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OpenCap: Human movement dynamics from smartphone videos
by
Kidziński, Łukasz
, Chaudhari, Akshay S.
, Uhlrich, Scott D.
, Hicks, Jennifer L.
, Falisse, Antoine
, Muccini, Julie
, Delp, Scott L.
, Ko, Michael
in
Algorithms
/ Applications programs
/ Arthritis
/ Biology and Life Sciences
/ Biomechanical engineering
/ Biomechanical Phenomena
/ Biomechanics
/ Cameras
/ Clinical medicine
/ Clinical trials
/ Cloud computing
/ Cost analysis
/ Data collection
/ Deep learning
/ Disease
/ Dynamics
/ Engineering and Technology
/ Health risks
/ Human locomotion
/ Human motion
/ Humans
/ Joints (anatomy)
/ Kinematics
/ Knee
/ Machine learning
/ Medicine and Health Sciences
/ Models, Biological
/ Motion capture
/ Movement - physiology
/ Muscles
/ Muscles - physiology
/ Musculoskeletal diseases
/ Neuromuscular diseases
/ Osteoarthritis
/ Physical Sciences
/ Physics
/ Pose estimation
/ Research and Analysis Methods
/ Simulation
/ Smart phones
/ Smartphone
/ Smartphones
/ Software
/ Video
2023
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OpenCap: Human movement dynamics from smartphone videos
by
Kidziński, Łukasz
, Chaudhari, Akshay S.
, Uhlrich, Scott D.
, Hicks, Jennifer L.
, Falisse, Antoine
, Muccini, Julie
, Delp, Scott L.
, Ko, Michael
in
Algorithms
/ Applications programs
/ Arthritis
/ Biology and Life Sciences
/ Biomechanical engineering
/ Biomechanical Phenomena
/ Biomechanics
/ Cameras
/ Clinical medicine
/ Clinical trials
/ Cloud computing
/ Cost analysis
/ Data collection
/ Deep learning
/ Disease
/ Dynamics
/ Engineering and Technology
/ Health risks
/ Human locomotion
/ Human motion
/ Humans
/ Joints (anatomy)
/ Kinematics
/ Knee
/ Machine learning
/ Medicine and Health Sciences
/ Models, Biological
/ Motion capture
/ Movement - physiology
/ Muscles
/ Muscles - physiology
/ Musculoskeletal diseases
/ Neuromuscular diseases
/ Osteoarthritis
/ Physical Sciences
/ Physics
/ Pose estimation
/ Research and Analysis Methods
/ Simulation
/ Smart phones
/ Smartphone
/ Smartphones
/ Software
/ Video
2023
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OpenCap: Human movement dynamics from smartphone videos
by
Kidziński, Łukasz
, Chaudhari, Akshay S.
, Uhlrich, Scott D.
, Hicks, Jennifer L.
, Falisse, Antoine
, Muccini, Julie
, Delp, Scott L.
, Ko, Michael
in
Algorithms
/ Applications programs
/ Arthritis
/ Biology and Life Sciences
/ Biomechanical engineering
/ Biomechanical Phenomena
/ Biomechanics
/ Cameras
/ Clinical medicine
/ Clinical trials
/ Cloud computing
/ Cost analysis
/ Data collection
/ Deep learning
/ Disease
/ Dynamics
/ Engineering and Technology
/ Health risks
/ Human locomotion
/ Human motion
/ Humans
/ Joints (anatomy)
/ Kinematics
/ Knee
/ Machine learning
/ Medicine and Health Sciences
/ Models, Biological
/ Motion capture
/ Movement - physiology
/ Muscles
/ Muscles - physiology
/ Musculoskeletal diseases
/ Neuromuscular diseases
/ Osteoarthritis
/ Physical Sciences
/ Physics
/ Pose estimation
/ Research and Analysis Methods
/ Simulation
/ Smart phones
/ Smartphone
/ Smartphones
/ Software
/ Video
2023
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Journal Article
OpenCap: Human movement dynamics from smartphone videos
2023
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Overview
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.
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