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Streaming Gait Assessment for Parkinson's Disease

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Authors: Cristopher Flagg, Ophir Frieder, Sean MacAvaney, Gholam Motamedi

Appeared in: Proceedings of the first Workshop on Health Search and Data Mining (HSDM @ WSDM 2020)

DBLP conf/wsdm/FlaggFMM20 Google Scholar 7wWfoDgAAAAJ:5nxA0vEk-isC Semantic Scholar 10b54a6e3f7c5e8905d748f02da44eb90908b38d smac.pub hsdm2020-gait


Patients with progressive neurological disorders such as Parkinson's Disease, Huntington's disease, and Amyotrophic Lateral Sclerosis (ALS) suffer both chronic and episodic difficulties with locomotion. These difficulties result in falls and injuries which negatively affect a patient's quality of life. Decision support within the health domain attempts to characterize the patient's current gait with respect to recent and long term gait characteristics to monitor disease degeneration and suggest preventative intervention. We propose the application of an attention based bi-directional recurrent neural network (RNN) to medical gait data collected from wearable mobile sensors to identify and rate the normality of gait patterns from streaming data and to inform clinicians of specific gait abnormalities. Experimental results with respect to multiple data sets demonstrate the effectiveness of streaming gait analysis to augment traditional health care diagnostic methods, automatically classify a patient’s mobility, and provide monitoring of patients outside of the clinical environment.

BibTeX @inproceedings{flagg:hsdm2020-gait, author = {Flagg, Cristopher and Frieder, Ophir and MacAvaney, Sean and Motamedi, Gholam}, title = {Streaming Gait Assessment for Parkinson's Disease}, booktitle = {Proceedings of the first Workshop on Health Search and Data Mining}, year = {2020} }