Assisted Gait Phase Estimation Through an Embedded Depth Camera Using Modified Random Forest Algorithm Classification

The paper presents a novel method for the classification of gait phases for power gait orthosis users based on machine learning. The classification uses depth images collected from a Time of Flight camera embedded in the crutches employed for the assisted gait. The machine learning algorithm foresee...

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Veröffentlicht in:IEEE sensors journal Jg. 20; H. 6; S. 3343 - 3355
Hauptverfasser: Pasinetti, Simone, Fornaser, Alberto, Lancini, Matteo, De Cecco, Mariolino, Sansoni, Giovanna
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 15.03.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Cameras
Classification
Classifiers
Data collection
Depth sensing
Floors
Foot
Gait
gait analysis
Image classification
Legged locomotion
Machine learning
measurement
Orthoses
Radio frequency
Sensors
Three-dimensional displays
Uncertainty
ISSN:1530-437X, 1558-1748
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Zusammenfassung:The paper presents a novel method for the classification of gait phases for power gait orthosis users based on machine learning. The classification uses depth images collected from a Time of Flight camera embedded in the crutches employed for the assisted gait. The machine learning algorithm foresees an initial phase of data collection and processing, identifying the 3D points belonging to the foot and those belonging to the floor. From these, a feature set is computed analyzing the values of percentiles of distances of the foot from the floor, and passed to a modified version of Random Forest classifier, called Sigma-z Random Forest. The classifier considers the uncertainties associated to each feature set and provides both the classification of the gait phase (stance or swing) and an associated confidence value. In this work, we propose the use of the confidence value to improve the reliability of the gait phase classification, by applying an optimized threshold to the confidence value obtained for each new frame. The algorithm has been tested on different subjects and environments. An average classification accuracy of 87.3% has been obtained (+6.3% with respect to the standard random forest classifier), with a minor loss of unclassifiable frames. Results highlight that unclassifiable samples are usually associated to transitions between stance and swing.
Bibliographie:ObjectType-Article-1
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ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2019.2957667