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README.md

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The <code>ensemble_code</code> folder contains the notebooks in which we tried to use ensemble methods. This code is not guaranteed to work, but if you want to check it out it is there.
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## Citations
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Please cite the following:
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```
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@InProceedings{Nishi_2021_CVPR,
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author = {Nishi, Kento and Ding, Yi and Rich, Alex and {H{\"o}llerer, Tobias},
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title = {Augmentation Strategies for Learning With Noisy Labels},
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booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
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month = {June},
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year = {2021},
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pages = {8022-8031}
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}
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```

index.html

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<!DOCTYPE html>
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<html>
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<head>
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<meta charset="utf-8">
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<meta name="description"
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content="Activity Recognition for Autism Diagnosis">
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<meta name="keywords" content="Nerfies, D-NeRF, NeRF">
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<title>Activity Recognition for Autism Diagnosis</title>
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<h1 class="title is-1 publication-title"> Activity Recognition for Autism Diagnosis</h1>
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<div class="is-size-5 publication-authors">
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<span class="author-block">
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<a href="https://keunhong.com">Anish Lakkapragada</a><sup>1</sup>,</span>
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<span class="author-block">
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<a href="https://utkarshsinha.com">Peter Washington</a><sup>2</sup>,</span>
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<span class="author-block">
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<a href="https://jonbarron.info">Dennis P. Wall</a><sup>2</sup>,
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</span>
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</div>
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<div class="is-size-5 publication-authors">
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<span class="author-block"><sup>1</sup>Lynbrook High School</span>
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<span class="author-block"><sup>2</sup>Stanford University</span>
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</div>
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<div class="column has-text-centered">
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<div class="publication-links">
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<!-- PDF Link.
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</span>
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<span>Paper</span>
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<a href="https://arxiv.org/abs/2108.07917"
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class="external-link button is-normal is-rounded is-dark">
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<i class="ai ai-arxiv"></i>
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<span>arXiv</span>
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<!-- Code Link. -->
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<span class="link-block">
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<a href="https://github.com/anish-lakkapragada/ActivRecognition-Autism-Diagnosis"
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class="external-link button is-normal is-rounded is-dark">
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<i class="fab fa-github"></i>
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<span>Code</span>
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<h2 class="subtitle has-text-centered">
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<span class="dnerf">Nerfies</span> turns selfie videos from your phone into
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free-viewpoint
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portraits.
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<h2 class="title is-3">Abstract</h2>
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<div class="content has-text-justified">
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<p> <em> A formal autism diagnosis can be an inefficient and lengthy process. Families may wait months orlonger before
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receiving a diagnosis for their child. One approach to lessen delays is the use digitaltechnologies to detect
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the presence of behaviors indicative of autism, which in aggregate may leadto remote and automated diagnostics.
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One of the strongest indicators of autism is stimming, whichincludes repetitive, self-stimulatory behaviors such
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as hand flapping, headbanging, and spinning.Using computer vision to detect hand flapping is especially
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difficult due to the sparsity of publictraining data in this space and excessive shakiness and motion in such
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data. Our work demonstratesa novel method that may overcome these issues: we use hand landmark detection over
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time as afeature representation which is then fed into a Long Short-Term Memory (LSTM) model. We achieve
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avalidation accuracy and F1 Score of about 72% on detecting whether videos from the Self-StimulatoryBehaviour
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Dataset (SSBD) contain hand flapping or not. Our best model also predicts accurately onexternal videos we
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recorded of ourselves outside of the dataset it was trained on. This model usesless than 26,000 parameters,
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providing promise for fast deployment into ubiquitous and wearabledigital settings for a remote autism
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diagnosis. </em> </p>
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<section class="section" id="BibTeX">
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<h2 class="title">BibTeX</h2>
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<pre><code>@article{park2021nerfies
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author = {Park, Keunhong and Sinha, Utkarsh and Barron, Jonathan T. and Bouaziz, Sofien and Goldman, Dan B and Seitz, Steven M. and Martin-Brualla, Ricardo},
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title = {Nerfies: Deformable Neural Radiance Fields},
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journal = {ICCV},
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year = {2021},
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}</code></pre>
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