Abnormal Infant Movements Classification with Deep Learning on Pose-based Features

kevin d. mccay, edmond s. l. ho, hubert p. h. shum, gerhard fehringer, claire marcroft and nicholas embleton
IEEE Access, 2020

Impact Factor: 3.476# Citation: 36## REF 2021 Submission

Abnormal Infant Movements Classification with Deep Learning on Pose-based Features
# Impact factors from the Journal Citation Reports 2021
## Citation counts from Google Scholar as of 2022

Abstract

The pursuit of early diagnosis of cerebral palsy has been an active research area with some very promising results using tools such as the General Movements Assessment (GMA). In our previous work, we explored the feasibility of extracting pose-based features from video sequences to automatically classify infant body movement into two categories, normal and abnormal. The classification was based upon the GMA, which was carried out on the video data by an independent expert reviewer. In this paper we extend our previous work by extracting the normalised pose-based feature sets, Histograms of Joint Orientation 2D (HOJO2D) and Histograms of Joint Displacement 2D (HOJD2D), for use in new deep learning architectures. We explore the viability of using these pose-based feature sets for automated classification within a deep learning framework by carrying out extensive experiments on five new deep learning architectures. Experimental results show that the proposed fully connected neural network FCNet performed robustly across different feature sets. Furthermore, the proposed convolutional neural network architectures demonstrated excellent performance in handling features in higher dimensionality. We make the code, extracted features and associated GMA labels publicly available.

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BibTeX

@article{mccay20abnormal,
 author={McCay, Kevin D. and Ho, Edmond S. L. and Shum, Hubert P. H. and Fehringer, Gerhard and Marcroft, Claire and Embleton, Nicholas},
 journal={IEEE Access},
 title={Abnormal Infant Movements Classification with Deep Learning on Pose-based Features},
 year={2020},
 volume={8},
 number={1},
 pages={51582--51592},
 numpages={11},
 doi={10.1109/ACCESS.2020.2980269},
 issn={2169-3536},
 publisher={IEEE},
}

RIS

TY  - JOUR
AU  - McCay, Kevin D.
AU  - Ho, Edmond S. L.
AU  - Shum, Hubert P. H.
AU  - Fehringer, Gerhard
AU  - Marcroft, Claire
AU  - Embleton, Nicholas
T2  - IEEE Access
TI  - Abnormal Infant Movements Classification with Deep Learning on Pose-based Features
PY  - 2020
VL  - 8
IS  - 1
SP  - 51582
EP  - 51592
DO  - 10.1109/ACCESS.2020.2980269
SN  - 2169-3536
PB  - IEEE
ER  - 

Plain Text

Kevin D. McCay, Edmond S. L. Ho, Hubert P. H. Shum, Gerhard Fehringer, Claire Marcroft and Nicholas Embleton, "Abnormal Infant Movements Classification with Deep Learning on Pose-based Features," IEEE Access, vol. 8, no. 1, pp. 51582-51592, IEEE, 2020.

Similar Research

Kevin D. McCay, Pengpeng Hu, Hubert P. H. Shum, Wai Lok Woo, Claire Marcroft, Nicholas D. Embleton, Adrian Munteanu and Edmond S. L. Ho, "A Pose-based Feature Fusion and Classification Framework for the Early Prediction of Cerebral Palsy in Infants", IEEE Transactions on Neural Systems and Rehabilitation Engineering (TNSRE), 2022
Haozheng Zhang, Edmond S. L. Ho and Hubert P. H. Shum, "CP-AGCN: Pytorch-based Attention Informed Graph Convolutional Network for Identifying Infants at Risk of Cerebral Palsy", Software Impacts (SIMPAC), 2022
Worasak Rueangsirarak, Jingtian Zhang, Nauman Aslam and Hubert P. H. Shum, "Automatic Musculoskeletal and Neurological Disorder Diagnosis with Relative Joint Displacement from Human Gait", IEEE Transactions on Neural Systems and Rehabilitation Engineering (TNSRE), 2018
Manli Zhu, Qianhui Men, Edmond S. L. Ho, Howard Leung and Hubert P. H. Shum, "Interpreting Deep Learning based Cerebral Palsy Prediction with Channel Attention", Proceedings of the 2021 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), 2021
Haozheng Zhang, Hubert P. H. Shum and Edmond S. L. Ho, "Cerebral Palsy Prediction with Frequency Attention Informed Graph Convolutional Networks", Proceedings of the 2022 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2022
Worasak Rueangsirarak, Kitchana Kaewkaen and Hubert P. H. Shum, "Identifying Abnormal Gait in Older People during Multiple-Tasks Assessment with Audio-Visual Cues", Proceedings of the 2018 International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON 2018), 2018

 

 
 

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