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...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transactions on neural systems and rehabilitation engineering Jg. 30; S. 108 - 114
Hauptverfasser:	Donahue, Seth R., Hahn, Michael E.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!