Mining Life Patterns from Wearable Sensors Data for Elderly Anomaly Detection

Cheng Hung Tsai, Cheng Hao Chu, Sun Wei Liu, Sun Yuan Hsieh, Vincent S. Tseng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 locationbased recommender systems, precise advertising, computer-aided scheduling, and 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.

Original languageEnglish
Title of host publicationProceedings - 2017 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages66-71
Number of pages6
ISBN (Electronic)9781538642030
DOIs
Publication statusPublished - 2018 May 9
Event2017 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2017 - Taipei, Taiwan
Duration: 2017 Dec 12017 Dec 3

Publication series

NameProceedings - 2017 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2017

Other

Other2017 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2017
CountryTaiwan
CityTaipei
Period17-12-0117-12-03

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Human-Computer Interaction

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  • Cite this

    Tsai, C. H., Chu, C. H., Liu, S. W., Hsieh, S. Y., & Tseng, V. S. (2018). Mining Life Patterns from Wearable Sensors Data for Elderly Anomaly Detection. In Proceedings - 2017 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2017 (pp. 66-71). (Proceedings - 2017 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/TAAI.2017.43