Gait phase classification for in-home gait assessment

Research output: Research - peer-reviewPaper

Standard

Gait phase classification for in-home gait assessment. / Ye, Minxiang; Yang, Cheng; Stankovic, Vladimir; Stankovic, Lina; Cheng, Samuel.

2017. 1-6 Paper presented at IEEE International Conference on Multimedia and Expo, Hong Kong, China.

Research output: Research - peer-reviewPaper

Harvard

Ye, M, Yang, C, Stankovic, V, Stankovic, L & Cheng, S 2017, 'Gait phase classification for in-home gait assessment' Paper presented at IEEE International Conference on Multimedia and Expo, Hong Kong, China, 10/07/17 - 14/07/17, pp. 1-6. DOI: 10.1109/ICME.2017.8019500

APA

Ye, M., Yang, C., Stankovic, V., Stankovic, L., & Cheng, S. (2017). Gait phase classification for in-home gait assessment. 1-6. Paper presented at IEEE International Conference on Multimedia and Expo, Hong Kong, China.DOI: 10.1109/ICME.2017.8019500

Vancouver

Ye M, Yang C, Stankovic V, Stankovic L, Cheng S. Gait phase classification for in-home gait assessment. 2017. Paper presented at IEEE International Conference on Multimedia and Expo, Hong Kong, China. Available from, DOI: 10.1109/ICME.2017.8019500

Author

Ye, Minxiang ; Yang, Cheng ; Stankovic, Vladimir ; Stankovic, Lina ; Cheng, Samuel. / Gait phase classification for in-home gait assessment. Paper presented at IEEE International Conference on Multimedia and Expo, Hong Kong, China.6 p.

BibTeX - Download

@conference{3c91071b92ec42bd95eca1579f2f81ef,
title = "Gait phase classification for in-home gait assessment",
abstract = "With growing ageing population, acquiring joint measurements with sufficient accuracy for reliable gait assessment is essential. Additionally, the quality of gait analysis relies heavily on accurate feature selection and classification. Sensor-driven and one-camera optical motion capture systems are becoming increasingly popular in the scientific literature due to their portability and cost-efficacy. In this paper, we propose 12 gait parameters to characterise gait patterns and a novel gait-phase classifier, resulting in comparable classification performance with a state-of-the-art multi-sensor optical motion system. Furthermore, a novel multi-channel time series segmentation method is proposed that maximizes the temporal information of gait parameters improving the final classification success rate after gait event reconstruction. The validation, conducted over 126 experiments on 6 healthy volunteers and 9 stroke patients with handlabelled ground truth gait phases, demonstrates high gait classification accuracy.",
keywords = "feature extraction, gait phase classification",
author = "Minxiang Ye and Cheng Yang and Vladimir Stankovic and Lina Stankovic and Samuel Cheng",
note = "© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.",
year = "2017",
month = "8",
doi = "10.1109/ICME.2017.8019500",
pages = "1--6",

}

RIS (suitable for import to EndNote) - Download

TY - CONF

T1 - Gait phase classification for in-home gait assessment

AU - Ye,Minxiang

AU - Yang,Cheng

AU - Stankovic,Vladimir

AU - Stankovic,Lina

AU - Cheng,Samuel

N1 - © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

PY - 2017/8/31

Y1 - 2017/8/31

N2 - With growing ageing population, acquiring joint measurements with sufficient accuracy for reliable gait assessment is essential. Additionally, the quality of gait analysis relies heavily on accurate feature selection and classification. Sensor-driven and one-camera optical motion capture systems are becoming increasingly popular in the scientific literature due to their portability and cost-efficacy. In this paper, we propose 12 gait parameters to characterise gait patterns and a novel gait-phase classifier, resulting in comparable classification performance with a state-of-the-art multi-sensor optical motion system. Furthermore, a novel multi-channel time series segmentation method is proposed that maximizes the temporal information of gait parameters improving the final classification success rate after gait event reconstruction. The validation, conducted over 126 experiments on 6 healthy volunteers and 9 stroke patients with handlabelled ground truth gait phases, demonstrates high gait classification accuracy.

AB - With growing ageing population, acquiring joint measurements with sufficient accuracy for reliable gait assessment is essential. Additionally, the quality of gait analysis relies heavily on accurate feature selection and classification. Sensor-driven and one-camera optical motion capture systems are becoming increasingly popular in the scientific literature due to their portability and cost-efficacy. In this paper, we propose 12 gait parameters to characterise gait patterns and a novel gait-phase classifier, resulting in comparable classification performance with a state-of-the-art multi-sensor optical motion system. Furthermore, a novel multi-channel time series segmentation method is proposed that maximizes the temporal information of gait parameters improving the final classification success rate after gait event reconstruction. The validation, conducted over 126 experiments on 6 healthy volunteers and 9 stroke patients with handlabelled ground truth gait phases, demonstrates high gait classification accuracy.

KW - feature extraction

KW - gait phase classification

U2 - 10.1109/ICME.2017.8019500

DO - 10.1109/ICME.2017.8019500

M3 - Paper

SP - 1

EP - 6

ER -

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