This paper is in the following e-collection/theme issue:

Biomechanics (9) Machine Learning (1748) Autism Spectrum Disorder (ASD) (130) Artificial Intelligence (1754) Development of Novel Medical Devices and Innovations for Existing Devices (89)

Published on in Vol 7, No 1 (2022): Jan-Jun

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/33771, first published .
The Classification of Abnormal Hand Movement to Aid in Autism Detection: Machine Learning Study

The Classification of Abnormal Hand Movement to Aid in Autism Detection: Machine Learning Study

The Classification of Abnormal Hand Movement to Aid in Autism Detection: Machine Learning Study

Authors of this article:

Anish Lakkapragada1 Author Orcid Image ;   Aaron Kline1 Author Orcid Image ;   Onur Cezmi Mutlu2 Author Orcid Image ;   Kelley Paskov3 Author Orcid Image ;   Brianna Chrisman4 Author Orcid Image ;   Nathaniel Stockham5 Author Orcid Image ;   Peter Washington6 Author Orcid Image ;   Dennis Paul Wall3 Author Orcid Image
Article Authors Cited by (23) Tweetations (13) Metrics

Journals

  1. deLeyer‐Tiarks J, Li M, Levine‐Schmitt M, Andrade B, Bray M, Peters E. Advancing autism technology. Psychology in the Schools 2023;60(2):495 View
  2. Banerjee A, Mutlu O, Kline A, Surabhi S, Washington P, Wall D. Training and Profiling a Pediatric Facial Expression Classifier for Children on Mobile Devices: Machine Learning Study. JMIR Formative Research 2023;7:e39917 View
  3. Putra I, Nurhayati O, Eridani D. Human Action Recognition (HAR) Classification Using MediaPipe and Long Short-Term Memory (LSTM). TEKNIK 2022;43(2):190 View
  4. Uddin M, Shahriar M, Mahamood M, Alnajjar F, Pramanik M, Ahad M. Deep learning with image-based autism spectrum disorder analysis: A systematic review. Engineering Applications of Artificial Intelligence 2024;127:107185 View
  5. Parab S, Boster J, Washington P. Parkinson Disease Recognition Using a Gamified Website: Machine Learning Development and Usability Study. JMIR Formative Research 2023;7:e49898 View
  6. Jaiswal A, Washington P. Using #ActuallyAutistic on Twitter for Precision Diagnosis of Autism Spectrum Disorder: Machine Learning Study. JMIR Formative Research 2024;8:e52660 View
  7. Jaiswal A, Kruiper R, Rasool A, Nandkeolyar A, Wall D, Washington P. Digitally Diagnosing Multiple Developmental Delays Using Crowdsourcing Fused With Machine Learning: Protocol for a Human-in-the-Loop Machine Learning Study. JMIR Research Protocols 2024;13:e52205 View
  8. Washington P. A Perspective on Crowdsourcing and Human-in-the-Loop Workflows in Precision Health. Journal of Medical Internet Research 2024;26:e51138 View
  9. Kargarandehkordi A, Kaisti M, Washington P. Personalization of Affective Models Using Classical Machine Learning: A Feasibility Study. Applied Sciences 2024;14(4):1337 View
  10. Barami T, Manelis-Baram L, Kaiser H, Ilan M, Slobodkin A, Hadashi O, Hadad D, Waissengreen D, Nitzan T, Menashe I, Michaelovsky A, Begin M, Zachor D, Sadaka Y, Koler J, Zagdon D, Meiri G, Azencot O, Sharf A, Dinstein I. Automated Analysis of Stereotypical Movements in Videos of Children With Autism Spectrum Disorder. JAMA Network Open 2024;7(9):e2432851 View
  11. Sá R, Michelassi G, Butrico D, Franco F, Sumiya F, Portolese J, Brentani H, Nunes F, Machado-Lima A. Enhancing ensemble classifiers utilizing gaze tracking data for autism spectrum disorder diagnosis. Computers in Biology and Medicine 2024;182:109184 View
  12. Natraj S, Kojovic N, Maillart T, Schaer M, Varrasi S. Video-audio neural network ensemble for comprehensive screening of autism spectrum disorder in young children. PLOS ONE 2024;19(10):e0308388 View
  13. Wagh V, Scott M, Kraeutner S. Quantifying Similarities Between MediaPipe and a Known Standard to Address Issues in Tracking 2D Upper Limb Trajectories: Proof of Concept Study. JMIR Formative Research 2024;8:e56682 View
  14. Yang Z, Zhang Y, Ning J, Wang X, Wu Z. Early Diagnosis of Autism: A Review of Video-Based Motion Analysis and Deep Learning Techniques. IEEE Access 2025;13:2903 View
  15. R. A, Senthilkumar R. DSVTN-ASD: Detection of Stereotypical Behaviors in Individuals with Autism Spectrum Disorder using a Dual Self-Supervised Video Transformer Network. Neurocomputing 2025;624:129397 View
  16. Wedasingha N, Samarasinghe P, Senevirathna L, Papandrea M, Puiatti A. Autoencoder based data clustering for identifying anomalous repetitive hand movements, and behavioral transition patterns in children. Physical and Engineering Sciences in Medicine 2025;48(1):221 View

Books/Policy Documents

  1. Dundi U, Kanaparthi V, Bandaru R, Umaiorubagam G. Computational Intelligence in Data Science. View
  2. Radha S, Abinaya M, Divya Darshini K, Kamalnath K, Jayanthi P. Soft Computing for Security Applications. View

Conference Proceedings

  1. Gao J, Jiang X, Yang Y, Li D, Qiu L. ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Unsupervised Video Anomaly Detection For Stereotypical Behaviours in Autism View
  2. Al-Rammah F, Masmoudi M, Jarraya S. 2022 IEEE/ACS 19th International Conference on Computer Systems and Applications (AICCSA). CNN-LSTM Based Approach for Analyzing and Detecting Stereotypical Motor Movements of Autistic Children in Pre-Meltdown Crisis View
  3. Shrivastava T, Singh A, Singh V, Agrawal A. 2024 11th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI). ASDenseFormer: A Video-Based Autistic Self-Stimulatory Gesture Recognition Using DenseNet121 and Transformer Model View
  4. B S, Velammal B. 2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES). Detection of Autism Spectrum Disorder Using Deep Learning Model View
  5. Jaiswal A, Wall D, Washington P. 2024 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI). Challenges in the Differential Classification of Individual Diagnoses from Co-Occurring Autism and ADHD Using Survey Data View