Jointly Prediction of Activities, Locations, and Starting Times for Isolated Elderly People

Restrictive public health measures such as isolation and quarantine have been used to reduce the pandemic virus's transmission. With no proper treatment, older adults have been specifically advised to stay home, given their vulnerability to COVID-19. This pandemic has created an increasing need for new and innovative assistive technologies capable of easing the lives of people with special needs. Smart home systems have become widely popular in providing such assistive services to isolated older adults. These systems can provide better services to assist older people if it anticipates what activities inhabitants will perform ahead of time. For example, a smart home can prompt inhabitants to initiate essential activities like taking medicine using activity prediction. This paper proposes a multi-task activity prediction system that jointly predicts labels, locations, and starting times of future activities. The observed sequence of previous activities characterizes future activities. We use body activity information from wearable sensors and motion information from passive environmental sensors to sense activities of daily living of older adults. The activity prediction system consists of recurrent neural networks to capture temporal dependencies. This work also carries out several experiments on collected and existing real datasets to evaluate the system's performance.

Automated summary

Restrictive public health measures like isolation and quarantine are used to reduce the transmission of the pandemic virus. Due to their vulnerability to COVID-19, older adults are specifically advised to stay at home. The increasing demand for assistive technologies for people with special needs during the pandemic has led to the popularity of smart home systems, which can provide better services through activity prediction. This paper proposes a multi-task activity prediction system using wearable sensors and environmental sensors to sense daily activities of older adults, and evaluates its performance through experiments on real datasets.