Recognition of Gait Disorders using Deep Learning Approaches

a 2022

Recognition of Gait Disorders using Deep Learning Approaches

SEDMIDUBSKÝ, Jan, Karel URBÁŠEK a Jakub RAČEK

Základní údaje

Originální název

Recognition of Gait Disorders using Deep Learning Approaches

Autoři

SEDMIDUBSKÝ, Jan (203 Česká republika, garant, domácí), Karel URBÁŠEK (203 Česká republika, domácí) a Jakub RAČEK (703 Slovensko, domácí)

Vydání

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

Další údaje

Jazyk

angličtina

Typ výsledku

Konferenční abstrakt

Obor

10200 1.2 Computer and information sciences

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Kód RIV

RIV/00216224:14330/22:00126334

Organizační jednotka

Fakulta informatiky

DOI

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

Klíčová slova anglicky

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

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 25. 1. 2023 15:52, doc. RNDr. Jan Sedmidubský, Ph.D.

Anotace

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.

Návaznosti

MUNI/G/1585/2019, interní kód MU

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

Investor: Masarykova univerzita, Improving Treatments in Cerebral-Palsy Children using Artificial Intelligence, INTERDISCIPLINARY - Mezioborové výzkumné projekty

Citovat

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

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