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Making sense of accelerometer measurements in pervasive physical activity applications
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Conference on Human Factors in Computing Systems archive
Proceedings of the 27th international conference extended abstracts on Human factors in computing systems table of contents
Boston, MA, USA
SESSION: Student research competition table of contents
Pages 3425-3430  
Year of Publication: 2009
ISBN:978-1-60558-247-4
Authors
Yuichi Fujiki  University of Houston, Houston, TX, USA
Panagiotis Tsiamyrtzis  Athens University of Economics and Business, Athens, Greece
Ioannis Pavlidis  University of Houston, Houston, TX, USA
Sponsors
ACM: Association for Computing Machinery
SIGCHI: ACM Special Interest Group on Computer-Human Interaction
Publisher
ACM  New York, NY, USA
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ABSTRACT

In the last few years, accelerometer-based entertainment and health applications have been receiving increased attention in the research and commercial worlds. The effect of accelerometer placement on different parts of the body, despite its apparent significance, received little consideration. This paper documents through experimentation the different characteristics of accelerometer output on the waist, arm, wrist, thigh, and ankle in the context of translational body motion (walk). Furthermore, it offers experimental formulas that transform peripheral body measurements to more reliable, center body (i.e., waist) measurements, and these in turn to caloric measurements, which are the standard physical activity units. The importance of these results on the design of ubiquitous health applications and the ensuing user experiences cannot be underestimated. The paper's methodology can be used in further studies in other physical activity contexts, where more elaborate body motion patterns are involved.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
Antonnson, E.K., Mann, R.W. The frequency content of gait. Journal of biomechanics, 18(1985), 39-47.
 
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Bouten, C.V., Koekkoek, K.T.M., Verduin, M., Kodde, R., Janssen, J.D. A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity. IEEE Transactions on Biomedical Engineering 44, 3 (1997), 136--147.
3
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5
 
6
Jequier, E., Acheson, K., Schutz, Y. Assessment of energy expenditure and fuel utilization in man. Annual Review of Nutrition 7 (1987), 187--208.
 
7
Lester J., Choudhury T., Borriello G., A practical approach to recognizing physical activities. Lecture Notes in Computer Science, 3968 (2006) 1--16, Springer-Verlag 2006.
 
8
 
9
Nawyn, J., Intille,S.S., Larson, K. Embedding behavior modification strategies into a consumer electronics device: a case study. In Proc. UbiComp 2006 , Springer-Verlag, 2006.

Collaborative Colleagues:
Yuichi Fujiki: colleagues
Panagiotis Tsiamyrtzis: colleagues
Ioannis Pavlidis: colleagues