ArticleSensors (Basel, Switzerland)2021
Application of Skeleton Data and Long Short-Term Memory in Action Recognition of Children with Autism Spectrum Disorder.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A Scoping Review of AI-Based Approaches for Detecting Autism Traits Using Voice and Behavioral Data.Bioengineering (Basel, Switzerland) · 2025Review
- Overlap and Differences of Autism and ADHD: Digital Phenotyping of Movement and Communication During Development.bioRxiv : the preprint server for biology · 2025Article
- Semi-Automated Multi-Label Classification of Autistic Mannerisms by Machine Learning on Post Hoc Skeletal Tracking.Autism research : official journal of the International Society for Autism Research · 2025Article
- Integrating OpenPose and SVM for Quantitative Postural Analysis in Young Adults: A Temporal-Spatial Approach.Bioengineering (Basel, Switzerland) · 2024Article
- Identifying activity level related movement features of children with ASD based on ADOS videos.Scientific reports · 2023Article
- Identifying the Posture of Young Adults in Walking Videos by Using a Fusion Artificial Intelligent Method.Biosensors · 2022Article
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Authors and funding
4 authors.
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Abstract
The recognition of stereotyped action is one of the core diagnostic criteria of Autism Spectrum Disorder (ASD). However, it mainly relies on parent interviews and clinical observations, which lead to a long diagnosis cycle and prevents the ASD children from timely treatment. To speed up the recognition process of stereotyped actions, a method based on skeleton data and Long Short-Term Memory (LSTM) is proposed in this paper. In the first stage of our method, the OpenPose algorithm is used to obtain the initial skeleton data from the video of ASD children. Furthermore, four denoising methods are proposed to eliminate the noise of the initial skeleton data. In the second stage, we track multiple ASD children in the same scene by matching distance between current skeletons and previous skeletons. In the last stage, the neural network based on LSTM is proposed to classify the ASD children's actions. The performed experiments show that our proposed method is effective for ASD children's action recognition. Compared to the previous traditional schemes, our scheme has higher accuracy and is almost non-invasive for ASD children.
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