ArticleChild and adolescent psychiatry and mental health2024
Objective and automatic assessment approach for diagnosing attention-deficit/hyperactivity disorder based on skeleton detection and classification analysis in outpatient videos.
Article in Child and adolescent psychiatry and mental health, 2024. 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.
- ADHD identification from real-life recorded video.Scientific reports · 2026Article
- Leveraging point-of-view camera and MediaPipe for objective hyperactivity assessment in preschool ADHD.Frontiers in psychiatry · 2026Article
- Attention deficit hyperactivity disorder assessment through objective measures: POV glasses and machine learning approach.Frontiers in psychiatry · 2026Article
- Objective diagnosis of attention-deficit/hyperactivity disorder by using load cell movement analysis under a smart chair in a simulated classroom: influence of sex and age.Journal of neurodevelopmental disorders · 2025Article
- Artificial intelligence for children with attention deficit/hyperactivity disorder: a scoping review.Experimental biology and medicine (Maywood, N.J.) · 2025Article
- Artificial intelligence in ADHD assessment: a comprehensive review of research progress from early screening to precise differential diagnosis.Frontiers in artificial intelligence · 2025Review
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Authors and funding
6 authors.
Funding
Abstract
backgroundAttention-deficit/hyperactivity disorder (ADHD) is diagnosed in accordance with Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria by using subjective observations and information provided by parents and teachers. However, subjective analysis often leads to overdiagnosis or underdiagnosis. There are two types of motor abnormalities in patients with ADHD. First, hyperactivity with fidgeting and restlessness is the major diagnostic criterium for ADHD. Second, developmental coordination disorder characterized by deficits in the acquisition and execution of coordinated motor skills is not the major criterium for ADHD. In this study, a machine learning-based approach was proposed to evaluate and classify 96 patients into ADHD (48 patients, 26 males and 22 females, with mean age: 7y6m) and non-ADHD (48 patients, 26 males and 22 females, with mean age: 7y8m) objectively and automatically by quantifying their movements and evaluating the restlessness scales.
methodsThis approach is mainly based on movement quantization through analysis of variance in patients' skeletons detected in outpatient videos. The patients' skeleton sequence in the video was detected using OpenPose and then characterized using 11 values of feature descriptors. A classification analysis based on six machine learning classifiers was performed to evaluate and compare the discriminating power of different feature combinations.
resultsThe results revealed that compared with the non-ADHD group, the ADHD group had significantly larger means in all cases of single feature descriptors. The single feature descriptor "thigh angle", with the values of 157.89 ± 32.81 and 15.37 ± 6.62 in ADHD and non-ADHD groups (p < 0.0001), achieved the best result (optimal cutoff, 42.39; accuracy, 91.03%; sensitivity, 90.25%; specificity, 91.86%; and AUC, 94.00%).
conclusionsThe proposed approach can be used to evaluate and classify patients into ADHD and non-ADHD objectively and automatically and can assist physicians in diagnosing ADHD.
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