In Japan, the demand for nursing homes is increasing owing to the rapid aging of the population, and the shortage of caregivers has become a serious problem. This problem has been recognized as a social issue because it increases the workload per caregiver. In response, we have been developing a platform that can easily collect data on nursing care activities to reduce the workload on the basis of the idea that the mental state (i.e., stress) of caregivers, which changes during care activities, might markedly affect their work efficiency. The objective of this study was to obtain new knowledge for reducing the workload of caregivers by visualizing and analyzing their stress. Specifically, we asked caregivers to wear the device, from which we obtained the objective stress indicators of R-R interval (RRI) and low frequency (LF)/high frequency (HF) ratio. We also obtained subjective stress indicators from questionnaires administered before the start of the experiment, before work, during breaks, and after work. To confirm that the stress level of caregivers depends on their care activity, work shift, and workday, we conducted an empirical experiment in an actual nursing home. We distributed devices and applications to five caregivers, obtained measurement data for a total of 28 days, and analyzed and visualized the changes in psychological states related to care activities, work shift, and workdays. As a result, we were able to collect data on the psychological state for 5 to 15 days per caregiver. Furthermore, the analysis of the data showed that, even though objective stress indicators and subjective evaluations did not necessarily coincide, stress tended to increase with specific care activities, work shift, and workdays. In addition, the subjective and objective stress indicators may change depending on the personality of the caregivers themselves.
In Japan, accidents involving the falling of elderly people are increasingly becoming a problem. To solve this problem, walking training is effective for preventing falls of elderly people. In this study, a walking training system was developed in which high-performance shoes are used to improve the efficiency of walking training. The high-performance shoes have three functions: 1) measurement of plantar pressure using changes in the inner pressure of the insole, 2) leg movement measurement using a six-axis motion sensor, and 3) applying stimulus to the sole of the foot by changing the shape of the insole. A unique rubber element was developed for these functions. Furthermore, a system to predict the behavior of patients during walking training was developed. Based on experimental results, four types of behavior of patients during walking training were predicted. Moreover, leave-one-person-out cross validation was performed by the random forest (RF) machine-learning algorithm, and the F-measure was calculated. As a result, the four types of behavior were classified with an F-measure of 78.6%.
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