Automated gait event detection for a variety of locomotion tasks using a novel gyroscope-based algorithm.
Initial contact
Linear movements
Rule-based algorithm
Toe-off
Turning conditions
Wearable sensors
Journal
Gait & posture
ISSN: 1879-2219
Titre abrégé: Gait Posture
Pays: England
ID NLM: 9416830
Informations de publication
Date de publication:
09 2020
09 2020
Historique:
received:
11
11
2019
revised:
09
06
2020
accepted:
14
06
2020
pubmed:
25
7
2020
medline:
4
5
2021
entrez:
25
7
2020
Statut:
ppublish
Résumé
The robust identification of initial contact (IC) and toe-off (TO) events is a vital task in mobile sensor-based gait analysis. Shank attached gyroscopes in combination with suitable algorithms for data processing can robustly and accurately complete this task for gait event detection. However, little research has considered gait detection algorithms that are applicable to different locomotion tasks. Does a gait event detection algorithm for various locomotion tasks provide comparable estimation accuracies as existing task-specific algorithms? Thirteen males, equipped with a gyroscope attached to the right shank, volunteered to perform nine different locomotion tasks consisting of linear movements and movements with a change of direction. A rule-based algorithm for IC and TO events was developed based on the shank sagittal plane angular velocity. The algorithm was evaluated against events determined by vertical ground reaction force. Absolute mean error (AME), relative absolute mean error (RAME) and Bland-Altman analysis was used to assess its accuracy. The average AME and RAME were 11 ± 3 ms and 3.07 ± 1.33 %, respectively, for IC and 29 ± 11 ms and 7.27 ± 2.92 %, respectively, for TO. Alterations of the walking movement, such as turns and types of running, slightly reduced the accuracy of IC and TO detection. In comparison to previous methods, increased or comparable accuracies for both IC and TO detection are shown. The study shows that the proposed algorithm is capable of detecting gait events for a variety of locomotion tasks by means of a single gyroscope located on the shank. In consequence, the algorithm can be applied to activities, which consist of various movements (e.g., soccer). Ultimately, this extends the use of mobile sensor-based gait analysis.
Sections du résumé
BACKGROUND
The robust identification of initial contact (IC) and toe-off (TO) events is a vital task in mobile sensor-based gait analysis. Shank attached gyroscopes in combination with suitable algorithms for data processing can robustly and accurately complete this task for gait event detection. However, little research has considered gait detection algorithms that are applicable to different locomotion tasks.
RESEARCH QUESTION
Does a gait event detection algorithm for various locomotion tasks provide comparable estimation accuracies as existing task-specific algorithms?
METHODS
Thirteen males, equipped with a gyroscope attached to the right shank, volunteered to perform nine different locomotion tasks consisting of linear movements and movements with a change of direction. A rule-based algorithm for IC and TO events was developed based on the shank sagittal plane angular velocity. The algorithm was evaluated against events determined by vertical ground reaction force. Absolute mean error (AME), relative absolute mean error (RAME) and Bland-Altman analysis was used to assess its accuracy.
RESULTS
The average AME and RAME were 11 ± 3 ms and 3.07 ± 1.33 %, respectively, for IC and 29 ± 11 ms and 7.27 ± 2.92 %, respectively, for TO. Alterations of the walking movement, such as turns and types of running, slightly reduced the accuracy of IC and TO detection. In comparison to previous methods, increased or comparable accuracies for both IC and TO detection are shown.
SIGNIFICANCE
The study shows that the proposed algorithm is capable of detecting gait events for a variety of locomotion tasks by means of a single gyroscope located on the shank. In consequence, the algorithm can be applied to activities, which consist of various movements (e.g., soccer). Ultimately, this extends the use of mobile sensor-based gait analysis.
Identifiants
pubmed: 32707401
pii: S0966-6362(20)30221-6
doi: 10.1016/j.gaitpost.2020.06.019
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
102-108Informations de copyright
Copyright © 2020 Elsevier B.V. All rights reserved.