The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Kinetic Gait Analysis Using a Low-Cost Insole
Abstract
Abnormal gait caused by stroke or other pathological reasons can greatly impact the life of an individual. Being able to measure and analyze that gait is often critical for rehabilitation. Motion analysis labs and many current methods of gait analysis are expensive and inaccessible to most individuals. The low-cost, wearable, and wireless insole-based gait analysis system in this study provides kinetic measurements of gait by using low-cost force sensitive resistors. This paper describes the design and fabrication of the insole and its evaluation in six control subjects and four hemiplegic stroke subjects. Subject-specific linear regression models were used to determine ground reaction force plus moments corresponding to ankle dorsiflexion/plantarflexion, knee flexion/extension, and knee abduction/adduction. Comparison with data simultaneously collected from a clinical motion analysis laboratory demonstrated that the insole results for ground reaction force and ankle moment were highly correlated (all >0.95) for all subjects, while the two knee moments were less strongly correlated (generally >0.80). This provides a means of cost-effective and efficient healthcare delivery of mobile gait analysis that can be used anywhere from large clinics to an individual's home.
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Cites
Dejnabadi, H., Jolles, B. M., & Aminian, K. (2005) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2005.851475 · Cited by 286
A Reliable Gyroscope-Based Gait-Phase Detection Sensor Embedded in a Shoe Insole
Pappas, I. P. I., Keller, T., Mangold, S., Popovic, M., Dietz, V., & Morari, M. (2004) · IEEE Sensors Journal · doi:10.1109/jsen.2004.823671 · Cited by 221
Cited by
Smart Insole: A Wearable Sensor Device for Unobtrusive Gait Monitoring in Daily Life
Lin, F., Wang, A., Zhuang, Y., Tomita, M. R., & Xu, W. (2016) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2016.2585643 · Cited by 265
Molinaro, D. D., Kang, I., Camargo, J., Gombolay, M. C., & Young, A. J. (2022) · IEEE Transactions on Medical Robotics and Bionics · doi:10.1109/tmrb.2022.3144025 · Cited by 90
Increasing fall risk awareness using wearables: a fall risk awareness protocol
Danielsen, A., Olofsen, H., & Bremdal, B. A. (2016) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2016.08.016 · Cited by 66
Molinaro, D. D., Park, E. O., & Young, A. J. (2023) · 2023 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra48891.2023.10161286 · Cited by 14
Metadata
- Authors
- Howell, A. M., Kobayashi, T., Hayes, H. A., Foreman, K. B., & Bamberg, S. J. M.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2013-12
- Volume
- 60
- Issue
- 12
- Pages
- 3284-3290
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- OpenAlex
- W1989636721
- Cited by
- 207
- References
- 2
- FWCI
- 6.20
- Type
- journal article
- Language
- en
- Open access
- closed
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
