Gait Disorder Assessment Based on a Large-Scale Clinical Trial: WiFi Versus Video Versus Doctor’s Visual Inspection

Neurological gait disorders affect a large population, significantly reducing life quality. This article brings a foundational understanding to the potentials of emerging sensing modalities (e.g., WiFi) for gait disorder assessment, via conducting a one-year-long clinical trial in collaboration with the Neurology Associates of Santa Barbara. Our medical campaign encompasses 114 real subjects and a wide spectrum of disorders (e.g., Parkinson’s, Neuropathy, Post Stroke, Dementia, Arthritis). We then develop the first WiFi-based gait disorder sensing system of its kind, distinguished by its scope of validation with a large and diverse patient cohort. To ensure generalizability, we mainly leverage publicly accessible online videos of gait disorders for training, and develop a video-to-RF pipeline to convert them to synthetic RF training data. We then extensively test the system in a neurology center (i.e., the Neurology Associates of Santa Barbara). Additionally, we provide a 1-1 comparison with a vision-based system, by developing a vision-based gait assessment system under identical conditions, a first-of-its-kind comparison to our knowledge. We finally contrast both systems with neurologists’ accuracy when basing evaluation solely on visual gait inspection, by designing/distributing a large survey to 70 neurologists, offering the first apples-to-apples comparison of these three sensing modalities. Our findings can help integrate these sensing systems into medical practice, working toward equitable healthcare.

Authors

Institutions

Publication Details

Journal
IEEE Internet of Things Journal
Published
2025-03-11
DOI
https://doi.org/10.1109/jiot.2025.3550204
Citations
3
Primary Topic
Gait Recognition and Analysis
Type
article
Field-Weighted Citation Impact
2.02

Funders

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article

Gait Disorder Assessment Based on a Large-Scale Clinical Trial: WiFi Versus Video Versus Doctor’s Visual Inspection

Yasamin Mostofi, Mert Torun, Philip R. Delio, Alireza Parsay
3 citations
IEEE Internet of Things Journal
Gait Recognition and Analysis
2.02
article

Gait Disorder Assessment Based on a Large-Scale Clinical Trial: WiFi Versus Video Versus Doctor’s Visual Inspection

Yasamin Mostofi, Mert Torun, Philip R. Delio, Alireza Parsay
article en
3 citations

Abstract

Neurological gait disorders affect a large population, significantly reducing life quality. This article brings a foundational understanding to the potentials of emerging sensing modalities (e.g., WiFi) for gait disorder assessment, via conducting a one-year-long clinical trial in collaboration with the Neurology Associates of Santa Barbara. Our medical campaign encompasses 114 real subjects and a wide spectrum of disorders (e.g., Parkinson’s, Neuropathy, Post Stroke, Dementia, Arthritis). We then develop the first WiFi-based gait disorder sensing system of its kind, distinguished by its scope of validation with a large and diverse patient cohort. To ensure generalizability, we mainly leverage publicly accessible online videos of gait disorders for training, and develop a video-to-RF pipeline to convert them to synthetic RF training data. We then extensively test the system in a neurology center (i.e., the Neurology Associates of Santa Barbara). Additionally, we provide a 1-1 comparison with a vision-based system, by developing a vision-based gait assessment system under identical conditions, a first-of-its-kind comparison to our knowledge. We finally contrast both systems with neurologists’ accuracy when basing evaluation solely on visual gait inspection, by designing/distributing a large survey to 70 neurologists, offering the first apples-to-apples comparison of these three sensing modalities. Our findings can help integrate these sensing systems into medical practice, working toward equitable healthcare.

IEEE Internet of Things JournalVol. 12(12)
University of California, Santa Barbara (US)
Office of Naval Research, Division of Computer and Network Systems
Quality Education
Openalex Percentile: Top 15%
Gait Recognition and Analysis
2.02
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