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Mobile physical activity recognition of stand-up and sit-down transitions for user behavior analysis

: Bieber, Gerald; Koldrack, Philipp; Sablowski, Christopher; Peter, Christian; Urban, Bodo


Makedon, F. ; Association for Computing Machinery -ACM-:
3rd ACM International Conference on PErvasive Technologies Related to Assistive Environments, PETRA 2010. Proceedings : Samos, Greece - June 23 - 25, 2010
New York: ACM, 2010
ISBN: 978-1-4503-0071-1
Art. 50, 5 S.
International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) <3, 2010, Samos>
Fraunhofer IGD ()
physical activity monitoring; acceleration sensor; assistive technology; user behavior

Sufficient physical activity is required for everybody, especially for elderly people. Monitoring of physical activity is possible in daily life by using mobile sensors such as acceleration sensors. The recognition of periodic activity types like walking, cycling, car driving etc. is easy to perform. However, the identification of transitions between physical activities is difficult, because those events are nonrecurring and unique. The estimation about the share of standing or sitting during work is interesting for the design of the modem workplace. Human ergonomics demand for a limitation of standing work; this may even be enforced by the legal protection of working mothers to improve the working condition. The recognition of standing und sitting is furthermore useful within the home living area design. Hereby a detection of staying, sitting and walking supports the assessment of the activities of daily life.
This paper addresses the methodology of mobile physical activity recognition of transitions between sitting und standing by using only one three-dimensional acceleration sensor. The recognition is performed by using n synthetic kernel signal arid a correlation of the measurement signal. For the evaluation, a detection application has been developed which uses the build-in sensors of a standard mobile phone. The evaluation inc1uded 12 subjects and the result show that mobile recognition of activity transitions is possible.