OpenCap: Human movement dynamics from smartphone videos.

Autor: Uhlrich SD; Departments of Bioengineering, Stanford University, Stanford, California, United States of America., Falisse A; Departments of Bioengineering, Stanford University, Stanford, California, United States of America., Kidziński Ł; Departments of Bioengineering, Stanford University, Stanford, California, United States of America., Muccini J; Radiology, Stanford University, Stanford, California, United States of America., Ko M; Radiology, Stanford University, Stanford, California, United States of America., Chaudhari AS; Radiology, Stanford University, Stanford, California, United States of America.; Biomedical Data Science, Stanford University, Stanford, California, United States of America., Hicks JL; Departments of Bioengineering, Stanford University, Stanford, California, United States of America., Delp SL; Departments of Bioengineering, Stanford University, Stanford, California, United States of America.; Mechanical Engineering, Stanford University, Stanford, California, United States of America.; Orthopaedic Surgery, Stanford University, Stanford, California, United States of America.
Jazyk: angličtina
Zdroj: PLoS computational biology [PLoS Comput Biol] 2023 Oct 19; Vol. 19 (10), pp. e1011462. Date of Electronic Publication: 2023 Oct 19 (Print Publication: 2023).
DOI: 10.1371/journal.pcbi.1011462
Abstrakt: Measures of human movement dynamics can predict outcomes like injury risk or musculoskeletal disease progression. However, these measures are rarely quantified in large-scale research studies or clinical practice due to the prohibitive cost, time, and expertise required. Here we present and validate OpenCap, an open-source platform for computing both the kinematics (i.e., motion) and dynamics (i.e., forces) of human movement using videos captured from two or more smartphones. OpenCap leverages pose estimation algorithms to identify body landmarks from videos; deep learning and biomechanical models to estimate three-dimensional kinematics; and physics-based simulations to estimate muscle activations and musculoskeletal dynamics. OpenCap's web application enables users to collect synchronous videos and visualize movement data that is automatically processed in the cloud, thereby eliminating the need for specialized hardware, software, and expertise. We show that OpenCap accurately predicts dynamic measures, like muscle activations, joint loads, and joint moments, which can be used to screen for disease risk, evaluate intervention efficacy, assess between-group movement differences, and inform rehabilitation decisions. Additionally, we demonstrate OpenCap's practical utility through a 100-subject field study, where a clinician using OpenCap estimated musculoskeletal dynamics 25 times faster than a laboratory-based approach at less than 1% of the cost. By democratizing access to human movement analysis, OpenCap can accelerate the incorporation of biomechanical metrics into large-scale research studies, clinical trials, and clinical practice.
Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: SDU and AF are co-founders of Model Health, Inc., which supports the non-academic, commercial use of the open-source software described here. OpenCap, the cloud-deployed academic version of this open-source software is hosted at Stanford University and will remain freely available to the academic research community for the foreseeable future. No other authors have competing interests to declare.
(Copyright: © 2023 Uhlrich et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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