Quantifying Asymmetric Gait Pattern Changes Using a Hidden Markov Model Similarity Measure (HMM-SM) on Inertial Sensor Signals

Quantifying Asymmetric Gait Pattern Changes Using a Hidden Markov Model Similarity Measure (HMM-SM) on Inertial Sensor Signals

Wearable gait analysis systems using inertial sensors offer the potential for easy-to-use gait assessment in lab and free-living environments. This can enable objective long-term monitoring and decision making for individuals with gait disabilities. This study explores a novel approach that applies...

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Bibliographic Details
Main Authors: Gabriel Ng, Aliaa Gouda, Jan Andrysek
Format: Article
Language:English
Published: MDPI AG 2024-10-01
Series:Sensors
Subjects:
gait assessment
gait disabilities
machine learning
unsupervised learning
wearable sensors
Online Access:https://www.mdpi.com/1424-8220/24/19/6431
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https://www.mdpi.com/1424-8220/24/19/6431

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