ArticleJournal of autism and developmental disorders2020
Detecting and Classifying Self-injurious Behavior in Autism Spectrum Disorder Using Machine Learning Techniques.
Article in Journal of autism and developmental disorders, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 60 citations in OpenAlex.
- Wearable Solutions Using Physiological Signals for Stress Monitoring on Individuals with Autism Spectrum Disorder (ASD): A Systematic Literature Review.Sensors (Basel, Switzerland) · 2024Pooled it
- Using Wearable Technology to Predict the Occurrence of Severe Behavior Problems among Neurodiverse Individuals: A Systematic Review.Perspectives on behavior science · 2026Review
- Effectiveness and Usability of Artificial Intelligence and/or Machine Learning Enabled Wrist and Ankle Wearables for Physiological and Behavioral Monitoring in Children and Adolescents With Autism Spectrum Disorder: A Systematic Review.Journal of autism and developmental disorders · 2026Article
- Using Wearables in Mental Health Care for Children and Adolescents: A Scoping Review.Research on child and adolescent psychopathology · 2026Article
- Approaches for Predicting the Occurrence of Challenging Behaviors in Individuals with Autism Spectrum Disorder: A Narrative Review.Journal of personalized medicine · 2025Review
- Wearable sensors in paediatric neurology.Developmental medicine and child neurology · 2025Review
- Using machine learning to identify features associated with different types of self-injurious behaviors in autistic youth.Psychological medicine · 2025Article
- Perceptions of Machine Learning among Therapists Practicing Applied Behavior Analysis: A National Survey.Behavior analysis in practice · 2024Article
- Characterizing associations between emotion dysregulation, anxiety, and repetitive behaviors in autistic youth with intellectual disability.Autism research : official journal of the International Society for Autism Research · 2024Article
- Engagnition: A multi-dimensional dataset for engagement recognition of children with autism spectrum disorder.Scientific data · 2024Article
- Starting the Conversation Around the Ethical Use of Artificial Intelligence in Applied Behavior Analysis.Behavior analysis in practice · 2024Review
- The Promises and Possibilities of Artificial Intelligence in the Delivery of Behavior Analytic Services.Behavior analysis in practice · 2024Review
- Predicting Problematic Behavior in Autism Spectrum Disorder Using Medical History and Environmental Data.Journal of personalized medicine · 2023Article
- Detection of autism spectrum disorder (ASD) in children and adults using machine learning.Scientific reports · 2023Article
- Feasibility of accelerometer technology with individuals with autism spectrum disorder referred for aggression, disruption, and self injury.Research in autism spectrum disorders · 2022Article
- Internet of Things (IoT)-Enhanced Applied Behavior Analysis (ABA) for Special Education Needs.Sensors (Basel, Switzerland) · 2021Article
- A Predictive Multimodal Framework to Alert Caregivers of Problem Behaviors for Children with ASD (PreMAC).Sensors (Basel, Switzerland) · 2021Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Traditional self-injurious behavior (SIB) management can place compliance demands on the caregiver and have low ecological validity and accuracy. To support an SIB monitoring system for autism spectrum disorder (ASD), we evaluated machine learning methods for detecting and distinguishing diverse SIB types. SIB episodes were captured with body-worn accelerometers from children with ASD and SIB. The highest detection accuracy was found with k-nearest neighbors and support vector machines (up to 99.1% for individuals and 94.6% for grouped participants), and classification efficiency was quite high (offline processing at ~ 0.1 ms/observation). Our results provide an initial step toward creating a continuous and objective smart SIB monitoring system, which could in turn facilitate the future care of a pervasive concern in ASD.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.