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A reliable gait phase detection system
Abstract
A new highly reliable gait phase detection system, which can be used in gait analysis applications and to control the gait cycle of a neuroprosthesis for walking, is described. The system was designed to detect in real-time the following gait phases: stance, heel-off, swing, and heel-strike. The gait phase detection system employed a gyroscope to measure the angular velocity of the foot and three force sensitive resistors to assess the forces exerted by the foot on the shoe sole during walking. A rule-based detection algorithm, which was running on a portable microprocessor board, processed the sensor signals. In the presented experimental study ten able body subjects and six subjects with impaired gait tested the device in both indoor and outdoor environments (0-25 degrees C). The subjects were asked to walk on flat and irregular surfaces, to step over small obstacles, to walk on inclined surfaces, and to ascend and descend stairs. Despite the significant variation in the individual walking styles the system achieved an overall detection reliability above 99% for both subject groups for the tasks involving walking on flat, irregular, and inclined surfaces. In the case of stair climbing and descending tasks the success rate of the system was above 99% for the able body subjects and above 96 % for the subjects with impaired gait. The experiments also showed that the gait phase detection system, unlike other similar devices, was insensitive to perturbations caused by nonwalking activities such as weight shifting between legs during standing, feet sliding, sitting down, and standing up.
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Cited by
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Qi, J., Yang, P., Min, G., Amft, O., Dong, F., & Xu, L. (2017) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2017.06.018 · Cited by 406
Activity Recognition Using a Single Accelerometer Placed at the Wrist or Ankle
MANNINI, A., INTILLE, S. S., ROSENBERGER, M., SABATINI, A. M., & HASKELL, W. (2013) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0b013e31829736d6 · Cited by 383
Gait recognition: a challenging signal processing technology for biometric identification
Boulgouris, N. V., Hatzinakos, D., & Plataniotis, K. N. (2005) · IEEE Signal Processing Magazine · doi:10.1109/msp.2005.1550191 · Cited by 362
Wearable sensor-based real-time gait detection: A systematic review
Prasanth, H., Caban, M., Keller, U., Courtine, G., Ijspeert, A., Vallery, H., & von Zitzewitz, J. (2021) · Sensors · doi:10.3390/s21082727 · Cited by 348
Metadata
- Authors
- Pappas, I. P. I., Popovic, M. R., Keller, T., Dietz, V., & Morari, M.
- Venue
- IEEE Transactions on Neural Systems and Rehabilitation Engineering
- Published
- 2001-06
- Volume
- 9
- Issue
- 2
- Pages
- 113-125
- ISSN
- 1534-4320 (ISSN-L)
- 1558-0210
- OpenAlex
- W2124517686
- Cited by
- 495
- References
- 0
- FWCI
- 7.09
- Type
- journal article
- Language
- en
- Open access
- closed
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- Pappas, I. P. I., Popovic, M. R., Keller, T., Dietz, V., & Morari, M. (2001). A reliable gait phase detection system. IEEE Transactions on Neural Systems and Rehabilitation Engineering. https://doi.org/10.1109/7333.928571
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