Analyzing Social Communication Deficits in Autism Using Wearable Sensors and Real-Time Affective Computing Systems


Authors : Paul Okugo Imoh; Omolola Dorcas; Joy Onma Enyejo

Volume/Issue : Volume 10 - 2025, Issue 5 - May

Google Scholar : https://tinyurl.com/5h39h25p

Scribd : https://tinyurl.com/36hymwcm

DOI : https://doi.org/10.38124/ijisrt/25may866

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Abstract : Social communication deficits are a hallmark characteristic of Autism Spectrum Disorder (ASD), often manifesting as challenges in interpreting and expressing emotions, maintaining eye contact, and engaging in reciprocal interactions. Traditional diagnostic and intervention methods, while valuable, can be limited by observer bias and lack of continuous monitoring. This review explores the emerging role of wearable sensors and real-time affective computing systems in assessing and addressing these deficits. By leveraging physiological signals (e.g., heart rate variability, skin conductance, and movement patterns) and behavioral data (e.g., gaze tracking, facial expressions, and speech prosody), wearable devices offer objective, non-intrusive insights into the emotional and communicative states of individuals with ASD. Affective computing systems, powered by machine learning and signal processing techniques, enable real-time analysis and adaptive feedback, potentially enhancing social skills training and personalized intervention. The paper examines current technologies, evaluation metrics, and case studies, while also addressing challenges such as data privacy, model generalizability, and user compliance. Finally, the review highlights future directions for integrating these technologies into clinical practice and educational environments to support early diagnosis, continuous monitoring, and intervention personalization in ASD care.

Keywords : Autism Spectrum Disorder (ASD); Social Communication Deficits; Wearable Sensor Technology; Affective Computing; Real-Time Emotion Recognition.

References :

  1. Abiodun, K., Ogbuonyalu, U. O., Dzamefe, S., Vera, E. N., Oyinlola, A., & Igba, E. (2023). Exploring Cross-Border Digital Assets Flows and Central Bank Digital Currency Risks to Capital Markets Financial Stability. International Journal of Scientific Research and Modern Technology, 2(11), 32–45. https://doi.org/10.38124/ijsrmt.v2i11.447
  2. Afzal, S., Khan, H. A., Khan, I. U., Piran, M. J., & Lee, J. W. (2023). A comprehensive survey on affective computing: Challenges, trends, applications, and future directions. arXiv. https://doi.org/10.485 50/arXiv.2305.07665
  3. Ahuja, D., Sarkar, A., Chandra, S., & Kumar, P. (2022). Wearable technology for monitoring behavioral and physiological responses in children with autism spectrum disorder: A literature review. Technology and Disability, 34(2), 69-84.
  4. Amiri, A. M., Peltier, N., Goldberg, C., & Mankodiya, K. (2017). WearSense: Detecting autism stereotypic behaviors through smartwatches. Proceedings of the 2017 IEEE International Conference on Body Sensor Networks (BSN), 1–6. https://doi.org/10.1109/ BSN.2017.35ResearchGate
  5. Anyibama, B. J., Orjinta, K. K., Omisogbon, T. O., Atalor, S. I., Daniels, E. O., Fadipe, E. & Galadima, D. A. (2025). Modern Agricultural Technologies for Sustainable Food Production: A Comprehensive Review of Technological Innovations and Their Impact on Global Food Systems. International Journal of Innovative Science and Research Technology ISSN No:-2456-2165 Volume 10, Issue 2 https://doi.org/10.5281/zenodo.14964384
  1. Azonuche T. I, Aigbogun, M. E & Enyejo, J. O. (2025). Investigating Hybrid Agile Frameworks Integrating Scrum and Devops for Continuous Delivery in Regulated Software Environments. International Journal of Innovative Science and Research Technology  Volume 10, Issue 4, ISSN No:-2456-2165 https://doi.org/10.38124/ijisrt/25apr1164
  2. Azonuche, T. I., & Enyejo, J. O. (2024). Agile Transformation in Public Sector IT Projects Using Lean-Agile Change Management and Enterprise Architecture Alignment. International Journal of Scientific Research and Modern Technology, 3(8), 21–39. https://doi.org/10.38124/ijsrmt.v3i8.432
  3. Azonuche, T. I., & Enyejo, J. O. (2024). Evaluating the Impact of Agile Scaling Frameworks on Productivity and Quality in Large-Scale Fintech Software Development. International Journal of Scientific Research and Modern Technology, 3(6), 57–69. https://doi.org/10.38124/ijsrmt.v3i6.449
  4. Azonuche, T. I., & Enyejo, J. O. (2024). Exploring AI-Powered Sprint Planning Optimization Using Machine Learning for Dynamic Backlog Prioritization and Risk Mitigation. International Journal of Scientific Research and Modern Technology, 3(8), 40–57. https://doi.org/10.38124/ijsrmt.v3i8.448.
  5. Azonuche, T. I., & Enyejo, J. O. (2025). Adaptive Risk Management in Agile Projects Using Predictive Analytics and Real-Time Velocity Data Visualization Dashboard. International Journal of Innovative Science and Research Technology Volume 10, Issue 4, April – 2025 ISSN No:-2456-2165 https://doi.org/10.38124/ijisrt/25apr2002
  6. Bazgir, O., Mohammadi, Z., & Habibi, S. A. H. (2019). Emotion recognition with machine learning using EEG signals. arXiv. https://arxiv.org/abs/1903.07272
  7. Benssassi, E. M., Gomez, J. C., Boyd, L. E., Hayes, G. R., & Ye, J. (2018). Wearable assistive technologies for autism: opportunities and challenges. IEEE Pervasive Computing, 17(2), 11-21.
  8. Binns, A., McMullan, M., & Cairns, P. (2018). The ethics of wearable health technologies: A survey of the challenges and future directions. International Journal of Human-Computer Studies, 112, 1-13. https://doi.org/10.1016/j.ijhcs.2018.02.005
  9. Chengoden, R., Victor, N., Huynh-The, T., Yenduri, G., Jhaveri, R. H., Alazab, M., ... & Gadekallu, T. R. (2023). Metaverse for healthcare: a survey on potential applications, challenges and future directions. Ieee Access, 11, 12765-12795.
  10. Costescu, C., Pitariu, D., David, C., & Roșan, A. (2022). Social communication predictors in autism spectrum disorder: A theoretical review. Journal of Experimental Psychopathology, 13(3), 20438087 2211069. https://doi.org/10.1177/204380872211069 55CoLab+1SAGE Journals+1
  11. El Kaliouby, R., Picard, R. W., & Baron-Cohen, S. (2006). Affective computing and autism. Annals of the New York Academy of Sciences, 1093(1), 228–248. https://doi.org/10.1196/annals.1382.016firah.org+1Wikipedia+1
  12. Fioriello, F., Maugeri, A., D'Alvia, L., Pittella, E., Piuzzi, E., Rizzuto, E., Del Prete, Z., Manti, F., & Sogos, C. (2020). A wearable heart rate measurement device for children with autism spectrum disorder. Scientific Reports, 10(1), 18659. https://doi.org/10. 1038/s41598-020-75768-1
  13. Francese, R., & Yang, X. (2022). Supporting autism spectrum disorder screening and intervention with machine learning and wearables: a systematic literature review. Complex & Intelligent Systems, 8(5 ), 3659-3674.
  14. Fuller, E. A., & Kaiser, A. P. (2020). The effects of early intervention on social communication outcomes for children with autism spectrum disorder: A meta-analysis. Journal of Autism and Developmental Disorders, 50(5), 1683–1700. https://doi.org/10.10 07/s10803-019-03927-z
  15. Gao, X., Yin, L., Tian, S., Huang, Y., & Ji, Q. (2024). Wearable technology for signal acquisition and interactive feedback in autism spectrum disorder intervention: A review. IEEE Sensors Journal.
  16. Gotts, S. J., Simmons, W. K., Milbury, L. A., Wallace, G. L., Cox, R. W., & Martin, A. (2012). Fractionation of social brain circuits in autism spectrum disorders. Brain, 135(9), 2711–2725. https://doi.org/10.1 093/brain/aws160
  17. Harkin, B. & Efron, D. (2022). Children with autism spectrum disorder – consultation tips for GPs
  18. Hauqing, Hong, Ling, Dai & Xiulin, Z. (2025). Advances in Wearable Sensors for Learning Analytics: Trends, Challenges, and Prospects. https://www.mdpi. com/1424-8220/25/9/2714
  19. https://medicinetoday.com.au/mt/2022/march/regular-series/children-autism-spe ctrum-disorder-%E2%80 %93-consultation-tips-gps
  20. Imoh, P. O., & Idoko, I. P. (2022). Gene-Environment Interactions and Epigenetic Regulation in Autism Etiology through Multi-Omics Integration and Computational Biology Approaches. International Journal of Scientific Research and Modern Technology, 1(8), 1–16. https://doi.org/10.3812 4/ijsrmt.v1i8.463
  21. Imoh, P. O., & Idoko, I. P. (2023). Evaluating the Efficacy of Digital Therapeutics and Virtual Reality Interventions in Autism Spectrum Disorder Treatment. International Journal of Scientific Research and Modern Technology, 2(8), 1–16. https://doi.org/10.381 24/ijsrmt.v2i8.462 
  22. Imoh, P. O., Adeniyi, M., Ayoola, V. B., & Enyejo, J. O. (2024). Advancing Early Autism Diagnosis Using Multimodal Neuroimaging and Ai-Driven Biomarkers for Neurodevelopmental Trajectory Prediction. International Journal of Scientific Research and Modern Technology, 3(6), 40–56. https://doi.org/10.3 8124/ijsrmt.v3i6.413
  23. Keshav, N. U., Salisbury, J. P., Vahabzadeh, A., & Sahin, N. T. (2017). Wearable technology and analytics as a complementary toolkit to facilitate autism care: From research to practice and back. Frontiers in Neuroscience, 11, 515. https://doi.org/ 10.3389/fnins.2017.00515
  24. Keshav, N. U., Salisbury, J. P., Vahabzadeh, A., & Sahin, N. T. (2018). Social communication coaching smartglasses: Well tolerated in a diverse sample of children and adults with autism. JMIR mHealth and uHealth, 6(9), e10730. https://doi.org/10.2196/10730
  25. Liu, R., Salisbury, J. P., Vahabzadeh, A., & Sahin, N.
  26. T. (2017). Feasibility of an Autism-Focused Augmented Reality Smartglasses System for Social Communication and Behavioral Coaching. Frontiers in Pediatrics, 5, 145. https://doi.org/10. 3389/fped.2017.00145
  27. Liu, Y., Ma, Y., & Liu, Y. (2023). Deep learning framework for subject-independent emotion detection using wireless signals. arXiv. https://arxiv.or g/abs/2006.04744
  28. Lord, C., Charman, T., Havdahl, A., Carbone, P., Anagnostou, E., Boyd, B., ... & McCauley, J. B. (2022). The Lancet Commission on the future of care and clinical research in autism. The Lancet, 399(10321), 271-334. https://doi.org/10.1016/S0140-6736(21)01541-5Wikipedia
  29. Messinger, D. S., Young, G. S., Ozonoff, S., Dobkins, K., Carter, A., Zwaigenbaum, L., ... & Sigman, M. (2015). Beyond autism: A baby siblings research consortium study of high-risk children at three years of age. Journal of the American Academy of Child & Adolescent Psychiatry, 54(4), 300–308.e1. https://doi.org/10.1016/j.jaac.2015.01.014
  30. Mundy, P., & Neal, A. R. (2001). Neural plasticity, joint attention, and a transactional social-orienting model of autism. International Review of Research in Mental Retardation, 23, 139–168. https://doi.org/10.10 16/S0074-7750(00)80007-1
  31. Newbutt, N., Sung, C., Kuo, H. J., & Leahy, M. J. (2017). The acceptance, challenges, and future applications of wearable technology and virtual reality to support people with autism spectrum disorders. Recent advances in technologies for inclusive well-being: From worn to off-body sensing, virtual worlds, and games for serious applications, 221-241.
  32. Ogbuonyalu, U. O, Abiodun, K., Dzamefe, S., Vera, E. N., Oyinlola, A. & Igba, E. (2025). Integrating Decentralized Finance Protocols with Systemic Risk Frameworks for Enhanced Capital Markets Stability and Regulatory Oversight. International Journal of Innovative Science and Research Technology Volume 10, Issue 4, ISSN No:-2456-2165 https://doi.org/ 10.38124/ijisrt/25apr1165
  33. Ogbuonyalu, U. O., Abiodun, K., Dzamefe, S., Vera, E. N., Oyinlola, A., &  Igba. E. (2024). Assessing Artificial Intelligence Driven Algorithmic Trading Implications on Market Liquidity Risk and Financial Systemic Vulnerabilities. International Journal of Scientific Research and Modern Technology, 3(4), 18–21. https://doi.org/10.38124/ijsrmt.v3i4.433
  1. Orefice, L. L., Zimmerman, A. L., Chirila, A. M., Sleboda, S. J., Head, J. P., & Ginty, D. D. (2016). Peripheral mechanosensory neuron dysfunction underlies tactile and behavioral deficits in mouse models of ASDs. Cell, 166(2), 299–313. https://doi.org/10.1016/j.cell.2016.05.033
  2. Palermo, E. H., Young, A. V., Deswert, S., Brown, A., Goldberg, M., Sultanik, E., ... & Nuske, H. J. (2023). A digital mental health app incorporating wearable biosensing for teachers of children on the autism spectrum to support emotion regulation: Protocol for a pilot randomized controlled trial. JMIR Research Protocols, 12(1), e45852.
  3. Picard, R. W. (2010). Emotion research by the people, for the people. Emotion Review, 2(3), 250–254. https://doi.org/10.1177/1754073910371980
  4. Pierce, K., Gazestani, V. H., Bacon, E., Courchesne, E., & Cheng, A. (2021). Get SET Early to Identify and Treatment Refer Autism Spectrum Disorder at 1 Year and Discover Factors That Influence Early Diagnosis. The Journal of Pediatrics, 239, 208–214.e2. https://doi.org/10.1016/j.jpeds.2021.08.017Wikipedia
  5. Qiu, Y., Zhao, X., & Zhang, Y. (2021). Affective computing for social interaction: The impact of wearable technologies on social skill development in children with autism. Autism Research, 14(3), 543-556. https://doi.org/10.1002/aur.2427
  6. Sahin, N. T., Abdus-Sabur, R., Keshav, N. U., Liu, R., Salisbury, J. P. & Vahabzadeh, A. (2018). Case Study of a Digital Augmented Reality Intervention for Autism in School Classrooms: Associated With Improved Social Communication, Cognition, and Motivation via Educator and ParentAssessment.htt ps://www.frontiersin.org/journals/education/articles/10.3389/feduc.2018.00057/full
  7. Tager-Flusberg, H. (2006). Defining language phenotypes in autism. Clinical Neuroscience Research, 6(3-4), 219–224. https://doi.org/10.10 16/j.cnr.2006.06.007
  8. Talaat, F. M. (2023). Real-time facial emotion recognition system among children with autism based on deep learning and IoT. Neural Computing and Applications, 35(17), 12717-12728.
  9. Torrado, J. C., Gomez, J., & Montoro, G. (2017). Emotional self-regulation of individuals with autism spectrum disorders: smartwatches for monitoring and interaction. Sensors, 17(6), 1359.
  10. Torrado, J. C., Gomez, J., & Montoro, G. (2017). Emotional self-regulation of individuals with autism spectrum disorders: smartwatches for monitoring and interaction. Sensors, 17(6), 1359.
  11. Zwaigenbaum, L., Bauman, M. L., Choueiri, R., Kasari, C., Carter, A., Granpeesheh, D., ... & Natowicz, M. R. (2015). Early intervention for children with autism spectrum disorder under 3 years of age: Recommendations for practice and research. Pediatrics, 136(Supplement 1), S60-S81. https://doi. org/10.1542/peds.2014-3667E

 

Social communication deficits are a hallmark characteristic of Autism Spectrum Disorder (ASD), often manifesting as challenges in interpreting and expressing emotions, maintaining eye contact, and engaging in reciprocal interactions. Traditional diagnostic and intervention methods, while valuable, can be limited by observer bias and lack of continuous monitoring. This review explores the emerging role of wearable sensors and real-time affective computing systems in assessing and addressing these deficits. By leveraging physiological signals (e.g., heart rate variability, skin conductance, and movement patterns) and behavioral data (e.g., gaze tracking, facial expressions, and speech prosody), wearable devices offer objective, non-intrusive insights into the emotional and communicative states of individuals with ASD. Affective computing systems, powered by machine learning and signal processing techniques, enable real-time analysis and adaptive feedback, potentially enhancing social skills training and personalized intervention. The paper examines current technologies, evaluation metrics, and case studies, while also addressing challenges such as data privacy, model generalizability, and user compliance. Finally, the review highlights future directions for integrating these technologies into clinical practice and educational environments to support early diagnosis, continuous monitoring, and intervention personalization in ASD care.

Keywords : Autism Spectrum Disorder (ASD); Social Communication Deficits; Wearable Sensor Technology; Affective Computing; Real-Time Emotion Recognition.

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