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Sensors 2017, 17(4), 671; doi:10.3390/s17040671

Real-Time Gait Event Detection Based on Kinematic Data Coupled to a Biomechanical Model †

1
Division PMA, Department of Mechanical Engineering, Katholieke Universiteit Leuven, 3000 Leuven, Belgium
2
Department of Biomedical Kinesiology, Katholieke Universiteit Leuven, 3000 Leuven, Belgium
3
AI Laboratory, Vrije Universiteit Brussel, 1050 Ixelles, Belgium
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 6 February 2017 / Revised: 21 March 2017 / Accepted: 22 March 2017 / Published: 24 March 2017
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [6633 KB, uploaded 24 March 2017]   |  

Abstract

Real-time detection of multiple stance events, more specifically initial contact (IC), foot flat (FF), heel off (HO), and toe off (TO), could greatly benefit neurorobotic (NR) and neuroprosthetic (NP) control. Three real-time threshold-based algorithms have been developed, detecting the aforementioned events based on kinematic data in combination with a biomechanical model. Data from seven subjects walking at three speeds on an instrumented treadmill were used to validate the presented algorithms, accumulating to a total of 558 steps. The reference for the gait events was obtained using marker and force plate data. All algorithms had excellent precision and no false positives were observed. Timing delays of the presented algorithms were similar to current state-of-the-art algorithms for the detection of IC and TO, whereas smaller delays were achieved for the detection of FF. Our results indicate that, based on their high precision and low delays, these algorithms can be used for the control of an NR/NP, with the exception of the HO event. Kinematic data is used in most NR/NP control schemes and is thus available at no additional cost, resulting in a minimal computational burden. The presented methods can also be applied for screening pathological gait or gait analysis in general in/outside of the laboratory. View Full-Text
Keywords: gait segmentation; modeling; real-time event detection; adaptive thresholds; neuroprostheses; neurorobotics; kinematics gait segmentation; modeling; real-time event detection; adaptive thresholds; neuroprostheses; neurorobotics; kinematics
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Lambrecht, S.; Harutyunyan, A.; Tanghe, K.; Afschrift, M.; De Schutter, J.; Jonkers, I. Real-Time Gait Event Detection Based on Kinematic Data Coupled to a Biomechanical Model †. Sensors 2017, 17, 671.

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