Webinar: Quantitative Movement Analysis Using Single-camera Videos, Part 1 of 2
Lukasz Kidzinski of Stanford University presents findings from analyzing a dataset of 1792 videos of patients from Gillette Children’s Specialty Healthcare. He used these data to train machine learning models and found that single-camera recordings can predict gait parameters with clinically relevant accuracy. His predictions include cadence, speed, peak knee flexion, as well as the Gait Deviation Index, a holistic measure of gait abnormality, and the likelihood of receiving a surgery.
This webinar is offered jointly with the Restore Center (https://restore.stanford.edu/), an NIH-funded Medical Rehabilitation Research Resource Network Center at Stanford University.
Read the associated publication: https://www.nature.com/articles/s4146...
Try the associated tutorial: https://github.com/stanfordnmbl/mobil...
Watch part 2 of this webinar – a tutorial on pose estimation for biomechanics: • Webinar (Tutorial): Quantitative Movement ...
Browse through Q&A: https://mobilize.stanford.edu/wp-cont...