Abstract
There are several algorithms that use the 3D acceleration and/or rotational velocity vectors from IMU sensors to identify gait events (i.e., toe-off and heel-strike). However, a clear understanding of how sensor location and the type of walking task effect the accuracy of gait event detection algorithms is lacking. To address this knowledge gap, seven participants were recruited (4M/3F; 26.0 ± 4.0 y/o) to complete a straight walking task and obstacle navigation task while data were collected from IMUs placed on the foot and shin. Five different commonly used algorithms to identify the toe-off and heel-strike gait events were applied to each sensor location on a given participant. Gait metrics were calculated for each sensor/algorithm combination using IMUs and a reference pressure sensing walkway. Results show algorithms using medial-lateral rotational velocity and anterior-posterior acceleration are fairly robust against different sensor locations and walking tasks. Certain algorithms applied to heel and lower lateral shank sensor locations will result in degraded algorithm performance when calculating gait metrics for curved walking compared to straight overground walking. Understanding how certain types of algorithms perform for given sensor locations and tasks can inform robust clinical protocol development using wearable technology to characterize gait in both laboratory and real-world settings.
Highlights
Evaluation of human gait is important for the diagnosis of gait pathologies caused by certain medical conditions as well as the rehabilitation of those gait pathologies [1].The use of wearable sensors, such as inertial measurement units (IMUs), to detect gait events and calculate spatiotemporal gait metrics in both healthy populations as well as those with gait pathologies is an emerging area of research and clinical relevance [2]
Limited previous works have noted that sensors mounted on the lumbar spine generally perform worse than sensors mounted on the shank or foot for gait event detection applications [2], a robust systematic evaluation of the impact of different sensor locations on the accuracy of various gait event detection algorithms is poorly documented [14] with very little information about differences between shank and foot mounted sensors
The mean absolute bias values and root mean square error (RMSE) values are reported in units of milliseconds to facilitate comparisons with previous studies
Summary
The use of wearable sensors, such as inertial measurement units (IMUs), to detect gait events and calculate spatiotemporal gait metrics in both healthy populations as well as those with gait pathologies is an emerging area of research and clinical relevance [2]. One research group recently published a literature review that identified 17 different algorithms for identifying heel strike and toe off gait events during over ground walking [2] and conducted an analysis of these 17 gait event detection algorithms during gait in water [9]. Limited previous works have noted that sensors mounted on the lumbar spine generally perform worse than sensors mounted on the shank or foot for gait event detection applications [2], a robust systematic evaluation of the impact of different sensor locations on the accuracy of various gait event detection algorithms is poorly documented [14] with very little information about differences between shank and foot mounted sensors
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