Individuality-Preserving Gait Pattern Prediction Based on Gait Feature Transitions

Tsuyoshi HIGASHIGUCHI; Norimichi UKITA; Masayuki KANBARA; Norihiro HAGITA

doi:10.1587/transinf.2018EDP7082

Individuality-Preserving Gait Pattern Prediction Based on Gait Feature Transitions

Tsuyoshi HIGASHIGUCHI, Norimichi UKITA, Masayuki KANBARA, Norihiro HAGITA

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This paper proposes a method for predicting individuality-preserving gait patterns. Physical rehabilitation can be performed using visual and/or physical instructions by physiotherapists or exoskeletal robots. However, a template-based rehabilitation may produce discomfort and pain in a patient because of deviations from the natural gait of each patient. Our work addresses this problem by predicting an individuality-preserving gait pattern for each patient. In this prediction, the transition of the gait patterns is modeled by associating the sequence of a 3D skeleton in gait with its continuous-value gait features (e.g., walking speed or step width). In the space of the prediction model, the arrangement of the gait patterns are optimized so that (1) similar gait patterns are close to each other and (2) the gait feature changes smoothly between neighboring gait patterns. This model allows to predict individuality-preserving gait patterns of each patient even if his/her various gait patterns are not available for prediction. The effectiveness of the proposed method is demonstrated quantitatively. with two datasets.

Publication: IEICE TRANSACTIONS on Information Vol.E101-D No.10 pp.2501-2508

Publication Date: 2018/10/01

Publicized: 2018/07/20

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2018EDP7082

Type of Manuscript: PAPER

Category: Pattern Recognition

Authors

Tsuyoshi HIGASHIGUCHI
  Nara Institute of Science and Technology
Norimichi UKITA
  Toyota Technological Institute
Masayuki KANBARA
  Nara Institute of Science and Technology
Norihiro HAGITA
  Nara Institute of Science and Technology

Keyword

gait, human skeleton, individuality-preserving prediction

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Tsuyoshi HIGASHIGUCHI, Norimichi UKITA, Masayuki KANBARA, Norihiro HAGITA, "Individuality-Preserving Gait Pattern Prediction Based on Gait Feature Transitions" in IEICE TRANSACTIONS on Information, vol. E101-D, no. 10, pp. 2501-2508, October 2018, doi: 10.1587/transinf.2018EDP7082.
Abstract: This paper proposes a method for predicting individuality-preserving gait patterns. Physical rehabilitation can be performed using visual and/or physical instructions by physiotherapists or exoskeletal robots. However, a template-based rehabilitation may produce discomfort and pain in a patient because of deviations from the natural gait of each patient. Our work addresses this problem by predicting an individuality-preserving gait pattern for each patient. In this prediction, the transition of the gait patterns is modeled by associating the sequence of a 3D skeleton in gait with its continuous-value gait features (e.g., walking speed or step width). In the space of the prediction model, the arrangement of the gait patterns are optimized so that (1) similar gait patterns are close to each other and (2) the gait feature changes smoothly between neighboring gait patterns. This model allows to predict individuality-preserving gait patterns of each patient even if his/her various gait patterns are not available for prediction. The effectiveness of the proposed method is demonstrated quantitatively. with two datasets.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDP7082/_p

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@ARTICLE{e101-d_10_2501,
author={Tsuyoshi HIGASHIGUCHI, Norimichi UKITA, Masayuki KANBARA, Norihiro HAGITA, },
journal={IEICE TRANSACTIONS on Information},
title={Individuality-Preserving Gait Pattern Prediction Based on Gait Feature Transitions},
year={2018},
volume={E101-D},
number={10},
pages={2501-2508},
abstract={This paper proposes a method for predicting individuality-preserving gait patterns. Physical rehabilitation can be performed using visual and/or physical instructions by physiotherapists or exoskeletal robots. However, a template-based rehabilitation may produce discomfort and pain in a patient because of deviations from the natural gait of each patient. Our work addresses this problem by predicting an individuality-preserving gait pattern for each patient. In this prediction, the transition of the gait patterns is modeled by associating the sequence of a 3D skeleton in gait with its continuous-value gait features (e.g., walking speed or step width). In the space of the prediction model, the arrangement of the gait patterns are optimized so that (1) similar gait patterns are close to each other and (2) the gait feature changes smoothly between neighboring gait patterns. This model allows to predict individuality-preserving gait patterns of each patient even if his/her various gait patterns are not available for prediction. The effectiveness of the proposed method is demonstrated quantitatively. with two datasets.},
keywords={},
doi={10.1587/transinf.2018EDP7082},
ISSN={1745-1361},
month={October},}

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TY - JOUR
TI - Individuality-Preserving Gait Pattern Prediction Based on Gait Feature Transitions
T2 - IEICE TRANSACTIONS on Information
SP - 2501
EP - 2508
AU - Tsuyoshi HIGASHIGUCHI
AU - Norimichi UKITA
AU - Masayuki KANBARA
AU - Norihiro HAGITA
PY - 2018
DO - 10.1587/transinf.2018EDP7082
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2018
AB - This paper proposes a method for predicting individuality-preserving gait patterns. Physical rehabilitation can be performed using visual and/or physical instructions by physiotherapists or exoskeletal robots. However, a template-based rehabilitation may produce discomfort and pain in a patient because of deviations from the natural gait of each patient. Our work addresses this problem by predicting an individuality-preserving gait pattern for each patient. In this prediction, the transition of the gait patterns is modeled by associating the sequence of a 3D skeleton in gait with its continuous-value gait features (e.g., walking speed or step width). In the space of the prediction model, the arrangement of the gait patterns are optimized so that (1) similar gait patterns are close to each other and (2) the gait feature changes smoothly between neighboring gait patterns. This model allows to predict individuality-preserving gait patterns of each patient even if his/her various gait patterns are not available for prediction. The effectiveness of the proposed method is demonstrated quantitatively. with two datasets.
ER -