ABSTRACT As older adults get older, their bodies deteriorate. A large proportion of the elderly population experiences decreased attention and memory failure. Through the intervention of music, the memory of the elderly can be improved to some extent, which is of great help to their physical and mental health. In this paper, a visual music therapy rehabilitation system was designed in which the cloud processing module performs physiological signal normalization, feature extraction of heart rate variability patterns, neural network‐based emotional state classification, and data storage for adaptive visual music intervention. When the system detected a change in old people's emotions, the subsystem judged the current model of people's expressions and transmitted the results to the server subsystem, which normalized the data and afterward sent it to the visualization subsystem. The visualization subsystem loaded the corresponding music file based on the server's data and generated a dynamic video to relieve the old people's mental health condition. In this paper, a systematic review was conducted of the three aspects of physical and mental debilitation and social debilitation in the elderly population. Experimental results showed that after 10 weeks of treatment, 34.7% of the elderly were in the pre‐debilitation state and 22% were in a robust state in the visual music therapy rehabilitation system group. This observation period represents a defined intervention stage used to examine the short‐ to medium‐term emotional response of elderly participants during continuous visual music therapy interaction. In this paper, the effects of performing music therapy on the debilitated elderly were observed, which contributes to the development of intelligent medical information systems for emotion‐aware rehabilitation in elderly populations.
Zhang et al. (Wed,) studied this question.