robot design for improved flexibility, and the enhancement of robot acceptability.

Control strategy for a myoelectric hand: measuring acceptable time delay in human intention discrimination.

In order to enhance controllability of a myoelectric hand, we focus on a gap between the time when a human intends to move a myoelectric hand and the time when the hand actually moves (i.e., time delay). Normally, the myoelectric hand users dislike the time delay because it makes them feel uncomfortable. However, the users learn the time delay within some time ranges and, eventually, get feel comfortable to operate the hand. Thus, we assume, if we reveal the acceptable delay time (i.e., the time the users accept the gap with their learning ability), we can provide more time in a human intention discrimination process, and enhance its success rate. Therefore, we developed a mobile myoelectric hand system with an embedded linux computer, and conducted a ball catch experiment: we investigate the acceptable delay time by adding the delay time (i.e., 120[ms], 170[ms], 220[ms], 270[ms], 320[ms]) into the human intention discrimination process. As a result, we confirmed that the max accept delay time was approximately 170 [ms] that achieves 61% success rate.