Predicting future sedentary behaviour using wearable and mobile devices |
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| Institution: | 1. School of Management, Wenzhou Business College, Wenzhou, China;2. School of Finance and Trade, Wenzhou Business College, Wenzhou, China |
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| Abstract: | Sedentarism is a common problem that can affect human health and wellbeing. Predicting sedentary behaviour is an emerging area that can benefit from data collected from sensors available in ubiquitous devices, such as wearables and smartphones. In this paper, we present an approach aiming at predicting the sedentary behaviour of a user from data collected from sensors installed in wearable/mobile devices. We compare personal and impersonal models using a real-life dataset consisting of sensing data of 48 users during 10 weeks. We found that impersonal models using Deep Neural Networks were able to accurately predict the subject’s future sedentary behaviour. |
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| Keywords: | Sedentary behaviour prediction Machine learning User modelling Wearable and mobile devices |
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