OpenCap: Human movement dynamics from smartphone videos.

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.

Home-based virtual reality therapy for hand recovery after stroke.

BACKGROUND: Many stroke survivors experience arm and hand weakness, but there are only limited efficacious options for arm therapy available. OBJECTIVE: To assess the feasibility of unsupervised home-based use of a virtual reality device (Smart Glove) for hand rehabilitation post stroke. DESIGN: Prospective single-arm study consisting of a 2-week run-in phase with no device use followed by an 8-week intervention period. SETTING: Participants were recruited at the Stanford Neuroscience Outpatient Clinic. PARTICIPANTS: Twenty chronic stroke patients with upper extremity impairment. INTERVENTIONS: Participants were instructed to use the Smart Glove 50 minutes per day, 5 days per week for 8 weeks. MAIN OUTCOME MEASURES: The following outcomes were measured: (1) compliance, (2) patients' impression of the intervention, and (3) efficacy using the upper extremity Fugl-Meyer (UE-FM), the Jebsen-Taylor hand function test (JTHFT), and the Stroke Impact Scale (SIS). RESULTS: Of 20 participants, seven (35%) met target compliance of 40 days use, and six (30%) used the device for 20-39 days. Eighty-five percent of participants were satisfied with the therapy, with 80% reporting improvement in hand function. During the run-in phase there were no improvements in hand function. During the intervention, patients improved by a mean of 26.6 ± 48.8 seconds on the JTHFT (P = .03), by 16.1 ± 15.3 points on the hand-domain of the SIS (P < .01) and there was a trend toward improvement on the UE-FM (2.2 ± 5.5 points, P = .10). CONCLUSIONS: Unsupervised use of the Smart Glove in the home environment may improve hand/arm function in subacute/chronic stroke patients. A randomized controlled trial is needed to confirm these results.