Asset Details
Do you wish to reserve the book?
Validity of artificial intelligence-based markerless motion capture system for clinical gait analysis: Spatiotemporal results in healthy adults and adults with Parkinson’s disease
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Validity of artificial intelligence-based markerless motion capture system for clinical gait analysis: Spatiotemporal results in healthy adults and adults with Parkinson’s disease
Do you wish to request the book?
Validity of artificial intelligence-based markerless motion capture system for clinical gait analysis: Spatiotemporal results in healthy adults and adults with Parkinson’s disease
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Validity of artificial intelligence-based markerless motion capture system for clinical gait analysis: Spatiotemporal results in healthy adults and adults with Parkinson’s disease
2023
Overview
Markerless motion capture methods are continuously in development to target limitations encountered in marker-, sensor-, or depth-based systems. Previous evaluation of the KinaTrax markerless system was limited by differences in model definitions, gait event methods, and a homogenous subject sample. The purpose of this work was to evaluate the accuracy of spatiotemporal parameters in the markerless system with an updated markerless model, coordinate- and velocity-based gait events, and subjects representing young adult, older adult, and Parkinson’s disease groups. Fifty-seven subjects and 216 trials were included in this analysis. Interclass correlation coefficients showed excellent agreement between the markerless system and a marker-based reference system for all spatial parameters. Temporal variables were similar, except swing time which showed good agreement. Concordance correlation coefficients were similar with all but swing time showing moderate to almost perfect concordance. Bland-Altman bias and limits of agreement (LOA) were small and improved from previous evaluations. Parameters showed similar agreement across coordinate- and velocity-based gait methods with the latter showing generally smaller LOAs. Improvements in spatiotemporal parameters in the present evaluation was due to inclusion of keypoints at the calcanei in the markerless model. Consistency in the calcanei keypoints relative to heel marker placements may improve results further. Similar to previous work, LOAs are within boundaries to detect differences in clinical groups. Results support the use of the markerless system for estimation of spatiotemporal parameters across age and clinical groups, but caution should be taken when generalizing findings due to remaining error in kinematic gait event methods.
