{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T21:16:09Z","timestamp":1777583769591,"version":"3.51.4"},"reference-count":67,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,20]],"date-time":"2022-02-20T00:00:00Z","timestamp":1645315200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Erasmus Plus project of European Commission","award":["2019-1-PL01- KA203-06509"],"award-info":[{"award-number":["2019-1-PL01- KA203-06509"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s22041649","type":"journal-article","created":{"date-parts":[[2022,2,21]],"date-time":"2022-02-21T08:34:47Z","timestamp":1645432487000},"page":"1649","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":60,"title":["Automatic Emotion Recognition in Children with Autism: A Systematic Literature Review"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4728-689X","authenticated-orcid":false,"given":"Agnieszka","family":"Landowska","sequence":"first","affiliation":[{"name":"Faculty of Electronics, Telecommunications and Informatics, Gda\u0144sk University of Technology, 80-233 Gda\u0144sk, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6198-1234","authenticated-orcid":false,"given":"Aleksandra","family":"Karpus","sequence":"additional","affiliation":[{"name":"Faculty of Electronics, Telecommunications and Informatics, Gda\u0144sk University of Technology, 80-233 Gda\u0144sk, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0544-5573","authenticated-orcid":false,"given":"Teresa","family":"Zawadzka","sequence":"additional","affiliation":[{"name":"Faculty of Electronics, Telecommunications and Informatics, Gda\u0144sk University of Technology, 80-233 Gda\u0144sk, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1646-901X","authenticated-orcid":false,"given":"Ben","family":"Robins","sequence":"additional","affiliation":[{"name":"School of Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8929-0459","authenticated-orcid":false,"given":"Duygun","family":"Erol Barkana","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Electrical and Electronics Engineering, Yeditepe University, Istanbul 34755, Turkey"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4796-4766","authenticated-orcid":false,"given":"Hatice","family":"Kose","sequence":"additional","affiliation":[{"name":"Department of AI and Data Engineering, Istanbul Technical University, Istanbul 34467, Turkey"}]},{"given":"Tatjana","family":"Zorcec","sequence":"additional","affiliation":[{"name":"Center for Early Intervention for Children with Autism, University Children\u2019s Hospital, 1000 Skopje, North Macedonia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1178-917X","authenticated-orcid":false,"given":"Nicholas","family":"Cummins","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology & Neuroscience, King\u2019s College London, London WC2R 2LS, UK"},{"name":"Embedded Intelligence for Health Care & Wellbeing, University of Augsburg, 86159 Augsburg, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,20]]},"reference":[{"key":"ref_1","unstructured":"Jordan, R. 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