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Activity recognition of the elderly

Published: 26 September 2010 Publication History

Abstract

The development of context aware services is one proposed way to support independent living for the elderly. Performing test scenarios with the elderly helps when developing the context aware services. However, rigorous testing is not always desirable when working with elderly subjects. Our research proposes to capture the activity data of the subjects to use with a virtual environment and virtual human to test the services. This paper begins a larger set of research by describing a process in which the daily activities of the elderly are captured using accelerometer sensors. The process consists of pre-investigation, data capturing and data postprocessing. Using common activity recognition methods daily activities chosen by two elderly subjects themselves are recognized reasonably well. This allows using the described process in larger experiments to acquire more activity data.

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Cited By

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  • (2016)MyData Approach for Personal Health -- A Service Design Case for Young AthletesProceedings of the 2016 49th Hawaii International Conference on System Sciences (HICSS)10.1109/HICSS.2016.436(3493-3502)Online publication date: 5-Jan-2016
  • (2016)User context recognition using smartphone sensors and classification modelsJournal of Network and Computer Applications10.1016/j.jnca.2016.03.01366:C(33-51)Online publication date: 1-May-2016
  • (2013)Using 3D Virtual Environments to Monitor Elderly Patient Activity with Low Cost SensorsProceedings of the 2013 Seventh International Conference on Next Generation Mobile Apps, Services and Technologies10.1109/NGMAST.2013.23(81-86)Online publication date: 25-Sep-2013
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cover image ACM Other conferences
CASEMANS '10: Proceedings of the 4th ACM International Workshop on Context-Awareness for Self-Managing Systems
September 2010
76 pages
ISBN:9781450302135
DOI:10.1145/1858367
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 September 2010

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Author Tags

  1. 6-dof sensors
  2. activity recognition
  3. e-health

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Cited By

View all
  • (2016)MyData Approach for Personal Health -- A Service Design Case for Young AthletesProceedings of the 2016 49th Hawaii International Conference on System Sciences (HICSS)10.1109/HICSS.2016.436(3493-3502)Online publication date: 5-Jan-2016
  • (2016)User context recognition using smartphone sensors and classification modelsJournal of Network and Computer Applications10.1016/j.jnca.2016.03.01366:C(33-51)Online publication date: 1-May-2016
  • (2013)Using 3D Virtual Environments to Monitor Elderly Patient Activity with Low Cost SensorsProceedings of the 2013 Seventh International Conference on Next Generation Mobile Apps, Services and Technologies10.1109/NGMAST.2013.23(81-86)Online publication date: 25-Sep-2013
  • (2012)User interaction in smart ambient environment targeted for senior citizenMedical & Biological Engineering & Computing10.1007/s11517-012-0906-850:11(1119-1126)Online publication date: 26-Apr-2012
  • (2012)Does location help daily activity recognition?Proceedings of the 10th international smart homes and health telematics conference on Impact Ananlysis of Solutions for Chronic Disease Prevention and Management10.1007/978-3-642-30779-9_11(83-90)Online publication date: 12-Jun-2012

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