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73+ < h1 class ="title is-1 publication-title "> Activity Recognition for Autism Diagnosis</ h1 >
74+ < div class ="is-size-5 publication-authors ">
75+ < span class ="author-block ">
76+ < a href ="https://keunhong.com "> Anish Lakkapragada</ a > < sup > 1</ sup > ,</ span >
77+ < span class ="author-block ">
78+ < a href ="https://utkarshsinha.com "> Peter Washington</ a > < sup > 2</ sup > ,</ span >
79+ < span class ="author-block ">
80+ < a href ="https://jonbarron.info "> Dennis P. Wall</ a > < sup > 2</ sup > ,
81+ </ span >
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84+ < div class ="is-size-5 publication-authors ">
85+ < span class ="author-block "> < sup > 1</ sup > Lynbrook High School</ span >
86+ < span class ="author-block "> < sup > 2</ sup > Stanford University</ span >
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98+ <span>Paper</span>
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117+ <span>Video</span>
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123+ < a href ="https://github.com/anish-lakkapragada/ActivRecognition-Autism-Diagnosis "
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217+ <!-- Abstract. -->
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220+ < h2 class ="title is-3 "> Abstract</ h2 >
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222+ < p > < em > A formal autism diagnosis can be an inefficient and lengthy process. Families may wait months orlonger before
223+ receiving a diagnosis for their child. One approach to lessen delays is the use digitaltechnologies to detect
224+ the presence of behaviors indicative of autism, which in aggregate may leadto remote and automated diagnostics.
225+ One of the strongest indicators of autism is stimming, whichincludes repetitive, self-stimulatory behaviors such
226+ as hand flapping, headbanging, and spinning.Using computer vision to detect hand flapping is especially
227+ difficult due to the sparsity of publictraining data in this space and excessive shakiness and motion in such
228+ data. Our work demonstratesa novel method that may overcome these issues: we use hand landmark detection over
229+ time as afeature representation which is then fed into a Long Short-Term Memory (LSTM) model. We achieve
230+ avalidation accuracy and F1 Score of about 72% on detecting whether videos from the Self-StimulatoryBehaviour
231+ Dataset (SSBD) contain hand flapping or not. Our best model also predicts accurately onexternal videos we
232+ recorded of ourselves outside of the dataset it was trained on. This model usesless than 26,000 parameters,
233+ providing promise for fast deployment into ubiquitous and wearabledigital settings for a remote autism
234+ diagnosis. </ em > </ p >
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237+ </ div >
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264+ < h2 class ="title "> BibTeX</ h2 >
265+ < pre > < code > @article{park2021nerfies
266+ 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},
267+ title = {Nerfies: Deformable Neural Radiance Fields},
268+ journal = {ICCV},
269+ year = {2021},
270+ }</ code > </ pre >
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