Abstract

Smartphones are ubiquitous and becoming more and more sophisticated, with ever-growing computing, networking and sensing powers. How can we help the users form a healthy habit by sending a reminder if s/he is sitting too long? How can we localize where we are inside a building and/or find the reception desk? Recognizing the physical activities (e.g., sitting, walking, jogging, etc) is a core building block to answer these questions and many more. We present AcRe, a human activity recognition application on smartphone. AcRe takes the motion data from different sensors on smartphones as inputs (e.g., accelerometer, compass, etc), and predicts a user's motion activities (e.g., walking upstairs, standing, sitting, etc) in real-time. It provides some additional functionalities, such as incorporating a user's feedback, daily activity summerization, etc. The application is built on iOS 7.0 and will be released soon in Apple's App Store. We will invite the audience to experiment with our AcRe in terms of its effectiveness, efficiency and applicability to various domains and the potential for further improvements.

Original languageEnglish (US)
Title of host publicationCIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery, Inc
Pages2021-2023
Number of pages3
ISBN (Print)9781450325981
DOIs
StatePublished - Nov 3 2014
Event23rd ACM International Conference on Information and Knowledge Management, CIKM 2014 - Shanghai, China
Duration: Nov 3 2014Nov 7 2014

Other

Other23rd ACM International Conference on Information and Knowledge Management, CIKM 2014
CountryChina
CityShanghai
Period11/3/1411/7/14

ASJC Scopus subject areas

  • Information Systems and Management
  • Computer Science Applications
  • Information Systems

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  • Cite this

    Su, X., Tong, H., & Ji, P. (2014). Accelerometer-based activity recognition on smartphone. In CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management (pp. 2021-2023). Association for Computing Machinery, Inc. https://doi.org/10.1145/2661829.2661836