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Real-time Streaming of Gait Assessment for Parkinson’s Disease

pdf bibtex doi: 10.1145/3437963.3441701 dblp: conf/wsdm/FlaggFMM21 ACM: 3437963.3441701 demonstration paper

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

Appeared in: Proceedings of the 14th ACM International Conference on Web Search and Data Mining (WSDM 2021)


Patients with progressive neurological disorders such as Parkinson’s disease, Huntington’s disease, and Amyotrophic Lateral Scle- rosis (ALS) suffer both chronic and episodic difficulties with locomotion. Real-time assessment and visualization of sensor data can be valuable to physicians monitoring the progression of these conditions. We present a system that utilizes the attention based bi-directional recurrent neural network (RNN) presented in [2] to evaluate foot pressure sensor data streamed directly from a pair of sensors attached to a patient. The demonstration also supports indirect streaming from recorded sessions, such as those stored in a FHIR [1] enabled electronic medical records repository, for post-hoc evaluation and comparison of a patient’s gait over time. The system evaluates and visualizes the streamed gait in a real time web interface to provide a personalized normality rating that highlights the strengths and weaknesses of a patient’s gait.

BibTeX @inproceedings{flagg:wsdm2021-gait, author = {Flagg, Cristopher and Frieder, Ophir and MacAvaney, Sean and Motamedi, Gholam}, title = {Real-time Streaming of Gait Assessment for Parkinson’s Disease}, booktitle = {Proceedings of the 14th ACM International Conference on Web Search and Data Mining}, year = {2021}, doi = {10.1145/3437963.3441701} }