Host: The Japan Society of Mechanical Engineers
Name : [in Japanese]
Date : May 27, 2020 - May 30, 2020
Our previous study proposed a nursing assistant system using deep learning and robot technology for patient safety. However, there are characteristic risks associated with artificial intelligence (AI) in applications such as nursing assistant system. In this paper, we present safety applications related to AI in fields where humans and robots coexist, especially when applying deep learning to the control of assistive autonomous mobile robots. First, we systematically extracted risks, risk manifestation phases, risk factors, and cases based on the results of patient simulation experiments performed on the campus. Next, we proposed risk reduction measures from the viewpoint of safety control. Finally, as a case study, we quantitatively evaluated the effects of risk reduction measures and confirmed its effectiveness of reducing the error rate during learning by about 30%. Furthermore, this method can be applied not only to hospital facilities but also to residential areas including nursing homes where the most incidents occur.
