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A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity
Open accessAbstract
The present study describes the development of a triaxial accelerometer (TA) and a portable data processing unit for the assessment of daily physical activity. The TA is composed of three orthogonally mounted uniaxial piezoresistive accelerometers and can be used to register accelerations covering the amplitude and frequency ranges of human body acceleration. Interinstrument and test-retest experiments showed that the offset and the sensitivity of the TA were equal for each measurement direction and remained constant on two measurement days. Transverse sensitivity was significantly different for each measurement direction, but did not influence accelerometer output (< 3% of the sensitivity along the main axis). The data unit enables the on-line processing of accelerometer output to a reliable estimator of physical activity over eight-day periods. Preliminary evaluation of the system in 13 male subjects during standardized activities in the laboratory demonstrated a significant relationship between accelerometer output and energy expenditure due to physical activity, the standard reference for physical activity (r = 0.89). Shortcomings of the system are its low sensitivity to sedentary activities and the inability to register static exercise. The validity of the system for the assessment of normal daily physical activity and specific activities outside the laboratory should be studied in free-living subjects.
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Bao, L., & Intille, S. S. (2004) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-24646-6_1 · Cited by 3,147
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Yang, C.-C., & Hsu, Y.-L. (2010) · Sensors · doi:10.3390/s100807772 · Cited by 1,058
Human motion tracking for rehabilitation-A survey
Zhou, H., & Hu, H. (2008) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2007.09.001 · Cited by 813
Physical Activity Assessment With Accelerometers: An Evaluation Against Doubly Labeled Water
Plasqui, G., & Westerterp, K. R. (2007) · Obesity · doi:10.1038/oby.2007.281 · Cited by 654
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Luinge, H. J., & Veltink, P. H. (2005) · Medical & Biological Engineering & Computing · doi:10.1007/bf02345966 · Cited by 647
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ESLIGER, D. W., ROWLANDS, A. V., HURST, T. L., CATT, M., MURRAY, P., & ESTON, R. G. (2011) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0b013e31820513be · Cited by 625
Direct measurement of human movement by accelerometry
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Hurling, R., Catt, M., De Boni, M., Fairley, B. W., Hurst, T., Murray, P., Richardson, A., & Sodhi, J. S. (2007) · Journal of Medical Internet Research · doi:10.2196/jmir.9.2.e7 · Cited by 536
Preece, S. J., Goulermas, J. Y., Kenney, L. P. J., & Howard, D. (2009) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2008.2006190 · Cited by 551
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Ward, J. A., Lukowicz, P., Troster, G., & Starner, T. E. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.197 · Cited by 431
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A pilot study of long-term monitoring of human movements in the home using accelerometry
Mathie, M. J., Coster, A. C. F., Lovell, N. H., Celler, B. G., Lord, S. R., & Tiedemann, A. (2004) · Journal of Telemedicine and Telecare · doi:10.1258/135763304323070788 · Cited by 153
Nijsen, T. M. E., Arends, J. B. A. M., Griep, P. A. M., & Cluitmans, P. J. M. (2005) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2005.04.011 · Cited by 139
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Takeda, R., Tadano, S., Todoh, M., Morikawa, M., Nakayasu, M., & Yoshinari, S. (2009) · Journal of Biomechanics · doi:10.1016/j.jbiomech.2008.10.027 · Cited by 128
Chamroukhi, F., Mohammed, S., Trabelsi, D., Oukhellou, L., & Amirat, Y. (2013) · Neurocomputing · doi:10.1016/j.neucom.2013.04.003 · Cited by 88
Drift Removal for Improving the Accuracy of Gait Parameters Using Wearable Sensor Systems
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Human activity recognition based on a sensor weighting hierarchical classifier
Banos, O., Damas, M., Pomares, H., Rojas, F., Delgado-Marquez, B., & Valenzuela, O. (2013) · Soft Computing · doi:10.1007/s00500-012-0896-3 · Cited by 79
Real-time elderly activity monitoring system based on a tri-axial accelerometer
Kang, D. W., Choi, J. S., Lee, J. W., Chung, S. C., Park, S. J., & Tack, G. R. (2010) · Disability and Rehabilitation: Assistive Technology · doi:10.3109/17483101003718112 · Cited by 43
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Le Heron, C., Fang, K., Gubbi, J., Churilov, L., Palaniswami, M., Davis, S., & Yan, B. (2014) · Cerebrovascular Diseases · doi:10.1159/000360808 · Cited by 15
Manohar, C. U., Koepp, G. A., McCrady-Spitzer, S. K., & Levine, J. A. (2012) · ICAN: Infant, Child, & Adolescent Nutrition · doi:10.1177/1941406412450644 · Cited by 12
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Nguyen, T., Gupta, S., Venkatesh, S., & Phung, D. (2016) · Pattern Recognition Letters · doi:10.1016/j.patrec.2015.11.003 · Cited by 8
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Metadata
- Authors
- Bouten, C. V. C., Koekkoek, K. T. M., Verduin, M., Kodde, R., & Janssen, J. D.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 1997-03
- Volume
- 44
- Issue
- 3
- Pages
- 136-147
- ISSN
- 0018-9294 (ISSN-L)
- OpenAlex
- W2118630306
- Cited by
- 911
- References
- 0
- FWCI
- 1.91
- Type
- journal article
- Language
- en
- Open access
- green
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