Portable in-clinic video-based gait analysis: validation study on prosthetic users Article Swipe
Anthony Cimorelli
,
Ankit Patel
,
Tasos Karakostas
,
R. Cotton
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1101/2022.11.10.22282089
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1101/2022.11.10.22282089
Despite the common focus of gait in rehabilitation, there are few tools that allow quantitatively characterizing gait in the clinic. We recently described an algorithm, trained on a large dataset from our clinical gait analysis laboratory, which produces accurate cycle-by-cycle estimates of spatiotemporal gait parameters including step timing and walking velocity. Here, we demonstrate this system generalizes well to clinical care with a validation study on prosthetic users seen in therapy and outpatient clinic. Specifically, estimated walking velocity was similar to annotated 10-meter walking velocities, and cadence and foot contact times closely mirrored our wearable sensor measurements. Additionally, we found that a 2D keypoint detector pre-trained on largely able-bodied individuals struggles to localize prosthetic joints, particularly for those individuals with more proximal or bilateral amputations, but it is possible to train a prosthetic-specific joint detector. Further work is required to validate the other outputs from our algorithm including sagittal plane joint angles and step length. Code and trained weights will be released upon publication.
Related Topics
Concepts
Sagittal plane
Cadence
Gait
Gait analysis
Computer science
Gait cycle
Effect of gait parameters on energetic cost
Physical medicine and rehabilitation
Focus (optics)
Wearable computer
Silhouette
Joint (building)
Artificial intelligence
Simulation
Computer vision
Kinematics
Medicine
Engineering
Architectural engineering
Radiology
Classical mechanics
Embedded system
Optics
Physics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2022.11.10.22282089
- https://www.medrxiv.org/content/medrxiv/early/2022/11/14/2022.11.10.22282089.full.pdf
- OA Status
- green
- Cited By
- 10
- References
- 38
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4309175704
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4309175704Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/2022.11.10.22282089Digital Object Identifier
- Title
-
Portable in-clinic video-based gait analysis: validation study on prosthetic usersWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-11-14Full publication date if available
- Authors
-
Anthony Cimorelli, Ankit Patel, Tasos Karakostas, R. CottonList of authors in order
- Landing page
-
https://doi.org/10.1101/2022.11.10.22282089Publisher landing page
- PDF URL
-
https://www.medrxiv.org/content/medrxiv/early/2022/11/14/2022.11.10.22282089.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.medrxiv.org/content/medrxiv/early/2022/11/14/2022.11.10.22282089.full.pdfDirect OA link when available
- Concepts
-
Sagittal plane, Cadence, Gait, Gait analysis, Computer science, Gait cycle, Effect of gait parameters on energetic cost, Physical medicine and rehabilitation, Focus (optics), Wearable computer, Silhouette, Joint (building), Artificial intelligence, Simulation, Computer vision, Kinematics, Medicine, Engineering, Architectural engineering, Radiology, Classical mechanics, Embedded system, Optics, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
10Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 3, 2023: 5Per-year citation counts (last 5 years)
- References (count)
-
38Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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