Title: A multi-modal deep learning model based automatic Wisconsin Gait Scale scoring system for stroke patients

Abstract: Gait impairments are a common consequence of stroke and assessing gait patterns is crucial for developing personalized rehabilitation programs. The Wisconsin Gait Scale (WGS) is an effective score criterion for evaluating gait quality in stroke patients. However, the manual scoring process is labor-intensive and often subject to biases due to the varying experience levels of the clinic practitioners. Deep learning techniques offer a potential solution to this challenging difficulty. We propose STAT-Net, a novel multi-modal deep learning model inspired by biomechanical properties and comprehensive insights for automatic WGS scoring based on skeleton data collected by the Inertial Measurement Unit (IMU). We also compare the performance of different models on the task to show the superiority of our method.

Prof. Yancheng Yuan is an assistant professor in the Department of Applied Mathematics at the Hong Kong Polytechnic University. His primary research interests lie in the theoretical and applied aspects of continuous optimization and machine learning. He has published papers in prestigious journals such as the "SIAM Journal on Optimization," "Journal of Machine Learning Research," and "IEEE Transactions on Neural Networks and Learning Systems", as well as presented at top academic conferences in the field of machine learning such as ICML, NeurIPS, SIGIR, and WWW. He has received the Best Paper Award Finalist (WWW 2021).

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Prof. Xin Wang obtained his PhD in 2014 from the University of Cambridge Department of Oncology and Cancer Research UK Cambridge Institute. From 2013 to 2015, Prof. Wang did his postdoctoral research at the Department of Biomedical Informatics, Harvard Medical School. He joined City University of Hong Kong in March 2015 and was promoted to Associate Professor and Associate Head at the Department of Biomedical Sciences. In 2021, he joined the Department of Surgery, Chinese University of Hong Kong, as an Associate Professor. He is currently also a Guest Associate Professor at the West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University. His major research field is Biomedical Informatics with a special focus on Cancer Bioinformatics.

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Dr. Zhen Li is currently an assistant professor at the School of Science and Engineering (SSE) of The Chinese University of Hong Kong, Shenzhen/Future Intelligent Network Research Institute (FNii) of The Chinese University of Hong Kong, Shenzhen. He is also a research scientist at the Shenzhen Institute of Big Data (SRIBD) and a special researcher at the South China Hospital Affiliated to Shenzhen University. Dr. Li Zhen was selected for the 2021-2023 Seventh China Association for Science and Technology Young Talent Support Project. Dr. Zhen Li received his PhD in Computer Science from the University of Hong Kong (2014-2018), a MS in Communication and Information Systems from Sun Yat-Sen University (2011-2014), and a BS in Automation from Sun Yat-Sen University (2007-2011). He was also a visiting scholar at the University of Chicago in 2018 and a visiting student at the Toyota Technical Institute (TTIC) in Chicago in 2016. His research interests include interdisciplinary research in artificial intelligence, 3D vision, computer vision, and deep learning-assisted medical big data analysis. He has published more than 30 papers in top conferences and journals, such as top journals Cell Systems and Nature Communications, IEEE TNNLS, IEEE TMI, PLOS CB, etc. and top conferences CVPR, ICCV, ECCV, AAAI, IJCAI, ACL, ECAI, MICCAI, RECOMB, ISBI, etc.

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Prof. Yifan Peng is an assistant professor in the division of Health Sciences at Weill Cornell Medicine. His main research interests include BioNLP and medical image analysis, such as named entity recognition, information extraction, and disease diagnosis and prognosis. Before joining Cornell Medicine, He was a research fellow at the National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH). He obtained his Ph.D. degree from the University of Delaware. During his doctoral training, he investigated applications of machine learning in biomedical relation extraction, with a focus on deep analysis of the linguistic structures of biomedical texts. He is the first awardee at the NCBI to receive the NIH K99/R00 grant, which supports his work on using NLP and ML to extract radiology specific domain knowledge and build an automated reporting system.