Human Gait-labeling Uncertainty and a Hybrid Model for Gait Segmentation

Abstract

<div><div><div><p>Motion capture systems are widely accepted as ground-truth for gait analysis and are used for the validation of other gait analysis systems.To date, their reliability and limitations in manual labeling of gait events have not been studied.</p><p><b>Objectives</b>: Evaluate human manual labeling uncertainty and introduce a new hybrid gait analysis model for long-term monitoring.</p><p><b>Methods</b>: Evaluate and estimate inter-labeler inconsistencies by computing the limits-of-agreement; develop a model based on dynamic time warping and convolutional neural network to identify a valid stride and eliminate non-stride data in walking inertial data collected by a wearable device; Gait events are detected within a valid stride region afterwards; This method makes the subsequent data computation more efficient and robust.</p><p><b>Results</b>: The limits of inter-labeler agreement for key</p><p>gait events of heel off, toe off, heel strike, and flat foot are 72 ms, 16 ms, 22 ms, and 80 ms, respectively; The hybrid model's classification accuracy for a stride and a non-stride are 95.16% and 84.48%, respectively; The mean absolute error for detected heel off, toe off, heel strike, and flat foot are 24 ms, 5 ms, 9 ms, and 13 ms, respectively.</p><p><b>Conclusions</b>: The results show the inherent label uncertainty and the limits of human gait labeling of motion capture data; The proposed hybrid-model's performance is comparable to that of human labelers and it is a valid model to reliably detect strides in human gait data.</p><p><b>Significance</b>: This work establishes the foundation for fully automated human gait analysis systems with performances comparable to human-labelers.</p></div></div></div>

Real-Time Gait Phase Detection on Wearable Devices for Unsupervised Gait

“…8 In addition, the algorithm only uses three signals to classify the gait phases, all of which come from a single gyroscope only. This reduces the amount of information used to characterize the gait cycle compared to the studies that use both gyroscope and accelerometers [31], and thus saves the energy and the cost of the wearable device. Moreover, no prior calibration is required for the sensor data, which is a significant improvement compared to other studies that need additional calibration procedures [32].…”

“…Gait phases were labeled by a rule-based detection algorithm, which has been validated with the motion capture system and shows high accuracy [31]. Most importantly, it is not constrained by the lab environment and can label outdoor walking data at any time and anywhere for real-time algorithm validation.…”

Real-Time Gait Phase Detection on Wearable Devices for Unsupervised Gait

“…In addition, the algorithm only uses three signals to classify the gait phases, all of which come from a single gyroscope only. This reduces the amount of information used to characterize the gait cycle compared to the studies that use both gyroscope and accelerometers [22], and thus saves the energy and the cost of the wearable device. Moreover, no prior calibration is required for the sensor data, which is a significant improvement compared to the studies that need additional calibration procedures [32].…”

“…The total number of free walking strides collected for real-time algorithm validation is around 50000. Gait phases were labeled by a rule-based detection algorithm, which has been validated with the motion capture system and shows high accuracy [22]. Most importantly, it is not constrained by the lab environment and can label outdoor walking data at any time and anywhere for real-time algorithm validation.…”