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Ambulatory system for human motion analysis using a kinematic sensor: monitoring of daily physical activity in the elderly
Open accessAbstract
A new method of physical activity monitoring is presented, which is able to detect body postures (sitting, standing, and lying) and periods of walking in elderly persons using only one kinematic sensor attached to the chest. The wavelet transform, in conjunction with a simple kinematics model, was used to detect different postural transitions (PTs) and walking periods during daily physical activity. To evaluate the system, three studies were performed. The method was first tested on 11 community-dwelling elderly subjects in a gait laboratory where an optical motion system (Vicon) was used as a reference system. In the second study, the system was tested for classifying PTs (i.e., lying-to-sitting, sitting-to-lying, and turning the body in bed) in 24 hospitalized elderly persons. Finally, in a third study monitoring was performed on nine elderly persons for 45-60 min during their daily physical activity. Moreover, the possibility-to-perform long-term monitoring over 12 h has been shown. The first study revealed a close concordance between the ambulatory and reference systems. Overall, subjects performed 349 PTs during this study. Compared with the reference system, the ambulatory system had an overall sensitivity of 99% for detection of the different PTs. Sensitivities and specificities were 93% and 82% in sit-to-stand, and 82% and 94% in stand-to-sit, respectively. In both first and second studies, the ambulatory system also showed a very high accuracy (> 99%) in identifying the 62 transfers or rolling out of bed, as well as 144 different posture changes to the back, ventral, right and left sides. Relatively high sensitivity (> 90%) was obtained for the classification of usual physical activities in the third study in comparison with visual observation. Sensitivities and specificities were, respectively, 90.2% and 93.4% in sitting, 92.2% and 92.1% in "standing + walking," and, finally, 98.4% and 99.7% in lying. Overall detection errors (as percent of range) were 3.9% for "standing + walking," 4.1% for sitting, and 0.3% for lying. Finally, overall symmetric mean average errors were 12% for "standing + walking," 8.2% for sitting, and 1.3% for lying.
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Cited by
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Ambient intelligence: Technologies, applications, and opportunities
Cook, D. J., Augusto, J. C., & Jakkula, V. R. (2009) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2009.04.001 · Cited by 964
Roh, E., Hwang, B.-U., Kim, D., Kim, B.-Y., & Lee, N.-E. (2015) · ACS Nano · doi:10.1021/acsnano.5b01613 · Cited by 927
Activity identification using body-mounted sensors—a review of classification techniques
Preece, S. J., Goulermas, J. Y., Kenney, L. P. J., Howard, D., Meijer, K., & Crompton, R. (2009) · Physiological Measurement · doi:10.1088/0967-3334/30/4/r01 · Cited by 600
Direct measurement of human movement by accelerometry
Godfrey, A., Conway, R., Meagher, D., & ÓLaighin, G. (2008) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2008.09.005 · Cited by 591
Metadata
- Authors
- Najafi, B., Aminian, K., Paraschiv-Ionescu, A., Loew, F., Bula, C. J., & Robert, P.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2003-06
- Volume
- 50
- Issue
- 6
- Pages
- 711-723
- ISSN
- 0018-9294 (ISSN-L)
- OpenAlex
- W2114868488
- Cited by
- 762
- References
- 1
- FWCI
- 13.11
- Type
- journal article
- Language
- en
- Open access
- green
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- Najafi, B., Aminian, K., Paraschiv-Ionescu, A., Loew, F., Bula, C. J., & Robert, P. (2003). Ambulatory system for human motion analysis using a kinematic sensor: monitoring of daily physical activity in the elderly. IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/tbme.2003.812189
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