Predicting Daily Physical Activity Level for Older Adults Using Wearable Activity Trackers

Abstract

In recent years, there is an increasing trend towards using wearable activity trackers to help monitor and track physical activities (PA) for older adults, with the purpose of motivating regular PA for better health. However, existing activity trackers are frequently abandoned within a short period of time. One of the major reasons is that they do not differentiate individual PA habits and provide PA recommendations based on a unified standard, which may lead to unrealistic suggestions and thus cause frustrations. In order to motivate long-term use of activity trackers and promote PA progression in older adults, PA recommendations should adapt to the changes of an individual’s PA habits. As a step towards achieving this, we introduce in this paper an innovative multi-scale personalized LSTM model that can predict an individual’s daily PA level with satisfied accuracy. This model is verified through a series of experimental studies.