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Physical Therapists (PT) and Kinesiologists recommend a series of exercises but often face challenges in continuously monitoring individuals performing exercises to ensure correct postures and prevent injury aggravation. This research attempts to address this issue by building a product designed to automate the detection of the incorrect exercises and provide users with timely feedback. The research effort began with a set of curated exercise videos, a set of biomechanical standards as well as developing a core model to analyze a single exercise - overhead squat. The core model approach consists of three main steps: preprocessing to standardize the videos, creating a 3D body model and calculating incorrectness scores for each repetition and aggregation based on measured joint angles. The work uses state-of-the-art computer vision models and computational algorithms for a customized solution. The results from the core model are used to provide feedback to both practitioners and users through visual overlays on the exercise video and graphical presentation of biomechanical measures captured during the exercise. Research Data Curation Program, UC San Diego, La Jolla, 92093-0175 (https://lib.ucsd.edu/rdcp) This project relies on external software packages, modules/libraries, or programs, use of which may carry specific license requirements. Users should comply with any licenses specified within the contents of this project. Desai, Vaaruni; Fisher, Laben F.; Imran, Zufeshan; Jogadhenu, Sagar R.; Shukla, Prakhar; Ochoa, Benjamin; Richardson, Brian; Duenas, Kevin; Franco, Malerie (2024). Physio AI Companion. In Data Science & Engineering Master of Advanced Study (DSE MAS) Capstone Projects. UC San Diego Library Digital Collections. https://doi.org/10.6075/J0HM58PG
Type
dataset
Identifier
ark:/20775/bb46200419
Language
English
Subject
Pose estimation Data science application Physical therapy Engineering Injury recovery Computer vision Capstone projects Multimedia computation Biomechanics Data Science & Engineering Master of Advanced Study (DSE MAS) DSE MAS - 2024 Cohort
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