PMID- 37430892
OWN - NLM
STAT- MEDLINE
DCOM- 20230712
LR  - 20230718
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 23
IP  - 10
DP  - 2023 May 22
TI  - WM-STGCN: A Novel Spatiotemporal Modeling Method for Parkinsonian Gait 
      Recognition.
LID - 10.3390/s23104980 [doi]
LID - 4980
AB  - Parkinson's disease (PD) is a neurodegenerative disorder that causes gait 
      abnormalities. Early and accurate recognition of PD gait is crucial for effective 
      treatment. Recently, deep learning techniques have shown promising results in PD 
      gait analysis. However, most existing methods focus on severity estimation and 
      frozen gait detection, while the recognition of Parkinsonian gait and normal gait 
      from the forward video has not been reported. In this paper, we propose a novel 
      spatiotemporal modeling method for PD gait recognition, named WM-STGCN, which 
      utilizes a Weighted adjacency matrix with virtual connection and Multi-scale 
      temporal convolution in a Spatiotemporal Graph Convolution Network. The weighted 
      matrix enables different intensities to be assigned to different spatial 
      features, including virtual connections, while the multi-scale temporal 
      convolution helps to effectively capture the temporal features at different 
      scales. Moreover, we employ various approaches to augment skeleton data. 
      Experimental results show that our proposed method achieved the best accuracy of 
      87.1% and an F1 score of 92.85%, outperforming Long short-term memory (LSTM), 
      K-nearest neighbors (KNN), Decision tree, AdaBoost, and ST-GCN models. Our 
      proposed WM-STGCN provides an effective spatiotemporal modeling method for PD 
      gait recognition that outperforms existing methods. It has the potential for 
      clinical application in PD diagnosis and treatment.
FAU - Zhang, Jieming
AU  - Zhang J
AUID- ORCID: 0009-0007-8796-6316
AD  - Department of Computer Science and Engineering, Sungkyunkwan University, Suwon 
      16419, Republic of Korea.
FAU - Lim, Jongmin
AU  - Lim J
AUID- ORCID: 0000-0002-1179-3996
AD  - Department of Computer Science and Engineering, Sungkyunkwan University, Suwon 
      16419, Republic of Korea.
FAU - Kim, Moon-Hyun
AU  - Kim MH
AD  - Hippo T&C Inc., Suwon 16419, Republic of Korea.
FAU - Hur, Sungwook
AU  - Hur S
AD  - Department of Computer Science and Engineering, Sungkyunkwan University, Suwon 
      16419, Republic of Korea.
FAU - Chung, Tai-Myoung
AU  - Chung TM
AD  - Department of Computer Science and Engineering, Sungkyunkwan University, Suwon 
      16419, Republic of Korea.
LA  - eng
GR  - 2020-0-00990/Institute of Information & communications Technology Planning & 
      Evaluation/
PT  - Journal Article
DEP - 20230522
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
SB  - IM
MH  - Humans
MH  - *Gait
MH  - *Parkinson Disease/diagnosis
MH  - Gait Analysis
MH  - Cluster Analysis
MH  - Memory, Long-Term
PMC - PMC10223022
OTO - NOTNLM
OT  - Parkinson's disease
OT  - gait recognition
OT  - graph convolution network
COIS- The authors declare no conflict of interest.
EDAT- 2023/07/11 06:42
MHDA- 2023/07/12 06:42
PMCR- 2023/05/22
CRDT- 2023/07/11 01:03
PHST- 2023/04/04 00:00 [received]
PHST- 2023/05/12 00:00 [revised]
PHST- 2023/05/19 00:00 [accepted]
PHST- 2023/07/12 06:42 [medline]
PHST- 2023/07/11 06:42 [pubmed]
PHST- 2023/07/11 01:03 [entrez]
PHST- 2023/05/22 00:00 [pmc-release]
AID - s23104980 [pii]
AID - sensors-23-04980 [pii]
AID - 10.3390/s23104980 [doi]
PST - epublish
SO  - Sensors (Basel). 2023 May 22;23(10):4980. doi: 10.3390/s23104980.