OpenCap: 3D human movement dynamics from smartphone videos

Abstract

Measures of human movement dynamics can predict outcomes like injury risk or musculoskeletal disease progression. However, these measures are rarely quantified in clinical practice due to the prohibitive cost, time, and expertise required. Here we present and validate OpenCap, an open-source platform for computing movement dynamics using videos captured from smartphones. 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 movement 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.

Towards Out-of-Lab Anterior Cruciate Ligament Injury Prevention and Rehabilitation Assessment: A Review of Portable Sensing Approaches

“…Many studies have proposed portable-sensing methods to estimate knee kinematics (Table 2). Also, as previously mentioned, portable sensors coupled with neural networks and musculoskeletal simulation have shown promise at predicting knee kinetics [36, 81, 82]. Using these technologies to develop assessments that are fast and accurate enough for clinical deployment could enable better return-to-sport decision making and potentially lower the risk of reinjury.…”

“…Among these primary parameters, knee angles were estimated by many of the reviewed studies; however, only one included study estimated knee extension moment [36] and no study estimated knee abduction moment during jump-landing tasks or cutting. Recent research has shown that knee abduction moment during gait can be estimated from simulated 2-D video data using neural networks [81] or from real 2-D video data using neural networks and musculoskeletal simulation [82]. Future research should test these methods for dynamic activities relevant to ACL injury risk screening, such as jump-landing and cutting motions.…”

Altered movement strategy during functional movement after an ACL injury, despite ACL reconstruction

“…The information provided by the biomechanical analysis may be useful for return-to-sport decision-making and for informing the rehabilitation process but required more accessible technologies for wide clinical adaption. More accessible technologies are currently being developed, [see e.g., ( 39 )] but future work is needed to improve and validate the technologies to support their transfer to clinical practice.…”

Pose2Sim: An open-source Python package for multiview markerless kinematics

“…Although the GUI is more user friendly, it is neither open-source nor customizable. OpenCap (Uhlrich et al, 2022) has recently been released, and offers a user-friendly web application working with low-cost hardware. It predicts the coordinates of 43 anatomical markers from 20 triangulated keypoints, and imports them in OpenSim.…”