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<li class="toctree-l2"><a class="reference internal" href="#use-cases">Use Cases</a>
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<p class="caption"><span class="caption-text">User Guide</span></p>
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<li class="toctree-l2"><a class="reference internal" href="user_guide/theoretical_framework/mid_level/mid_level/">Layer 3 - Mid-Level Features</a>
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<li class="toctree-l1"><a class="reference internal" href="user_guide/integrations/">Integrations</a>
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<p class="caption"><span class="caption-text">API</span></p>
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<li class="toctree-l1"><a class="reference internal" >Analysis Primitives</a>
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<li class="toctree-l2"><a class="reference internal" href="API/analysis_primitives/bilateral_symmetry/">Bilateral Symmetry</a>
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<li class="toctree-l2"><a class="reference internal" href="API/analysis_primitives/clusterability/">Clusterability</a>
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<li class="toctree-l2"><a class="reference internal" href="API/analysis_primitives/mse_dominance/">Mse Dominance</a>
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<li class="toctree-l2"><a class="reference internal" href="API/analysis_primitives/rarity/">Rarity</a>
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<li class="toctree-l2"><a class="reference internal" href="API/analysis_primitives/statistical_moment/">Statistical Moment</a>
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<li class="toctree-l2"><a class="reference internal" href="API/analysis_primitives/synchronization/">Synchronization</a>
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<li class="toctree-l1"><a class="reference internal" >Data Models</a>
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<li class="toctree-l2"><a class="reference internal" href="API/data_models/sliding_window/">Sliding Window</a>
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<li class="toctree-l1"><a class="reference internal" >Low Level</a>
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<li class="toctree-l2"><a class="reference internal" href="API/low_level/contraction_expansion/">Contraction Expansion</a>
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<li class="toctree-l1"><a class="reference internal" >Utils</a>
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<li class="toctree-l2"><a class="reference internal" href="API/utils/math_utils/">Math Utils</a>
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<div class="hero">
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<h1 id="pyeyesweb">PyEyesWeb</h1>
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<p><strong>Movement Analysis Toolkit</strong></p>
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<p><img alt="Open Source" src="https://img.shields.io/badge/Open%20Source-Yes-brightgreen" />
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<img alt="Platform" src="https://img.shields.io/badge/Platform-Python-blue" />
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<img alt="Domain" src="https://img.shields.io/badge/Domain-Movement%20Analysis-purple" /></p>
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<hr />
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<p>🚀 <strong>Extract features</strong> from raw human movement data<br />
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🎓 <strong>Apply in science, health, and the arts</strong><br />
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🤝 <strong>Developed with the partial support of the <a href="https://resilence.eu/">EU ICT STARTS Resilence Project</a></strong></p>
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<p><strong><a href="user_guide/getting_started/">📖 Get Started</a></strong> | <strong><a href="https://github.com/InfoMusCP/PyEyesWeb">GitHub Repository</a></strong></p>
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<hr />
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<h2 id="why-pyeyesweb">Why PyEyesWeb?</h2>
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<p>PyEyesWeb builds on the <strong>Expressive Gesture Analysis</strong> library of <a href="https://casapaganini.unige.it/eyesweb_bp" target="_blank">EyesWeb</a>,
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bringing expressive movement analysis into <strong>Python</strong> as a core aim of the project,
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creating a modern, modular, and accessible toolkit for research, health, and artistic applications. </p>
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<p>PyEyesWeb is designed to facilitate adoption in <strong>artificial intelligence and machine learning pipelines</strong>,
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while also enabling seamless integration with <strong>creative and interactive platforms</strong>
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(e.g., <strong>TouchDesigner, Unity, and Max/MSP</strong> among others), supporting innovative,
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cross-disciplinary projects at the intersection of science and the arts.</p>
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<details>
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<summary>Learn more about EyesWeb</summary>
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<a href="https://casapaganini.unige.it/eyesweb_bp" target="_blank">EyesWeb</a> is an open software research platform for the design and development of <b>real-time multimodal systems and interfaces</b>.
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It supports a wide variety of inputs, including motion capture, cameras, game controllers (Kinect, Wii), multichannel audio, and physiological signals.
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<br><br>
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Outputs include multichannel audio, video, analog devices, and robotic platforms. EyesWeb provides libraries such as <b>Non-Verbal Expressive Gesture Analysis</b> and <b>Non-Verbal Social Signals Analysis</b>, and a visual programming environment that enables users to develop <b>real-time, networked applications</b>.
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<br><br>
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Originally started in 1997, EyesWeb has been adopted worldwide in scientific research, education, and industry, including EU projects and collaborations with organizations such as INTEL and NYU.
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</details>
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<h2 id="use-cases">Use Cases</h2>
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<ul>
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<p><strong>🎓 Research &amp; Science</strong>
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Quantify movement expressivity and analyze biomechanics with validated methods.</p>
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</li>
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<p><strong>🔗 Interactive Media</strong>
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Integrate PyEyesWeb in real-time with <a href="user_guide/integrations">TouchDesigner</a>.</p>
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</li>
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<p><strong>🏥 Health &amp; Rehabilitation</strong>
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Assess movement disorders, monitor recovery, and support clinical studies.</p>
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<p><strong>🎭 Artistic Performance</strong>
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Explore synchrony, smoothness, and expressive qualities in dance and live performance.</p>
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</ul>
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<h2 id="methodological-foundation">Methodological Foundation</h2>
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<p>PyEyesWeb is informed by decades of research at the intersection of <strong>movement science, computational modeling, and expressive gesture analysis</strong>. </p>
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<p>A key methodological foundation of PyEyesWeb is the layered conceptual framework for analyzing expressive qualities of movement developed by <a href="http://www.casapaganini.org/index_eng.php" target="_blank"><strong>InfoMus Lab – Casa Paganini</strong></a> [1].
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This framework models an observer of a dance performance through four interconnected layers: from the physical signals captured by sensors, to the higher-level expressive qualities conveyed by movement (such as emotions).</p>
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<div class="admonition references">
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<p class="admonition-title">References</p>
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<p>[1] Camurri, A., Volpe, G., Piana, S., Mancini, M., Niewiadomski, R., Ferrari, N., &amp; Canepa, C. (2016, July). The dancer in the eye: towards a multi-layered computational framework of qualities in movement. In Proceedings of the 3rd International Symposium on Movement and Computing (pp. 1-7).</p>
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<h2 id="project-context">Project Context</h2>
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<p class="admonition-title">About the Authors</p>
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<p>PyEyesWeb is developed by <a href="http://www.casapaganini.org/index_eng.php" target="_blank"><strong>InfoMus Lab – Casa Paganini</strong></a>, University of Genoa, as partners of the Resilence EU Project. </p>
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<img alt="InfoMus Lab Logo" src="assets/cp-logo.png" width="512" />
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<p>PyEyesWeb is developed with the partial support of the <a href="https://resilence.eu/">EU ICT STARTS Resilence Project</a>,
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funded by the European Union’s Horizon programme. </p>
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<div align="center">
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<img src="assets/eu-logo.png" alt="EU Logo" width="100" style="margin:15px"/>
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<div class="admonition info">
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<p class="admonition-title">Community &amp; Collaboration</p>
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<p>Whether you are a <strong>researcher, artist, or developer</strong>, PyEyesWeb helps you bridge movement, computation, and expression.<br />
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It is designed to be <strong>modular, accessible, and integrable</strong>, supporting a variety of use cases from scientific analysis to interactive artistic performances.</p>
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<p>💡 We welcome contributions, collaborations, and use cases from the community.<br />
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Check out our <a href="https://github.com/InfoMusCP/PyEyesWeb">GitHub repository</a> to report issues, suggest features, or contribute code.</p>
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<p>MIT Licensed · Open for collaboration </p>
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<p>Copyright &copy; 2025 InfoMus Lab - Casa Paganini, Genoa, Italy. All rights reserved.</p>
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