Mining Life Patterns from Wearable Sensors Data for Elderly Anomaly Detection

Abstract

Life patterns can represent an individual's life style and they can help people understand their daily behavior as well as the regular habits. Discovery of life patterns has a manifold of application scenarios, which can be embedded into location based recommender systems, precise advertising, computer-aided scheduling, kind care/alert systems. In this paper, we propose an approach for life style mining with applications on elderly anomaly detection. Although there existed already studies for discovering life styles, they were mostly based on traditional single-sensor environment. Consequently, it cannot completely represent an individual's lifestyle due to the lack of sufficient information and related applications like anomaly detection cannot reach high accuracy. To deal with above-mentioned problems, our approach can mine an individual's life pattern from wearable-devices-based environment with multiple sensors. When the life patterns are applied to elderly anomaly detection, multiple-sensors-based elderly's conditions, such as physical condition and locations, are taken into considerations at the same time. For experimental evaluations, we design a data simulator to generate sensors data of elderly's daily life, based on which the effectiveness of our proposed framework is verified.