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

@article{McCay2022APF,
  title={A Pose-Based Feature Fusion and Classification Framework for the Early Prediction of Cerebral Palsy in Infants},
  author={Kevin D. McCay and Pengpeng Hu and Hubert P. H. Shum and Wai Lok Woo and Claire Marcroft and Nicholas D. Embleton and Adrian Munteanu and Edmond S. L. Ho},
  journal={IEEE Transactions on Neural Systems and Rehabilitation Engineering},
  year={2022},
  volume={30},
  pages={8-19}
}
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… 

Figures and Tables from this paper

Cerebral Palsy Prediction with Frequency Attention Informed Graph Convolutional Networks
TLDR
A frequency attention informed graph convolutional network is proposed and validates it on two consumer-grade RGB video datasets, namely MINI-RGBD and RVI-38 datasets and provides a way for supporting the early diagnosis of CP in the resource-limited regions where the clinical resources are not abundant.
Predicting Sleeping Quality using Convolutional Neural Networks
TLDR
A Convolution Neural Network (CNN) architecture that improves the classification performance and is benchmarked from different methods, including traditional machine learning methods such as Logistic Regression (LR), Decision Trees (DT), k-Nearest Neighbour (k-NN), Naïve Bayes (NB) and Support Vector Machine (SVM).

References

SHOWING 1-10 OF 65 REFERENCES
Identification of Abnormal Movements in Infants: A Deep Neural Network for Body Part-Based Prediction of Cerebral Palsy
TLDR
A new deep learning framework is proposed for the early diagnosis of cerebral palsy and a visualization framework which identifies body-parts with the greatest contribution towards a classification decision is proposed, which helps provide greater interpretability.
Abnormal Infant Movements Classification With Deep Learning on Pose-Based Features
TLDR
This paperextends the feasibility of extracting pose-based features from video sequences to automatically classify infant body movement into two categories, normal and abnormal, by carrying out extensive experiments on five new deep learning architectures.
Establishing Pose Based Features Using Histograms for the Detection of Abnormal Infant Movements
TLDR
A pilot study on extracting important information from video sequences to classify the body movement into two categories, normal and abnormal, and compared the results provided by an independent expert reviewer based on GMA is conducted.
RGB-D Videos-Based Early Prediction of Infant Cerebral Palsy via General Movements Complexity
TLDR
A novel method for early prediction of infant cerebral palsy based on General Movements Assessment (GMA) theory with RGB-D videos that achieved state-of-the-art with sensitivity of 100, specificity of 87.5%, and accuracy of 91.7%.
Towards Explainable Abnormal Infant Movements Identification: A Body-part Based Prediction and Visualisation Framework
TLDR
This paper proposes a new framework for the automated classification of infant body movements, based upon the General Movements Assessment, which unlike previous methods, also incorporates a visualization framework to aid with interpretability.
Computer Vision for Medical Infant Motion Analysis: State of the Art and RGB-D Data Set
TLDR
The Moving INfants In RGB-D (MINI-RGBD) data set is released, created using the recently introduced Skinned Multi-Infant Linear body model (SMIL) to foster research in medical infant motion analysis to get closer to an automated system for early detection of neurodevelopmental disorders.
Frequency Analysis and Feature Reduction Method for Prediction of Cerebral Palsy in Young Infants
TLDR
A feature selection method that determines features with significant predictive ability is proposed that decreases the risk of false discovery and, therefore, the prediction model is more likely to be valid and generalizable for future use.
A Spatio-Temporal Attention-Based Model for Infant Movement Assessment From Videos
TLDR
This work develops and validate a new method for fidgety movement assessment from consumer-grade videos using human pose extracted from short clips, significantly outperforming existing competing methods with better interpretability.
Interpreting Deep Learning based Cerebral Palsy Prediction with Channel Attention
TLDR
This paper proposes a channel attention module for deep learning models to predict cerebral palsy from infants’ body movements, which highlights the key features the model identifies as important, thereby indicating why certain diagnostic results are found.
Detection of Atypical and Typical Infant Movements using Computer-based Video Analysis
  • S. Orlandi, K. Raghuram, T. Chau
  • Medicine, Psychology
    2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
  • 2018
TLDR
A computerbased assessment would provide clinicians with an objective tool for early diagnosis of CP, to facilitate early intervention and improve functional outcomes.
...
...