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
- Yasamin Mostofi (ORCID: https://orcid.org/0000-0003-2670-2214)
- Mert Torun (ORCID: https://orcid.org/0009-0006-5928-7773)
- Philip R. Delio (ORCID: https://orcid.org/0000-0002-6056-6431)
- Alireza Parsay
Institutions
- University of California, Santa Barbara (US)
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
- Office of Naval Research
- Division of Computer and Network Systems