Masahide Nakamura - Improving Health and Quality of Life in One-Person Households Using IoT and Machine Learning

Worldwide, there has been an increase in the number of individuals that live alone in one-person households (OPHs). Compared to those living with family,
people in OPHs easily lose control of life rhythm. Given that the disturbance of life rhythm leads to chronic disease, they have a higher risk of
illness. As such, there is an urgent demand for assistive technology that allows people in OPHs to enjoy healthy, high-quality lives.
For decades, there has been significant research and development of smart systems to assist people at home. However, there are still limitations on the practical use of these systems in actual OPHs. More specifically, they are often too intrusive to the lifestyle of users or home objects. In addition, they are often expensive to deploy and maintain. Furthermore, these systems are unable to evaluate the quality of life rhythm. As a result, it is difficult for individual users to determine what their healthy life rhythms should be, and how to improve their current situation.

The goal of research is to develop a new smart system for OPHs that can minimize intrusiveness and cost, while also facilitating the assessment of life
rhythms of individual users. The new system collects user position and environmental data inside the house in a non-intrusive way, using affordable IoT devices. From this data, the system then recognizes the daily activities of the user. Based on these activities, eventually, the system can quantitatively
evaluate the user’s life rhythms and provide practical advice for maintaining a healthy life.