Julie Muccini is an occupational therapist specializing in working with individuals with neurological diagnoses. She is registered and licensed in California and is a member of the American Occupational Therapy Association (AOTA) and the Occupational Therapy Association of California (OTAC). She is actively involved in research on recovery post-stroke. She works in the Human Performance Lab with an interdisciplinary team integrating biomechanics, biomedical engineering, physiology, psychology, and rehabilitation. Ms. Muccini received her bachelor's of science from the University of Massachusetts at Amherst in Industrial Engineering and Operations Research and her master's of science from Boston University in Occupational Therapy. She started working at the hospital at Stanford in 1997 and transitioned to the Outpatient Neuro Rehab Clinic at the Stanford Neurosciences Health Center in 2014. In March 2021, Julie moved over to the Stanford School of Medicine to work in the Human Performance Lab at the Arrillaga Center for Sports and Recreation, ACSR.
Education & Certifications
MSc, Boston University, Sargent College, Occupational Therapy (1996)
BSc, University of Massachusetts, Amherst, Industrial Engineering and Operations Research (1990)
While an undergraduate, Julie competed in Division I Track and Field in the 400-meter hurdles and triple jump; after graduate school, she became a competitive cyclist on the track and road and won two Masters National Championships on the Velodrome for the Points Race. In addition, she enjoys Muay Thai Fighting, Brazilian Jiu-jitsu, hiking, road cycling, and mountain biking.
Professional Affiliations and Activities
Member, Occupational Therapy Association of California (1997 - Present)
Member, American Occupational Therapy Association (1997 - Present)
OpenCap: Human movement dynamics from smartphone videos.
PLoS computational biology
2023; 19 (10): e1011462
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.
View details for DOI 10.1371/journal.pcbi.1011462
View details for PubMedID 37856442
View details for PubMedCentralID PMC10586693
Home-based Virtual Reality Therapy for Hand Recovery After Stroke.
PM & R : the journal of injury, function, and rehabilitation
Many stroke survivors suffer from arm and hand weakness, but there are only limited efficacious options for arm therapy available.To assess the feasibility of unsupervised home-based use of a virtual reality device (Smart Glove) for hand rehabilitation post stroke.Prospective single-arm study consisting of a 2-week run-in phase with no device use followed by an 8-week intervention period.Participants were recruited at the Stanford Neuroscience Outpatient Clinic.Twenty chronic stroke patients with upper extremity impairment.Participants were instructed to use the Smart Glove 50 minutes per day, 5 days per week for 8 weeks.We measured (1) compliance, (2) patients' impression of the intervention, and (3) efficacy measures including the upper extremity Fugl-Meyer (UE-FM), the Jebsen-Taylor hand function test (JTHFT) and the Stroke Impact Scale (SIS).Of 20 subjects, 7 (35%) met target compliance of 40 days use, and 6 (30%) used the device for 20-39 days. Eighty-five percent of subjects 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 = 0.03), by 16.1 ± 15.3 points on the hand-domain of the SIS (P < 0.01) and there was a trend towards improvement on the UE-FM (2.2 ± 5.5 points, P = 0.10).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. This article is protected by copyright. All rights reserved.
View details for DOI 10.1002/pmrj.12598
View details for PubMedID 33773059
How to Design Woke Stroke Tech: The STORIES Project
LIPPINCOTT WILLIAMS & WILKINS. 2020
View details for Web of Science ID 000590040201402