SynthesisSensors (Basel, Switzerland)2022
Automatic Emotion Recognition in Children with Autism: A Systematic Literature Review.
Synthesis in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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
16 citing papers in PubMed, 61 citations in OpenAlex.
- Decoding Social Cues: A Scoping Review of AI-Supported Emotional Competencies and Their Impact on School-Aged Populations.Journal of Intelligence · 2026Review
- A Data-Driven Unsupervised Framework for Discovering Interpretable Gaze-Based Behavioral Pseudo-Zones in Children with Autism Spectrum Disorder.Diagnostics (Basel, Switzerland) · 2026Article
- Use of Automation Technologies and Data Mining in Speech Recognition for Autism.Brain and behavior · 2026Review
- Late Fusion Model for Emotion Recognition from Facial Expressions and Biosignals in a Dataset of Children with Autism Spectrum Disorder.Sensors (Basel, Switzerland) · 2025Article
- Spatiotemporal multimodal emotion recognition using Temporal video sequences and pose features for child emotion classification.Scientific reports · 2025Article
- Recognition of Basic Emotions Through Facial Expressions in Children with Attention-Deficit/Hyperactivity Disorder.Revista de neurologia · 2025Article
- Editorial: Artificial intelligence for child health and wellbeing.Frontiers in digital health · 2025Article
- Breaking barriers, building futures: Inclusive workplaces for autistic people.Frontiers in psychiatry · 2025Article
- AI technology to support adaptive functioning in neurodevelopmental conditions in everyday environments: a systematic review.NPJ digital medicine · 2024Article
- Using facial reaction analysis and machine learning to objectively assess the taste of medicines in children.PLOS digital health · 2024Article
- Relationship of autistic traits and the severity of fear of the COVID-19 pandemic in the general population.Frontiers in psychiatry · 2024Article
- In-the-Wild Affect Analysis of Children with ASD Using Heart Rate.Sensors (Basel, Switzerland) · 2023Article
- A Bimodal Emotion Recognition Approach through the Fusion of Electroencephalography and Facial Sequences.Diagnostics (Basel, Switzerland) · 2023Article
- Assessing the construct validity of a theory of mind battery adapted to Tunisian school-aged children.Frontiers in psychiatry · 2023Article
- Searching for Best Predictors of Paralinguistic Comprehension and Production of Emotions in Communication in Adults With Moderate Intellectual Disability.Frontiers in psychology · 2022Article
- Current status and challenges of artificial intelligence application in managing Children's emotions and attention.Digital healthReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 5 institutions in 4 countries.
Funding
Abstract
The automatic emotion recognition domain brings new methods and technologies that might be used to enhance therapy of children with autism. The paper aims at the exploration of methods and tools used to recognize emotions in children. It presents a literature review study that was performed using a systematic approach and PRISMA methodology for reporting quantitative and qualitative results. Diverse observation channels and modalities are used in the analyzed studies, including facial expressions, prosody of speech, and physiological signals. Regarding representation models, the basic emotions are the most frequently recognized, especially happiness, fear, and sadness. Both single-channel and multichannel approaches are applied, with a preference for the first one. For multimodal recognition, early fusion was the most frequently applied. SVM and neural networks were the most popular for building classifiers. Qualitative analysis revealed important clues on participant group construction and the most common combinations of modalities and methods. All channels are reported to be prone to some disturbance, and as a result, information on a specific symptoms of emotions might be temporarily or permanently unavailable. The challenges of proper stimuli, labelling methods, and the creation of open datasets were also identified.
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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.