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Open AccessArticle

Automated Accelerometer-Based Gait Event Detection During Multiple Running Conditions

1
Faculty of Kinesiology, University of Calgary, Calgary, AB T2N 1N4, Canada
2
Faculty of Nursing and Cumming School of Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada
3
Running Injury Clinic, University of Calgary, Calgary, AB T2N 1N4, Canada
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(7), 1483; https://doi.org/10.3390/s19071483
Received: 7 February 2019 / Revised: 15 March 2019 / Accepted: 22 March 2019 / Published: 27 March 2019
(This article belongs to the Special Issue Wearable Sensors for Gait and Motion Analysis 2018)
The identification of the initial contact (IC) and toe off (TO) events are crucial components of running gait analyses. To evaluate running gait in real-world settings, robust gait event detection algorithms that are based on signals from wearable sensors are needed. In this study, algorithms for identifying gait events were developed for accelerometers that were placed on the foot and low back and validated against a gold standard force plate gait event detection method. These algorithms were automated to enable the processing of large quantities of data by accommodating variability in running patterns. An evaluation of the accuracy of the algorithms was done by comparing the magnitude and variability of the difference between the back and foot methods in different running conditions, including different speeds, foot strike patterns, and outdoor running surfaces. The results show the magnitude and variability of the back-foot difference was consistent across running conditions, suggesting that the gait event detection algorithms can be used in a variety of settings. As wearable technology allows for running gait analyses to move outside of the laboratory, the use of automated accelerometer-based gait event detection methods may be helpful in the real-time evaluation of running patterns in real world conditions. View Full-Text
Keywords: wearable technology; gait event detection; running; accelerometer; initial contact; toe off wearable technology; gait event detection; running; accelerometer; initial contact; toe off
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MDPI and ACS Style

Benson, L.C.; Clermont, C.A.; Watari, R.; Exley, T.; Ferber, R. Automated Accelerometer-Based Gait Event Detection During Multiple Running Conditions. Sensors 2019, 19, 1483.

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