Recognition of Gait Disorders using Deep Learning Approaches

a 2022

Recognition of Gait Disorders using Deep Learning Approaches

SEDMIDUBSKÝ, Jan, Karel URBÁŠEK and Jakub RAČEK

Basic information

Original name

Recognition of Gait Disorders using Deep Learning Approaches

Authors

SEDMIDUBSKÝ, Jan (203 Czech Republic, guarantor, belonging to the institution), Karel URBÁŠEK (203 Czech Republic, belonging to the institution) and Jakub RAČEK (703 Slovakia, belonging to the institution)

Edition

European Society for Movement Analysis in Adults and Children (ESMAC), 2022

Other information

Language

English

Type of outcome

Konferenční abstrakt

Field of Study

10200 1.2 Computer and information sciences

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

URL

RIV identification code

RIV/00216224:14330/22:00126334

Organization unit

Faculty of Informatics

DOI

http://dx.doi.org/10.1016/j.gaitpost.2022.07.123

Keywords in English

human motion data;gait recognition;normalization;augmentation;LSTM

Abstract

V originále

Introduction. We anticipate that the rapid development of deep-learning technologies will significantly influence the process of gait analysis in the future. In this work, we investigated the suitability of current deep-learning classifiers to the problem of recognition of gait disorders. Research Question. What is the most suitable approach to distinguish between two different musculoskeletal disorders (Legg Calvé Perthes Disease – LCPD and Cerebral Palsy – CP) based on various data-transformation techniques and deep-learning classifiers applied to kinematic/kinetic data from instrumented gait analysis? Methods. A total number of 1,870 gait cycles for 80 LCPD and 82 CP patients were used for analysis. Gait cycles were randomly split into halves in a balanced way to determine the training and test sets of patients. Both kinematics and kinetics were used – a single gait cycle was described by 21 parameters for each side of the lower body (x/y/z angles for pelvis, hip, knee, and ankle; and x/y/z moments for hip, knee, and ankle). Such gait-cycle data were further transformed into various representations by normalizing the temporal dimension or swapping the left and right legs. Three types of deep-learning classifiers (auto-encoder, convolutional network, and recurrent network in other three configurations with GRU, LSTM, and bi-LSTM cells) were trained on such transformed representations to recognize the movement disorder on the level of individual gait cycles. To recognize the disorder on the level of patients, the majority-voting principle was applied to a set of gait cycles that reflect a person’s movement pattern. Results. The best results were achieved by the combination of temporal normalization of gait cycles, data augmentation by mirroring the representations of the left and right leg, and the application of the LSTM-based neural network classifier. In particular, auto-encoder, convolutional network, and GRU, LSTM and bi-LSTM configurations of a recurrent network recognized the patient’s disease with the accuracy of ~81%, ~92%, ~88%, ~94%, and ~94%, respectively. The classification accuracy decreased by 5 percentage points if it was simply evaluated on the level of individual gait cycles, i.e., without the application of the majority-voting principle on the level of patients. The accuracy further decreased by 3 percentage points if the training set was not augmented using the leg-mirroring approach. Discussion. The main contribution to existing approaches was the combination of both kinematic and kinetic gait parameters and the application of deep-learning classifiers, in contrast to traditionally used SVM or PCA methods.

Links

MUNI/G/1585/2019, interní kód MU

Name: Improving Treatments in Cerebral-Palsy Children using Artificial Intelligence

Investor: Masaryk University, INTERDISCIPLINARY - Interdisciplinary research projects

Citovat

SEDMIDUBSKÝ, Jan, Karel URBÁŠEK and Jakub RAČEK. Recognition of Gait Disorders using Deep Learning Approaches. In European Society for Movement Analysis in Adults and Children (ESMAC). 2022. Available from: https://dx.doi.org/10.1016/j.gaitpost.2022.07.123.

@proceedings{1882889,
   author = {Sedmidubský, Jan and Urbášek, Karel and Raček, Jakub},
   booktitle = {European Society for Movement Analysis in Adults and Children (ESMAC)},
   doi = {http://dx.doi.org/10.1016/j.gaitpost.2022.07.123},
   keywords = {human motion data;gait recognition;normalization;augmentation;LSTM},
   language = {eng},
   title = {Recognition of Gait Disorders using Deep Learning Approaches},
   url = {https://www.sciencedirect.com/science/article/pii/S096663622200323X},
   year = {2022}
}

TY  - CONF
ID  - 1882889
AU  - Sedmidubský, Jan - Urbášek, Karel - Raček, Jakub
PY  - 2022
TI  - Recognition of Gait Disorders using Deep Learning Approaches
KW  - human motion data;gait recognition;normalization;augmentation;LSTM
UR  - https://www.sciencedirect.com/science/article/pii/S096663622200323X
N2  - Introduction. We anticipate that the rapid development of deep-learning technologies will significantly influence the process of gait analysis in the future. In this work, we investigated the suitability of current deep-learning classifiers to the problem of recognition of gait disorders. Research Question. What is the most suitable approach to distinguish between two different musculoskeletal disorders (Legg Calvé Perthes Disease – LCPD and Cerebral Palsy – CP) based on various data-transformation techniques and deep-learning classifiers applied to kinematic/kinetic data from instrumented gait analysis? Methods. A total number of 1,870 gait cycles for 80 LCPD and 82 CP patients were used for analysis. Gait cycles were randomly split into halves in a balanced way to determine the training and test sets of patients. Both kinematics and kinetics were used – a single gait cycle was described by 21 parameters for each side of the lower body (x/y/z angles for pelvis, hip, knee, and ankle; and x/y/z moments for hip, knee, and ankle). Such gait-cycle data were further transformed into various representations by normalizing the temporal dimension or swapping the left and right legs. Three types of deep-learning classifiers (auto-encoder, convolutional network, and recurrent network in other three configurations with GRU, LSTM, and bi-LSTM cells) were trained on such transformed representations to recognize the movement disorder on the level of individual gait cycles. To recognize the disorder on the level of patients, the majority-voting principle was applied to a set of gait cycles that reflect a person’s movement pattern. Results. The best results were achieved by the combination of temporal normalization of gait cycles, data augmentation by mirroring the representations of the left and right leg, and the application of the LSTM-based neural network classifier. In particular, auto-encoder, convolutional network, and GRU, LSTM and bi-LSTM configurations of a recurrent network recognized the patient’s disease with the accuracy of ~81%, ~92%, ~88%, ~94%, and ~94%, respectively. The classification accuracy decreased by 5 percentage points if it was simply evaluated on the level of individual gait cycles, i.e., without the application of the majority-voting principle on the level of patients. The accuracy further decreased by 3 percentage points if the training set was not augmented using the leg-mirroring approach. Discussion. The main contribution to existing approaches was the combination of both kinematic and kinetic gait parameters and the application of deep-learning classifiers, in contrast to traditionally used SVM or PCA methods.
ER  -

SEDMIDUBSKÝ, Jan, Karel URBÁŠEK and Jakub RAČEK. Recognition of Gait Disorders using Deep Learning Approaches. In \textit{European Society for Movement Analysis in Adults and Children (ESMAC)}. 2022. Available from: https://dx.doi.org/10.1016/j.gaitpost.2022.07.123.

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