Adaptive Algorithm for Gait Segmentation Using a Single IMU in the Thigh Pocket

Gait segmentation may help monitoring the evolution of patients during rehabilitation treatment through the analysis of properly defined metrics. Many algorithms detect gait events using Inertial Measurement Units (IMUs). However, most of them require the IMUs to be attached to the body, or to assis...

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Veröffentlicht in:IEEE sensors journal Jg. 22; H. 13; S. 13251 - 13261
Hauptverfasser: Garcia, Francisco A., Perez-Ibarra, Juan C., Terra, Marco H., Siqueira, Adriano A. G.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.07.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Abnormalities
Adaptive algorithms
Algorithms
Angular velocity
Decoding
Evaluation
Foot
Gait
Gait analysis
hidden Markov model
Hidden Markov models
inertial measurement units
Inertial platforms
machine learning
Markov chains
Rehabilitation
Segmentation
Sensors
Smart phones
Smartphones
Thigh
Viterbi algorithm
Viterbi decoders
wearable sensors
ISSN:1530-437X, 1558-1748
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Zusammenfassung:Gait segmentation may help monitoring the evolution of patients during rehabilitation treatment through the analysis of properly defined metrics. Many algorithms detect gait events using Inertial Measurement Units (IMUs). However, most of them require the IMUs to be attached to the body, or to assistive devices. Often individuals must go to a gait laboratory which owns specialized equipment so that gait analysis can be performed. IMUs are present in modern smartphones. An IMU<inline-formula> <tex-math notation="LaTeX">/ </tex-math></inline-formula>smartphone carried in the pocket during daily activities could allow the analysis of much more data in a more comfortable setting. We address the detection of four gait events: Heel-Strike (HS), Flat-Foot (FF), Heel-Off (HO) and Toe-Off (TO) using a single noisy IMU attached to a smartphone placed inside the thigh pocket. Gait was modelled as a four-state left-right Hidden Markov Model (HMM) whose observations follow multivariate Gaussian distributions. The decoding in post-processing was performed with a modified Viterbi decoder that accounts for a rule-based (RB) detection of TO. To validate the approach, experiments were performed in nine subjects with no gait abnormalities. Our algorithm obtained median F1-Scores <inline-formula> <tex-math notation="LaTeX">\geq0.955 </tex-math></inline-formula> for all events in intra-subject evaluation and median F1-Scores <inline-formula> <tex-math notation="LaTeX">\geq0.757 </tex-math></inline-formula> in inter-subject evaluations (with fast training and with a populational model). It demonstrated high generalization capability in our dataset and competitive performances when compared to other algorithms using high-quality IMUs attached to the body. This work is a step towards accurate and refined gait segmentation using a smartphone carried in the pocket.
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ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2022.3177951