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"AI for Parkinson's Disease – Accurate Detection of Freezing of
Gait with Wearable Sensor"
Professor Yuzhu Guo Beihang University, Beijing, China |
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Abstract: Parkinson’s diseases are the second prevalent neurodegenerative disease, and affects more than sixteen million people worldwide. Freezing of gait (FoG) is an episodic gait disturbance affecting patients' locomotion, which closely relate to the risk of fall. Prompt detection of freezing of gait is crucial to fall prevention and effective intervention. Several engineering approaches, including time-varying system identification, time-frequency spectral estimation, multimodal information fusion, proxy measurement, and dynamic mode decomposition were employed to promote the accurate detection of FoG, ameliorate the wearablility and advance knowledge in the brain activities during FoG. The engineering solutions to the Parkinson’s FoG, possessing both the advantages of rich multimodal information and of high wearability, enables the effective detection of FoG with cheap, wearable sensors, and provides a promising path for the long-term monitoring and improving the healthcare management of Parkinson’s disease in living environments. Moreover, the complete characterisation of the brain activities from space, time and frequency domain makes possible the patient-specific electrical and magnetically neuromodulation of Parkinson’s disease. |
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Biography: Dr. Yuzhu Guo is an Associated Professor of School of Automation Science and Electrical Engineering, Beihang University, China. His research specialized in the development of new technologies for studying brain activities, with an emphasis on the application of AI technologies in clinical disease diagnosis, neural rehabilitation and advanced brain computer interfaces. He received his PhD degree in Automatic Control and System Engineering from the University of Sheffield in 2009 and worked as postdoc researcher in Signal Processing and Complex Systems Group and INSEGNEO Institute for in silico Medicine, the University of Sheffield. He joint Beihang University in 2017. His recent scientific contributions include new nonlinear system identification methodology, brain inspired time-varying system model and spectral estimation, the proxy measurement of physiological information. He has also contributed to the development of extremely low-cost electrophysiological signal acquisition systems. |