Skip to main content
SpringerLink
Log in

A review on diagnostic autism spectrum disorder approaches based on the Internet of Things and Machine Learning

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Children with autism spectrum disorders (ASDs) have some disturbance activities. Usually, they cannot speak fluently. Instead, they use gestures and pointing words to make a relationship. Hence, understanding their needs is one of the most challenging tasks for caregivers, but early diagnosis of the disease can make it much easier. The lack of verbal and nonverbal communications can be eliminated by assistive technologies and the Internet of Things (IoT). The IoT-based systems help to diagnose and improve the patients’ lives through applying Deep Learning (DL) and Machine Learning (ML) algorithms. This paper provides a systematic review of the ASD approaches in the context of IoT devices. The main goal of this review is to recognize significant research trends in the field of IoT-based healthcare. Also, a technical taxonomy is presented to classify the existing papers on the ASD methods and algorithms. A statistical and functional analysis of reviewed ASD approaches is provided based on evaluation metrics such as accuracy and sensitivity.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Baio J (2014) Prevalence of autism spectrum disorder among children aged 8 years—autism and developmental disabilities monitoring network, 11 Sites, United States, 2010. Surveillance Summaries, vol 63, pp 1–21

  2. Souri A et al (2019) A systematic review of IoT communication strategies for an efficient smart environment. Tran Emerg Telecommun Technol. https://doi.org/10.1002/ett.3736

    Article  Google Scholar 

  3. Pashazadeh A, Navimipour NJ (2018) Big data handling mechanisms in the healthcare applications: a comprehensive and systematic literature review. J Biomed Inform 82:47–62

    Article  Google Scholar 

  4. Souri A et al (2020) A new machine learning-based healthcare monitoring model for student’s condition diagnosis in Internet of Things environment. Soft Comput. https://doi.org/10.1007/s00500-020-05003-6

    Article  Google Scholar 

  5. Razzaghzadeh S et al (2017) Probabilistic modeling to achieve load balancing in Expert Clouds. Ad Hoc Netw 59:12–23

    Article  Google Scholar 

  6. Seyyedrazzagi E, Navimipour NJ (2017) Disease genes prioritizing mechanisms: a comprehensive and systematic literature review. Network Model Anal Health Inf Bioinf 6(1):13

    Article  Google Scholar 

  7. Souri A, Hosseini R (2018) A state-of-the-art survey of malware detection approaches using data mining techniques. Human Centric Comput Inf Sci 8(1):3

    Article  Google Scholar 

  8. Jayatilleka I, Halgamuge MN (2020) Internet of Things in healthcare: Smart devices, sensors, and systems related to diseases and health conditions, in Real-Time Data Analytics for Large Scale Sensor Data. Elsevier, Amsterdam, pp 1–35

    Google Scholar 

  9. Helmy J, Helmy A (2016) The Alzimio App for Dementia, Autism & Alzheimer’s: using novel activity recognition algorithms and geofencing. In: 2016 IEEE International Conference on Smart Computing (SMARTCOMP). IEEE, New York

  10. Aisuwarya R, Ferdian R (2019) Monitoring and notification system of the position of a person with dementia based on Internet of Things (IoT) and google maps. In: 2019 International Conference on Electrical Engineering and Computer Science (ICECOS). IEEE, New York

  11. Ida Seraphim B, Samuel Rao L, Joshi S (2018) Survey on early detection of autism using data mining techniques. Int J Eng Technol 7(2.24):79–80

    Article  Google Scholar 

  12. Leroy G, Irmscher A, Charlop-Christy M, Kuriakose S, Pishori A, Wurzman L et al (2006) Data mining techniques to study therapy success. In: International Conference on Data Mining, 26–29 June 2006. Institute of Electrical and Electronics Engineers (IEEE), Las Vegas, USA, pp 1–7

  13. Hyde KK et al (2019) Applications of supervised Machine Learning in Autism Spectrum Disorder research: a review. Rev J Autism Dev Disord 6(2):128–146

    Article  Google Scholar 

  14. Koumpouros Y, Kafazis T (2019) Wearables and mobile technologies in Autism Spectrum Disorder interventions: a systematic literature review. Res Autism Spectr Disord 66:101405

    Article  Google Scholar 

  15. Robins B et al (2004) Robot-mediated joint attention in children with autism: a case study in robot-human interaction. Interaction studies 5(2):161–198

    Article  Google Scholar 

  16. Kitchenham B et al (2009) Systematic literature reviews in software engineering—a systematic literature review. Inf Softw Technol 51(1):7–15

    Article  Google Scholar 

  17. Stone PW (2002) Popping the (PICO) question in research and evidence-based practice. Appl Nurs Res 15(3):197–198

    Article  Google Scholar 

  18. Sundhara Kumar KB, Bairavi K (2016) IoT based health monitoring system for autistic patients. In: Proceedings of the 3rd International Symposium on Big Data and Cloud Computing Challenges (ISBCC-16’). p. 371–376

  19. Yang S et al (2018) IoT structured long-term wearable social sensing for mental wellbeing. IEEE Internet of Things J 6(2):3652–3662

    Article  Google Scholar 

  20. Krishna C, Sampath N (2017) Healthcare monitoring system based on IoT. In: 2017 2nd International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS). IEEE, New York

  21. Eshetu Y, Bhuyan P, Behura A (2018) A service-oriented IoT system to support individuals with ASD. In: 2018 International Conference on Communication, Computing and Internet of Things (IC3IoT). IEEE, New York

  22. Mano LY et al (2016) Exploiting IoT technologies for enhancing Health Smart Homes through patient identification and emotion recognition. Comput Commun 89–90:178–190

    Article  Google Scholar 

  23. Lavanya K, Anitha SM, Joveka J, Priyatharshni R, Mahipal S (2019) Emotion recognition of autism children using IoT. Int J Appl Eng Res 14(6). ISSN 0973-4562

  24. Amiri AM et al (2017) WearSense: detecting autism stereotypic behaviors through smartwatches. Healthcare (Basel) 5(1):11

    Article  Google Scholar 

  25. Moradi H et al (2017) Autism screening using an intelligent toy car, in ubiquitous computing and ambient intelligence. Springer, Cham, pp 817–827

    Google Scholar 

  26. Manju T, Padmavathi S (2018) Tamilselvi D (2018) A rehabilitation therapy for autism spectrum disorder using virtual reality, in smart secure systems—IoT and analytics perspective. Springer, Singapore, pp 328–336

    Google Scholar 

  27. Abdolzadegan D, Moattar MH, Ghoshuni M (2020) A robust method for early diagnosis of autism spectrum disorder from EEG signals based on feature selection and DBSCAN method. Biocybern Biomed Eng 40(1):482–493

    Article  Google Scholar 

  28. Shankar VG, Sisodia DS, Chandrakar P (2020) DataAutism: an early detection framework of autism in infants using data science, in data management, analytics and innovation. Springer, New York, pp 167–178

    Google Scholar 

  29. Praveena TL, Lakshmi NM (2020) Detection of autism spectrum disorder effectively using modified regression algorithm, in emerging research in data engineering systems and computer communications. Springer, New York, pp 163–175

    Book  Google Scholar 

  30. Alam ME et al (2018) An IoT-belief rule base smart system to assess autism. In: 2018 4th international conference on electrical engineering and information & communication technology (iCEEiCT). IEEE, New York

  31. Rahman MA, Bhuiyan M (2015) IOT enabled sensor in multimodal intelligent applications for children with special needs. In: 2015 Internet Technologies and Applications (ITA). IEEE, New York

  32. Shi Y et al (2017) An experimental wearable iot for data-driven management of autism. In: 2017 9th International Conference on Communication Systems and Networks (COMSNETS). IEEE, New York

  33. Sumi AI et al (2018) FASSERT: a fuzzy assistive system for children with autism using internet of things. brain Informatics. Springer, Cham, pp 403–412

    Chapter  Google Scholar 

  34. Tang TY (2016) Helping neuro-typical individuals to “Read” the emotion of children with autism spectrum disorder. In: proceedings of the 15th international conference on interaction design and children-IDC’16. p. 666–671

  35. Tang TY, Winoto P (2018) An interactive picture exchange communication system (PECS) embedded with augmented aids enabled by IoT and sensing technologies for chinese individuals with autism. In: Proceedings of the 2018 ACM international joint conference and 2018 international symposium on pervasive and ubiquitous computing and wearable computers-UbiComp’18. p. 299–302

  36. Liu X et al (2016) An interactive training system of motor learning by imitation and speech instructions for children with autism. In: 2016 9th International Conference on Human System Interactions (HSI). IEEE, New York

  37. Einarson D, Sommarlund P, Segelström F (2016) IoT-support systems for parents with ADHD an autism. In: 2016 International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS). IEEE, New York

  38. Grimaldi R, Palatucci M, Medaglia CM (2016) Smart objects for autism: a proposal of classification of the objects based on the autism symptoms, in universal access in human-computer interaction. Users and context diversity. p. 203–212

  39. Sula A et al (2013) An IoT-Based framework for supporting children with autism spectrum disorder. Information Technology Convergence. Springer, New York, pp 193–202

    Chapter  Google Scholar 

  40. Sula A et al (2014) A smart environment and heuristic diagnostic teaching principle-based system for supporting children with autism during learning. In: 2014 28th International Conference on Advanced Information Networking and Applications Workshops. IEEE, New York

  41. Karakosta E et al (2019) Using the humanoid robot Kaspar in a Greek school environment to support children with Autism Spectrum Condition. Paladyn J Behav Robot 10(1):298–317

    Article  Google Scholar 

  42. So WC et al (2019) Who is a better teacher for children with autism? Comparison of learning outcomes between robot-based and human-based interventions in gestural production and recognition. Res Dev Disabil 86:62–75

    Article  Google Scholar 

  43. Khullar V, Singh HP, Bala M (2019) IoT based assistive companion for hypersensitive individuals (ACHI) with autism spectrum disorder. Asian J Psychiatr 46:92–102

    Article  Google Scholar 

  44. Ismail LI et al (2012) Estimation of concentration by eye contact measurement in robot–based intervention program with autistic children. Procedia Eng 41:1548–1552

    Article  Google Scholar 

Download references

Acknowledgements

This paper derives from the Research Project with code 98-1-37-14862 and Approval ID IR.IUMS.REC.1398.308.

Author information

Authors and Affiliations

  1. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

    Mehdi Hosseinzadeh

  2. Health Management and Economics Research Centre, Iran University of Medical Sciences, Tehran, Iran

    Mehdi Hosseinzadeh, Alireza Souri & Aziz Rezapour

  3. Department of Community Medicine, Preventive Medicine & Public Health Research Center, Iran University of Medical Sciences, Tehran, Iran

    Jalil Koohpayehzadeh

  4. Diplomacy and Public Relations Department, University of Human Development, Sulaymaniyah, Iraq

    Ahmed Omar Bali

  5. Department of Computer Engineering, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran

    Farnoosh Afshin Rad

  6. Department of Sports Medicine, School of Medicine, Hazrat Rasool Hospital, Iran University of Medical Sciences, Tehran, Iran

    Ali Mazaherinezhad

  7. Department of Computer Science and Information Technology, Institute for Advanced Studies in Basic Sciences, Zanjan, Iran

    Mahdi Bohlouli

  8. Research and Innovation Department, Petanux GmbH, Bonn, Germany

    Mahdi Bohlouli

Authors
  1. Mehdi Hosseinzadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jalil Koohpayehzadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ahmed Omar Bali
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Farnoosh Afshin Rad
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Alireza Souri
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ali Mazaherinezhad
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Aziz Rezapour
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Mahdi Bohlouli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alireza Souri.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hosseinzadeh, M., Koohpayehzadeh, J., Bali, A.O. et al. A review on diagnostic autism spectrum disorder approaches based on the Internet of Things and Machine Learning. J Supercomput 77, 2590–2608 (2021). https://doi.org/10.1007/s11227-020-03357-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-020-03357-0

Keywords

Search

Navigation