Feature Identification With a Heuristic Algorithm and an Unsupervised Machine Learning Algorithm for Prior Knowledge of Gait Events

The purpose of this study was to compare a heuristic feature identification algorithm with output from the Beta Process Auto Regressive Hidden Markov Model (BP-AR-HMM) utilizing minimally sampled (≤ 100 Hz) human locomotion data for identification of gait events prior to their occurrence. Data were...

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Bibliographic Details
Published in:IEEE transactions on neural systems and rehabilitation engineering Vol. 30; pp. 108 - 114
Main Authors: Donahue, Seth R., Hahn, Michael E.
Format: Journal Article
Language:English
Published: United States IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Biomechanical Phenomena
Foot
Gait
Heuristic
Heuristic algorithms
Heuristic methods
Heuristics
Hidden Markov models
Humans
Inertial measurement units
Inertial platforms
Inertial sensing devices
Integrated circuits
Lead time
Learning algorithms
Locomotion
Machine learning
Machine learning algorithms
Markov chains
Problem solving
real-world gait event detection
Time series analysis
Unsupervised learning
Unsupervised Machine Learning
Walking
ISSN:1534-4320, 1558-0210, 1558-0210
Online Access:Get full text
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Summary:The purpose of this study was to compare a heuristic feature identification algorithm with output from the Beta Process Auto Regressive Hidden Markov Model (BP-AR-HMM) utilizing minimally sampled (≤ 100 Hz) human locomotion data for identification of gait events prior to their occurrence. Data were collected from 16 participants (21-64 years) using a single gyroscopic sensor in an inertial measurement unit on the dorsum of the foot, across multiple locomotion modes, including level ground walking and running (across speeds 0.8 m s −1 - 3.0 m s −1 ), ramps and stairs. Identification of gait events, initial contact (IC) and toe off (TO) with the heuristic algorithm, was 94% across locomotion modes. The features identified prior to initial contact had a lead time of 186.32 ± 86.70 ms, while TO had a lead time of 63.96 ± 46.30 ms. The BP-AR-HMM identified features that indicated an impending IC and TO with 99% accuracy, with a lead time of 59.41 ± 54.41 ms for IC and 90.79 ± 35.51 ms for TO. These approaches are consistent in their identification of gait events and have the potential to be utilized for classification and prediction of locomotion mode.
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ISSN:1534-4320
1558-0210
1558-0210
DOI:10.1109/TNSRE.2021.3131953