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A Pose-Based Feature Fusion and Classification Framework for the Early Prediction of Cerebral Palsy in Infants

Kevin D. McCay, Pengpeng Hu, Hubert P. H. Shum, Wai Lok Woo, Claire Marcroft, Nicholas D. Embleton, Adrian Munteanu and Edmond S. L. Ho
IEEE Transactions on Neural Systems and Rehabilitation Engineering (TNSRE), 2022

 Impact Factor: 4.9 Citation: 17#

A Pose-Based Feature Fusion and Classification Framework for the Early Prediction of Cerebral Palsy in Infants
# According to Google Scholar 2023"

Abstract

The early diagnosis of cerebral palsy is an area which has recently seen significant multi-disciplinary research. Diagnostic tools such as the General Movements Assessment (GMA), have produced some very promising results. However, the prospect of automating these processes may improve accessibility of the assessment and also enhance the understanding of movement development of infants. Previous works have established the viability of using pose-based features extracted from RGB video sequences to undertake classification of infant body movements based upon the GMA. In this paper, we propose a series of new and improved features, and a feature fusion pipeline for this classification task. We also introduce the RVI-38 dataset, a series of videos captured as part of routine clinical care. By utilising this challenging dataset we establish the robustness of several motion features for classification, subsequently informing the design of our proposed feature fusion framework based upon the GMA. We evaluate our proposed framework’s classification performance using both the RVI-38 dataset and the publicly available MINI-RGBD dataset. We also implement several other methods from the literature for direct comparison using these two independent datasets. Our experimental results and feature analysis show that our proposed pose-based method performs well across both datasets. The proposed features afford us the opportunity to include finer detail than previous methods, and further model GMA specific body movements. These new features also allow us to take advantage of additional body-part specific information as a means of improving the overall classification performance, whilst retaining GMA relevant, interpretable, and shareable features.

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Citations

BibTeX

@article{mccay22posebased,
 author={McCay, Kevin D. and Hu, Pengpeng and Shum, Hubert P. H. and Woo, Wai Lok and Marcroft, Claire and Embleton, Nicholas D. and Munteanu, Adrian and Ho, Edmond S. L.},
 journal={IEEE Transactions on Neural Systems and Rehabilitation Engineering},
 title={A Pose-Based Feature Fusion and Classification Framework for the Early Prediction of Cerebral Palsy in Infants},
 year={2022},
 volume={30},
 pages={8--19},
 numpages={12},
 doi={10.1109/TNSRE.2021.3138185},
 issn={1534-4320 },
 publisher={IEEE},
}

RIS

TY  - JOUR
AU  - McCay, Kevin D.
AU  - Hu, Pengpeng
AU  - Shum, Hubert P. H.
AU  - Woo, Wai Lok
AU  - Marcroft, Claire
AU  - Embleton, Nicholas D.
AU  - Munteanu, Adrian
AU  - Ho, Edmond S. L.
T2  - IEEE Transactions on Neural Systems and Rehabilitation Engineering
TI  - A Pose-Based Feature Fusion and Classification Framework for the Early Prediction of Cerebral Palsy in Infants
PY  - 2022
VL  - 30
SP  - 8
EP  - 19
DO  - 10.1109/TNSRE.2021.3138185
SN  - 1534-4320
PB  - IEEE
ER  - 

Plain Text

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, vol. 30, pp. 8-19, IEEE, 2022.

Supporting Grants

The Royal Society
Autonomous Monitoring for Patients and Older People using Smart Environments with Sensor Fusion
Royal Society International Exchanges (Ref: IES\R1\191147): £11,940, Co-Applicant (PI: Dr Edmond S. L. Ho)
Received from The Royal Society, UK, 2019-2021
Project Page

Similar Research

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, 2020
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
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 International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 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
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
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
Haozheng Zhang, Edmond S. L. Ho, Francis Xiatian Zhang and Hubert P. H. Shum, "Pose-Based Tremor Classification for Parkinson’s Disease Diagnosis from Video", Proceedings of the 2022 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2022
Manli Zhu, Qianhui Men, Edmond S. L. Ho, Howard Leung and Hubert P. H. Shum, "A Two-Stream Convolutional Network for Musculoskeletal and Neurological Disorders Prediction", Journal of Medical Systems (JMS), 2022

 

 

Last updated on 17 February 2024
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