Activity prediction for improving well-being of both the elderly and caregivers

Abstract

The issue of ageing population is gaining significant attention across the world, while the caregivers’ psychological burden caused by a variety of geriatric symptoms is often overlooked. Efficient collaboration between the elderly and caregivers has great potential to relieve the caregivers’ psychological burden and improve the caregiving quality. For instance, activity prediction can provide a promising approach to cultivate this efficient collaboration. Given the ability to predict the elderly patients’ activity and its timing, caregivers can provide timely and appropriate care, which not only can relieve caregiving stress for professional or family caregivers, but also can reduce the unwanted conflicts between both parties. In this paper, we train an activity predictor by integrating the activity temporal information into the Long Short-Term Memory (LSTM) networks. The approach leads to significant improvements in the prediction accuracy both in the next activity and its precise occurrence time.