The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
A Gait Monitoring System Based on Air Pressure Sensors Embedded in a Shoe
Abstract
Measurement of ground contact forces (GCFs) provides necessary information to detect human gait phases. In this paper, a new analysis method of the GCF signals is discussed for detection of the gait phases. Human gaits are complicated, and the gait phases cannot be exactly distinguished by comparing sensor outputs to a threshold. This paper proposes a method by fuzzy logic for detecting the gait phases continuously and smoothly. The smooth and continuous detection of the gait phases enables a full use of information obtained from GCF sensors. For advanced rehabilitation systems, this paper also introduces a higher level algorithm that quantitatively monitors the amount of abnormalities in a human gait. The abnormalities detected by the proposed method include an improper GCF pattern as well as an incorrect sequence of the gait phases. To realize the monitoring algorithm, the gait phases are analyzed as a vector and the abnormalities are detected by simple kinematic equations. The proposed methods are implemented by using signals from sensor-embedded shoes called smart shoes. Each smart shoe has four GCF sensors installed between the cushion pad and the sole. The GCF sensor applies an air pressure sensor connected to an air bladder. A gait monitoring system that integrates the proposed methods is shown in this paper and verified for both normal and abnormal gaits.
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Citations
Cites
Biomechanics and motor control of human movement
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Mechanical Behavior of Materials
Hosford, W. F. (2009) · Cambridge University Press eBooks · doi:10.1017/cbo9780511810923 · Cited by 1,218
Adaptive control of a variable-impedance ankle-foot orthosis to assist drop-foot gait
Blaya, J. A., & Herr, H. (2004) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2003.823266 · Cited by 741
Gait Analysis Using a Shoe-Integrated Wireless Sensor System
Bamberg, S., Benbasat, A. Y., Scarborough, D. M., Krebs, D. E., & Paradiso, J. A. (2008) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2007.899493 · Cited by 737
A reliable gait phase detection system
Pappas, I. P. I., Popovic, M. R., Keller, T., Dietz, V., & Morari, M. (2001) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/7333.928571 · Cited by 495
Design and control of an exoskeleton for the elderly and patients
Kong, K., & Jeon, D. (2006) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2006.878550 · Cited by 403
Self-citationHayashi, T., Kawamoto, H., & Sankai, Y. (2005) · 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems · doi:10.1109/iros.2005.1545505 · Cited by 377
Intelligent shoes for abnormal gait detection
Chen, M., Huang, B., & Xu, Y. (2008) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2008.4543503 · Cited by 122
A Wearable Biomechatronic Interface for Controlling Robots with Voluntary Foot Movements
Carrozza, M. C., Persichetti, A., Laschi, C., Vecchi, F., Lazzarini, R., Vacalebri, P., & Dario, P. (2007) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2006.886250 · Cited by 71
Gait Event Detection with Intelligent Shoes
Huang, B., Chen, M., Shi, X., & Xu, Y. (2007) · IEEE International Conference on Information Acquisition · doi:10.1109/icia.2007.4295799 · Cited by 44
Cited by
Gait Partitioning Methods: A Systematic Review
Taborri, J., Palermo, E., Rossi, S., & Cappa, P. (2016) · Sensors · doi:10.3390/s16010066 · Cited by 361
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
Gait phase analysis based on a Hidden Markov Model
Bae, J., & Tomizuka, M. (2011) · Mechatronics · doi:10.1016/j.mechatronics.2011.03.003 · Cited by 106
Self-citationPersonalized Human Activity Recognition Based on Integrated Wearable Sensor and Transfer Learning
Fu, Z., He, X., Wang, E., Huo, J., Huang, J., & Wu, D. (2021) · Sensors · doi:10.3390/s21030885 · Cited by 79
Recognition of walking environments and gait period by surface electromyography
Kyeong, S., Shin, W., Yang, M., Heo, U., Feng, J.-R., & Kim, J. (2019) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.1800601 · Cited by 68
Metadata
- Authors
- Kong, K., & Tomizuka, M.
- Venue
- IEEE/ASME Transactions on Mechatronics
- Published
- 2009-06
- Volume
- 14
- Issue
- 3
- Pages
- 358-370
- ISSN
- 1083-4435 (ISSN-L)
- 1941-014X
- OpenAlex
- W2047993060
- Cited by
- 301
- References
- 13
- FWCI
- 8.73
- Type
- journal article
- Language
- en
- Open access
- closed
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