About the Project
Cerebral palsy (CP) is the collective term given to a group lifelong neurological conditions and the most prevalent physical disability found in children, with 2.11 diagnoses per 1000 live births. There is also an increased prevalence of CP in infants born prematurely, with 32.4 diagnoses per 1000 infants born very preterm (28-32 weeks gestation), and 70.6 diagnoses per 1000 infants born extremely preterm (<28 weeks gestation).
As such, the early diagnosis of CP is an ongoing area of multidisciplinary research, as it has the potential to allow for early intervention clinical care. However, early diagnosis can be difficult and time-consuming. Diagnostic tools such as the General Movements Assessment (GMA), have produced some very promising results. However, the prospect of automating these processes may improve the accessibility of the assessment and also enhance the understanding of the movement development of infants.
In this research project, we aim at proposing new machine learning based framework for early prediction of CP. We have been working closely with local hospitals such as NHS Royal Victoria Infirmary and real-world data will be used under the ethical approval “Pilot study into Human Activity Recognition and Classification Techniques for the Early Detection of Movement Difficulties in Infants” (IRAS project ID: 252317, REC reference: 19/LO/0606). With our interdisciplinary research team (established in 2018), we have published our research outcomes in top-tier venues, such as
K. D. McCay et al., "A Pose-based Feature Fusion and Classification Framework for the Early Prediction of Cerebral Palsy in Infants," in IEEE Transactions on Neural Systems and Rehabilitation Engineering, doi: 10.1109/TNSRE.2021.3138185, Dec 2021.
In particular, this project will focus on modelling the multimodal data collected from our NHS partners to improve the robustness and accuracy of the early prediction of CP. Other projects in the relevant areas will also be considered, see http://www.edho.net/projects/index.html
For informal enquiries please contact [Email Address Removed]
Eligibility and How to Apply:
Please note eligibility requirement:
· Academic excellence of the proposed student i.e. 2:1 (or equivalent GPA from non-UK universities [preference for 1st class honours]); or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement.
· Appropriate IELTS score, if required.
For further details of how to apply, entry requirements and the application form, see
https://www.northumbria.ac.uk/research/postgraduate-research-degrees/how-to-apply/
Please note: Applications that do not include a research proposal of approximately 1,000 words (not a copy of the advert), or that do not include the advert reference (e.g. SF22/…) will not be considered.
Start Date: 1 October 2022
References
1. 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, accepted, 2022.
2. Pengpeng Hu, Edmond S. L. Ho and Adrian Munteanu, "3DBodyNet: Fast Reconstruction of 3D Animatable Human Body Shape from a Single Commodity Depth Camera", IEEE Transactions on Multimedia, accepted, 2022.
3. Dipanwita Thakur, Suparna Biswas, Edmond S. L. Ho and Samiran Chattopadhyay, "ConvAE-LSTM: Convolutional Autoencoder Long Short-Term Memory Network for Smartphone-Based Human Activity Recognition", IEEE Access, accepted, 2022.
4. Mohamed Hammad, Abdullah M Iliyasu, Abdulhamit Subasi, Edmond S. L. Ho and Ahmed A. Abd El-Latif, "A Multi-tier Deep Learning Model for Arrhythmia Detection", IEEE Transactions on Instrumentation & Measurement, vol 70, pp. 1-9, 2021.
5. Qianhui Men, Edmond S. L. Ho, Hubert P. H. Shum and Howard Leung, "A Quadruple Diffusion Convolutional Recurrent Network for Human Motion Prediction", IEEE Transactions on Circuits and Systems for Video Technology, vol 31(9), pp. 3417-3432, Sept 2021.
6. Dimitrios Sakkos, Kevin D. McCay, Claire Marcroft, Nicholas D. Embleton, Samiran Chattopadhyay and Edmond S. L. Ho, "Identification of Abnormal Movements in Infants: A Deep Neural Network for Body Part-based Prediction of Cerebral Palsy", IEEE Access, vol. 9, pp. 94281-94292, June 2021.
7. He Wang, Edmond S. L. Ho, Hubert P. H. Shum and Zhanxing Zhu, "Spatio-temporal Manifold Learning for Human Motions via Long-horizon Modeling", IEEE Transactions on Visualization and Computer Graphics, vol 27(1), pp. 216-227, Jan 2021.
8. Yijun Shen, Longzhi Yang, Edmond S. L. Ho and Hubert P. H. Shum, "Interaction-based Human Activity Comparison", IEEE Transactions on Visualization and Computer Graphics, vol 26(8), pp. 2620-2633, Aug 2020.
9. Ying Huang, Hubert P. H. Shum, Edmond S. L. Ho and Nauman Aslam, "High-speed Multi-person Pose Estimation with Deep Feature Transfer", Computer Vision and Image Understanding, vol. 197-198, pp. 103010, Aug 2020.
10. Kevin McCay, Edmond S. L. Ho, Hubert P. H. Shum, Gerhard Fehringer, Claire Marcroft and Nicholas D. Embleton, "Abnormal Infant Movements Classification with Deep Learning on Pose-based Features", IEEE Access, vol. 8, pp. 51582-51592, March 2020.
11. Kangxue Yin, Hui Huang, Edmond S. L. Ho, Hao Wang, Taku Komura, Daniel Cohen-Or and Hao Zhang, "A Sampling Approach to Generating Closely Interacting 3D Pose-pairs from 2D Annotations", IEEE Transactions on Visualization and Computer Graphics, vol 25(6), pp. 2387-2396, June 2019.
12. Worasak Rueangsirarak, Jingtian Zhang, Nauman Aslam, Edmond S. L. Ho, 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, vol 26(12), pp. 2387-2396, Dec 2018.
13. Edmond S. L. Ho, Jacky C. P. Chan, Donald C. K. Chan, Hubert P. H. Shum, Yiu-ming Cheung, Pong C. Yuen, "Improving Posture Classification Accuracy for Depth Sensor-based Human Activity Monitoring in Smart Environments", Computer Vision and Image Understanding, vol 148, pp. 97-110, July 2016.
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