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anish-lakkapragada committed Sep 3, 2021
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1 change: 1 addition & 0 deletions README.md
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Expand Up @@ -127,6 +127,7 @@ The <code>ensemble_code</code> folder contains the notebooks in which we tried t
## Citations
Please cite the following:
```
@article{lakkapragada2021activity,
title={Activity Recognition for Autism Diagnosis},
author={Lakkapragada, Anish and Washington, Peter and Wall, Dennis},
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<head>
<meta charset="utf-8">
<meta name="description"
content="Activity Recognition for Autism Diagnosis">
<meta name="keywords" content="Nerfies, D-NeRF, NeRF">
<meta name="description" content="Activity Recognition for Autism Diagnosis">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>Activity Recognition for Autism Diagnosis</title>

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</head>

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<h1 class="title is-1 publication-title"> Activity Recognition for Autism Diagnosis</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://keunhong.com">Anish Lakkapragada</a><sup>1</sup>,</span>
<a href="https://wall-lab.stanford.edu/people/interns/anishl/">Anish
Lakkapragada</a><sup>1</sup>,</span>
<span class="author-block">
<a href="https://utkarshsinha.com">Peter Washington</a><sup>2</sup>,</span>
<a href="https://wall-lab.stanford.edu/people/current/peter/">Peter
Washington</a><sup>2</sup>,</span>
<span class="author-block">
<a href="https://jonbarron.info">Dennis P. Wall</a><sup>2</sup>,
<a href="https://wall-lab.stanford.edu/people/current/dennis/">Dennis P.
Wall</a><sup>2</sup>,
</span>
</div>

<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup>Lynbrook High School</span>
<span class="author-block"><sup>2</sup>Stanford University</span>
<span class="author-block">Lynbrook High School<sup>1</sup></span>
<span class="author-block">Stanford University<sup>2</sup></span>
</div>

<div class="column has-text-centered">
Expand All @@ -97,7 +76,7 @@ <h1 class="title is-1 publication-title"> Activity Recognition for Autism Diagno
</span>
<span>Paper</span>
</a>
</span> -->
</span> -->
<span class="link-block">
<a href="https://arxiv.org/abs/2108.07917"
class="external-link button is-normal is-rounded is-dark">
Expand All @@ -116,7 +95,7 @@ <h1 class="title is-1 publication-title"> Activity Recognition for Autism Diagno
</span>
<span>Video</span>
</a>
</span> -->
</span> -->
<!-- Code Link. -->

<span class="link-block">
Expand Down Expand Up @@ -151,7 +130,7 @@ <h2 class="subtitle has-text-centered">
</h2>
</div>
</div>
</section>-->
</section>-->

<!--
<section class="hero is-light is-small">
Expand Down Expand Up @@ -210,99 +189,121 @@ <h2 class="subtitle has-text-centered">
</div>
</div>
</section>
-->
-->

<section class="section">
<div class="container is-max-desktop">
<!-- Abstract. -->

<!-- Overview-->

<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p> <em> A formal autism diagnosis can be an inefficient and lengthy process. Families may wait months orlonger before
receiving a diagnosis for their child. One approach to lessen delays is the use digitaltechnologies to detect
the presence of behaviors indicative of autism, which in aggregate may leadto remote and automated diagnostics.
One of the strongest indicators of autism is stimming, whichincludes repetitive, self-stimulatory behaviors such
as hand flapping, headbanging, and spinning.Using computer vision to detect hand flapping is especially
difficult due to the sparsity of publictraining data in this space and excessive shakiness and motion in such
data. Our work demonstratesa novel method that may overcome these issues: we use hand landmark detection over
time as afeature representation which is then fed into a Long Short-Term Memory (LSTM) model. We achieve
avalidation accuracy and F1 Score of about 72% on detecting whether videos from the Self-StimulatoryBehaviour
Dataset (SSBD) contain hand flapping or not. Our best model also predicts accurately onexternal videos we
recorded of ourselves outside of the dataset it was trained on. This model usesless than 26,000 parameters,
providing promise for fast deployment into ubiquitous and wearabledigital settings for a remote autism
diagnosis. </em> </p>
<h2 class="title is-3">Overview</h2>
<div class="content has-text-justified">
<figure>
<img src = "Approach.png">
<figcaption> Our overall model to detect hand flapping, an indicator of autism, in videos. </figcaption>
</figure>
<p> Our objective was to be able to create a model that could reliably detect hand flapping, an indicator of autism, in videos that then could be applied
to apps to help create a remote autism diagnosis. We do this by going frame (an image) by frame in a given video, and detecting the
numerical coordinates of the hand's landmarks in the images. These coordinates are fed overtime into a Long Term Short-Term Memory (LSTM) model. The output
at the last time step of the LSTM model is fed into a fully-connected layer to get the prediction on whether hand flapping was detected (1) or not (0). A visual diagram
of our approach is shown above. Our best model uses less than 26,000 parameters, so it can easily be deployed into any app.</p>
</div>
</div>
</div>
<!--/ Abstract. -->

<!-- Paper video.
<!-- Abstract. -->
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-3">Video</h2>
<div class="publication-video">
<iframe src="https://www.youtube.com/embed/MrKrnHhk8IA?rel=0&amp;showinfo=0" frameborder="0"
allow="autoplay; encrypted-media" allowfullscreen></iframe>
<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p> <em> A formal autism diagnosis can be an inefficient and lengthy process. Families may wait
months orlonger before
receiving a diagnosis for their child. One approach to lessen delays is the use
digitaltechnologies to detect
the presence of behaviors indicative of autism, which in aggregate may leadto remote and
automated diagnostics.
One of the strongest indicators of autism is stimming, whichincludes repetitive,
self-stimulatory behaviors such
as hand flapping, headbanging, and spinning.Using computer vision to detect hand
flapping is especially
difficult due to the sparsity of publictraining data in this space and excessive
shakiness and motion in such
data. Our work demonstratesa novel method that may overcome these issues: we use hand
landmark detection over
time as afeature representation which is then fed into a Long Short-Term Memory (LSTM)
model. We achieve
avalidation accuracy and F1 Score of about 72% on detecting whether videos from the
Self-StimulatoryBehaviour
Dataset (SSBD) contain hand flapping or not. Our best model also predicts accurately
onexternal videos we
recorded of ourselves outside of the dataset it was trained on. This model usesless than
26,000 parameters,
providing promise for fast deployment into ubiquitous and wearabledigital settings for a
remote autism
diagnosis. </em> </p>
</div>
</div>
</div>
-->


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<section class="section" id="BibTeX">
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<h2 class="title">BibTeX</h2>
<pre><code>@article{park2021nerfies
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},
title = {Nerfies: Deformable Neural Radiance Fields},
journal = {ICCV},
year = {2021},
}</code></pre>
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<h2 class="title">Citation</h2>
<pre> <code>
@article{lakkapragada2021activity,
title={Activity Recognition for Autism Diagnosis},
author={Lakkapragada, Anish and Washington, Peter and Wall, Dennis},
journal={arXiv preprint arXiv:2108.07917},
year={2021}
}
</code> </pre>
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