Augmenting Knee Rehabilitation Replays Using MediaPipe Pose Estimation
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Bacic, Boris
Vasile, Claudiu
Ciucă, Marian G.
Feng, Chengwei
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Abstract
Overview This project implements a methodology for analysing knee angles from video footage using MediaPipe Pose estimation. It provides near real-time visualisation and analysis of knee movements, supporting both front-view and side-view perspectives. Note Near real-time visualisation of MP4 videos depends on the computer specification. Hint: If using a decade old laptop for fast scrolling and A-B sequence analysis, consider capturing first a screen cast of the generated augmented video replay. As a stand-alone application, this source code is the second part of the three stage processing workflow, which is also a part of it's parent project codebase 1 intended for home use and advancements of near-future analytical healthcare systems. See more: "Towards nation-wide analytical healthcare infrastructures: A privacy-preserving augmented knee rehabilitation case study".Description
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Bacic, B. (2025). tnwahi-appakrcs (Initial project fork) [Computer software]. GitHub. https://github.com/bbacic/tnwahi-appakrcs
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Copyright (c) 2024 Claudiu
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