Skip to main content

Advertisement

SpringerLink
Log in

Using a small dataset to classify strength-interactions with an elastic display: a case study for the screening of autism spectrum disorder

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

Health data collection of children with autism spectrum disorder (ASD) is challenging, time-consuming, and expensive; thus, working with small datasets is inevitable in this area. The diagnosis rate in ASD is low, leading to several challenges, including imbalance classes, potential overfitting, and sampling bias, making it difficult to show its potential in real-life situations. This paper presents a data analytics pilot-case study using a small dataset leveraging domain-specific knowledge to uncover differences between the gestural patterns of children with ASD and neurotypicals. We collected data from 59 children using an elastic display we developed during a sensing campaign and 9 children using the elastic display as part of a therapeutic program. We extracted strength-related features and selected the most relevant ones based on how the motor atypicality of children with ASD influences their interactions: children with ASD make smaller and narrower gestures and experience variations in the use of strength. The proposed machine learning models can correctly classify children with ASD with 97.3% precision and recall even if the classes are unbalanced. Increasing the size of the dataset via synthetic data improved the model precision to 99%. We finish discussing the importance of leveraging domain-specific knowledge in the learning process to successfully cope with some of the challenges faced when working with small datasets in a concrete, real-life scenario.

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

Notes

  1. system for human-computer interaction that the user operates through intuitive actions such a voice, gestures, and body movements.

  2. Given the context of the study our participants prefer to use Children with Autism spectrum Disorder (ASD), thereforewe will use this term thought the paper.

  3. http://www.tsps.cc/.

  4. We used the Kinect camera, although any alternative 3D depth cameras can also be used.

  5. Duration duration of the gesture; PI Initial point; PF final point; hgstx highest point to the camera on the x-axe; anchoGesto gesture width; altoGesto height of the gesture

References

  1. Ahmed KL, Simon AR, Dempsey JR, Samaco RC, Goin-Kochel RP (2020) Evaluating two common strategies for research participant recruitment Into autism studies: observational study. J Med Internet Res 22(9). https://doi.org/10.2196/16752. URL pmc/articles/PMC7545324//pmc/articles/PMC7545324/?report=abstracthttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7545324/

  2. Albinali F, Goodwin MS, Intille SS (2009) Recognizing stereotypical motor movements in the laboratory and classroom. In: Proceedings of the 11th international conference on Ubiquitous computing - Ubicomp ’09, p. 71. ACM Press, New York, New York, USA . https://doi.org/10.1145/1620545.1620555

  3. Althnian A, AlSaeed D, Al-Baity H, Samha A, Dris AB, Alzakari N, Abou Elwafa A, Kurdi H (2021) Impact of dataset size on classification performance: an empirical evaluation in the medical domain. Appl Sci 11(2):796 https://doi.org/10.3390/app11020796. https://www.mdpi.com/2076-3417/11/2/796

  4. American Psychiatric Association (2013) Diagnostic and statistical manual of mental disorders, 5th edn. Washington, DC

  5. Anzulewicz A, Sobota K, Delafield-butt JT (2016) Toward the autism motor signature : gesture patterns during smart tablet gameplay identify children with autism. Nat Publishing Group (August). https://doi.org/10.1038/srep31107

  6. Berls AT, McEwen IR (1999) Battelle developmental inventory. Phys Ther 79(8):776–783 https://doi.org/10.1093/ptj/79.8.776.https://academic.oup.com/ptj/article/79/8/776/2857780/Battelle-Developmental-Inventory

  7. Bidwell J, Essa IA, Rozga A, Abowd GD (2014) Measuring child visual attention using markerless head tracking from color and depth sensing cameras. In: Proceedings of the 16th International Conference on Multimodal Interaction - ICMI ’14, pp. 447–454. ACM Press, New York, New York, USA . https://doi.org/10.1145/2663204.2663235

  8. Boem A, Troiano GM (2019). Non-rigid HCI: a review of deformable interfaces and input. https://doi.org/10.1145/3322276.3322347

  9. Boursalie O, Samavi R, Doyle TE (2015) M4CVD: Mobile machine learning model for monitoring cardiovascular disease. In: Procedia Computer Science, vol. 63, pp. 384–391. Elsevier B.V. https://doi.org/10.1016/j.procs.2015.08.357

  10. Brown C, Chauhan J, Grammenos A, Han J, Hasthanasombat A, Spathis D, Xia T, Cicuta P, Mascolo C (2020) Exploring automatic diagnosis of COVID-19 from crowd-sourced respiratory sound data. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 11 . https://doi.org/10.1145/3394486

  11. Buongiorno D, Bortone I, Cascarano GD, Trotta GF, Brunetti A, Bevilacqua V (2019) A low-cost vision system based on the analysis of motor features for recognition and severity rating of Parkinson’s disease. BMC Med Informa Decis Mak 19(9):1–13. https://doi.org/10.1186/S12911-019-0987-5/TABLES/5

    Article  Google Scholar 

  12. Butt AH, Rovini E, Dolciotti C, De Petris G, Bongioanni P, Carboncini MC, Cavallo F (2018) Objective and automatic classifcation of Parkinson disease with Leap Motion controller. BioMed Eng Online 17(1):1–21. https://doi.org/10.1186/S12938-018-0600-7/TABLES/8

    Article  Google Scholar 

  13. Carette R, Cilia F, Dequen G, Bosche J, Guerin JL, Vandromme L (2018) Automatic autism spectrum disorder detection thanks to eye-tracking and neural network-based approach. pp. 75–81. Springer, Cham. https://doi.org/10.1007/978-3-319-76213-5_11

  14. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:321–357. https://doi.org/10.1613/jair.953. https://www.jair.org/index.php/jair/article/view/10302

  15. Chen J, Wang G, Zhang K, Wang G, Liu L (2019) A pilot study on evaluating children with autism spectrum disorder using computer games. Comput Human Behav 90:204–214. https://doi.org/10.1016/J.CHB.2018.08.057. https://www.sciencedirect.com/science/article/pii/S0747563218304321

  16. Chen RJ, Lu MY, Chen TY, Williamson DF, Mahmood F (2021) Synthetic data in machine learning for medicine and healthcare. Nat Biomed Eng 2021 5:6 5(6), 493–497 . https://doi.org/10.1038/s41551-021-00751-8. https://www.nature.com/articles/s41551-021-00751-8

  17. Christensen JH, Pontoppidan NH, Anisetti M, Bellandi V, Cremonini M (2019) Improving hearing healthcare with big data analytics of real-time hearing aid data. Proceedings - 2019 IEEE World Congress on Services, SERVICES 2019 pp. 307–313 . https://doi.org/10.1109/SERVICES.2019.00086

  18. Cibrian F, Beltran J, Tentori M (2018) Assessing the force and timing control of children with motor problems using elastic displays. In: Proceedings of the Proceedings of the 12th EAI International Conference on Pervasive Computing Technologies for Healthcare - Demos, Posters, Doctoral Colloquium, pp. 1–4. EAI. https://doi.org/10.4108/eai.20-4-2018.2276348

  19. Cibrian FL, Ley-Flores J, Newbold JW, Singh A, Bianchi-Berthouze N, Tentori M (2021) Interactive sonification to assist children with autism during motor therapeutic interventions. Personal Ubiquitous Comput 25(2):391–410. https://doi.org/10.1007/S00779-020-01479-Z/FIGURES/5

    Article  Google Scholar 

  20. Cibrian FL, Madrigal M, Avelais M, Tentori M (2020) Supporting coordination of children with ASD using neurological music therapy: a pilot randomized control trial comparing an elastic touch-display with tambourines. Res Develop Disabil 106:103741. https://doi.org/10.1016/j.ridd.2020.103741. https://linkinghub.elsevier.com/retrieve/pii/S0891422220301712

  21. Cibrian FL, Peña O, Ortega D, Tentori M (2017) BendableSound: an elastic multisensory surface using touch-based interactions to assist children with severe autism during music therapy. Int J Human-Comput Stud 107:22–37. https://doi.org/10.1016/j.ijhcs.2017.05.003. https://linkinghub.elsevier.com/retrieve/pii/S1071581917300757

  22. Cibrian FL, Pena O, Vazquez V, Cardenas C, Tentori M (2016) Designing a deformable musical surface for children with autism. In: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing Adjunct - UbiComp ’16, pp. 977–982. ACM Press, New York, New York, USA . https://doi.org/10.1145/2968219.2968262

  23. Crippa A, Salvatore C, Perego P, Forti S, Nobile M, Molteni M, Castiglioni I (2015) Use of machine learning to identify children with autism and their motor abnormalities. J Autism Develop Disord https://doi.org/10.1007/s10803-015-2379-8

  24. Demšar J, Erjavec A, Hočevar T, Milutinovič M, Možina M, Toplak M, Umek L, Zbontar J, Zupan B (2013) Orange: data mining toolbox in python Tomaž Curk Matija Polajnar Laň Zagar. Tech. rep

  25. Dongxin Xu, Gilkerson J, Richards J, Yapanel U, Gray S (2009) Child vocalization composition as discriminant information for automatic autism detection. In: 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2518–2522. IEEE . https://doi.org/10.1109/IEMBS.2009.5334846. http://ieeexplore.ieee.org/document/5334846/

  26. Donnellan AM, Hill DA, Leary MR (2013) Rethinking autism: implications of sensory and movement differences for understanding and support. Front Integr Neurosci 6:124. https://doi.org/10.3389/fnint.2012.00124

    Article  Google Scholar 

  27. Fombonne E, Marcin C, Manero AC, Bruno R, Diaz C, Villalobos M, Ramsay K, Nealy B (2016) Prevalence of autism spectrum disorders in Guanajuato, Mexico: the Leon survey. J Autism Develop Disord 46(5):1669–1685. https://doi.org/10.1007/s10803-016-2696-6

    Article  Google Scholar 

  28. Fournier KA, Hass CJ, Naik SK, Lodha N, Cauraugh JH (2010) Motor coordination in autism spectrum disorders: a synthesis and meta-analysis. J Autism and Developmental Disorders 40(10):1227–1240. https://doi.org/10.1007/s10803-010-0981-3

    Article  Google Scholar 

  29. González-Cortés T, Gutiérrez-Contreras E, Espino-Silva PK, Cruz JHS, Álvarez-Cruz D, Rosales-González CC, Sida-Godoy C, Nava-Hernández MP, López-Márquez FC, Ruiz-Flores P (2019) Clinical Profile of Autism Spectrum Disorder in a Pediatric Population from Northern Mexico. Journal of Autism and Developmental Disorders 2019 49:11 49(11), 4409–4420 . https://doi.org/10.1007/S10803-019-04154-2

  30. Gowen E, Hamilton A (2012) Motor Abilities in Autism: A Review Using a Computational. Context https://doi.org/10.1007/s10803-012-1574-0. http://www.antoniahamilton.com/GowenHamilton_JADD_2012.pdf

  31. Guinchat V, Chamak B, Bonniau B, Bodeau N, Perisse D, Cohen D, Danion A (2012) Very early signs of autism reported by parents include many concerns not specific to autism criteria. Research in Autism Spectrum Disorders 6(2):589–601. https://doi.org/10.1016/J.RASD.2011.10.005. https://www.sciencedirect.com/science/article/pii/S175094671100170X

  32. Haas K, Debra Costley, Falkmer M, Richdale A, Sofronoff K, Falkmer T (2016) Factors Influencing the Research Participation of Adults with Autism Spectrum Disorders. Journal of Autism and Developmental Disorders 46 (2016). https://doi.org/10.1007/s10803-016-2708-6

  33. Heller MD, Roots K, Srivastava S, Schumann J, Srivastava J, Hale TS (2013) A Machine Learning-Based Analysis of Game Data for Attention Deficit Hyperactivity Disorder Assessment. Games for Health Journal 2(5):291–298. https://doi.org/10.1089/g4h.2013.0058

    Article  Google Scholar 

  34. Ilias S, Tahir NM, Jailani R, Hasan CZC (2016) Classification of autism children gait patterns using Neural Network and Support Vector Machine. In: ISCAIE 2016 - 2016 IEEE Symposium on Computer Applications and Industrial Electronics, pp. 52–56. Institute of Electrical and Electronics Engineers Inc. . https://doi.org/10.1109/ISCAIE.2016.7575036. http://ieeexplore.ieee.org/document/7575036/

  35. Jain AK, Duin RP, Mao J (2000) Statistical pattern recognition: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(1):4–37. https://doi.org/10.1109/34.824819

    Article  Google Scholar 

  36. Jeni LA, Cohn JF, De La Torre F (2013) Facing imbalanced data - Recommendations for the use of performance metrics. Proceedings - 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, ACII 2013 pp. 245–251 . https://doi.org/10.1109/ACII.2013.47

  37. Kern JK, Geier DA, Adams JB, Troutman MR, Davis G, King PG, Young JL, Geier MR (2011) Autism severity and muscle strength: A correlation analysis. Research in Autism Spectrum Disorders 5(3):1011–1015. https://doi.org/10.1016/j.rasd.2010.11.002. https://linkinghub.elsevier.com/retrieve/pii/S1750946710001698

  38. Khir NHBM, Ismail M, Jamil N, Razak FHA (2015) Can spatiotemporal gait analysis identify a child with Autistic Spectrum Disorder? In: 2014 IEEE International Symposium on Robotics and Manufacturing Automation, IEEE-ROMA2014, pp. 115–119. Institute of Electrical and Electronics Engineers Inc. . https://doi.org/10.1109/ROMA.2014.7295872. https://ieeexplore.ieee.org/document/7295872/

  39. Kursa MB, Rudnicki WR (2010) Feature Selection with the Boruta Package. Journal Of Statistical Software 36(11), 1–13 . Vol. 36, Issue 11, Sep 2010. https://www.esencialblog.es/es/que-es-el-curriculum-oculto/

  40. LaGasse AB, Hardy MW (2013) Considering rhythm for sensorimotor regulation in children with autism spectrum disorders. Music Therapy Perspectives 31(1):67–77. https://doi.org/10.1093/mtp/31.1.67

    Article  Google Scholar 

  41. Landauer, TK (1988) Research Methods in Human-Computer Interaction. In: Handbook of Human-Computer Interaction, pp. 905–928. Elsevier . https://doi.org/10.1016/B978-0-444-70536-5.50047-6. https://linkinghub.elsevier.com/retrieve/pii/B9780444705365500476

  42. Li B, Sharma A, Meng J, Purushwalkam S, Gowen E (2017) Applying machine learning to identify autistic adults using imitation: An exploratory study. PLOS ONE 12(8):e0182652. https://doi.org/10.1371/journal.pone.0182652

    Article  Google Scholar 

  43. Lu H, Rabbi M, Chittaranjan GT, Frauendorfer D, Mast MS, Campbell AT, Gatica-Perez D, Choudhury T (2012) StressSense: Detecting stress in unconstrained acoustic environments using smartphones. In: UbiComp’12 - Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pp. 351–360. ACM Press, New York, New York, USA . https://doi.org/10.1145/2370216.2370270

  44. Maenner MJ, Shaw KA, Baio J, Washington A, Patrick M, DiRienzo M, Christensen DL, Wiggins LD, Pettygrove S, Andrews JG, Lopez M, Hudson A, Baroud T, Schwenk Y, White T, Rosenberg CR, Lee LC, Harrington RA, Huston M, Hewitt A, Esler A, Hall-Lande J, Poynter JN, Hallas-Muchow L, Constantino JN, Fitzgerald RT, Zahorodny W, Shenouda J, Daniels JL, Warren Z, Vehorn A, Salinas A, Durkin MS, Dietz PM (2020) Prevalence of autism spectrum disorder among children aged 8 Years-Autism and developmental disabilities monitoring network, 11 Sites, United States, 2016. MMWR Surveillance Summaries 69(4), 1–12 . https://doi.org/10.15585/MMWR.SS6904A1. https://www.cdc.gov/mmwr/volumes/69/ss/ss6904a1.htm

  45. Mannini A, Intille SS, Rosenberger M, Sabatini AM, Haskell W (2013) Activity Recognition Using a Single Accelerometer Placed at the Wrist or Ankle. Medicine & Science in Sports & Exercise 45(11):2193–2203. https://doi.org/10.1249/MSS.0b013e31829736d6. http://journals.lww.com/00005768-201311000-00022

  46. Margiotta N, Avitabile G, Coviello G (2017) A wearable wireless system for gait analysis for early diagnosis of Alzheimer and Parkinson disease. In: International Conference on Electronic Devices, Systems, and Applications, pp. 1–4. IEEE Computer Society . https://doi.org/10.1109/ICEDSA.2016.7818553. http://ieeexplore.ieee.org/document/7818553/

  47. Masino AJ, Forsyth D, Nuske H, Herrington J, Pennington J, Kushleyeva Y, Bonafide CP (2019) M-Health and autism: Recognizing stress and anxiety with machine learning and wearables data. In: Proceedings - IEEE Symposium on Computer-Based Medical Systems, vol. 2019-June, pp. 714–719. Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/CBMS.2019.00144

  48. Mayer R, Hittmeir M, Ekelhart A (2020) Privacy-Preserving Anomaly Detection Using Synthetic Data. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 12122(LNCS):195–207. https://doi.org/10.1007/978-3-030-49669-2_11

    Article  Google Scholar 

  49. Mosconi MW, Mohanty S, Greene RK, Cook EH, Vaillancourt DE, Sweeney JA (2015) Feedforward and Feedback Motor Control Abnormalities Implicate Cerebellar Dysfunctions in Autism Spectrum Disorder. Journal of Neuroscience 35(5):2015–2025. https://doi.org/10.1523/JNEUROSCI.2731-14.2015

    Article  Google Scholar 

  50. Müller M, Gründer T, Groh R (2015) Data exploration on elastic displays using physical metaphors. xCoAx 2015: Proceedings of the Third Conferenc on Computation, Communication, Aesthetics and X pp. 111–124

  51. health organization, W.: Autism spectrum disorders (2021). https://www.who.int/news-room/fact-sheets/detail/autism-spectrum-disorders

  52. Padala SA, Padala SA, Barsouk A, Thandra KC, Saginala K, Mohammed A, Vakiti A, Rawla P, Barsouk A (2020) Epidemiology of Renal Cell Carcinoma. World Journal of Oncology 11(3):79–87. https://doi.org/10.4021/wjon.v11i3.1279. https://www.wjon.org/index.php/wjon/article/view/1279www.wjon.org/index.php/wjon/article/view/1279

  53. Pater JA, Pfafman R, Coupe A (2021) Standardizing reporting of participant compensation in hci: A systematic literature review and recommendations for the field. Conference on Human Factors in Computing Systems - Proceedings. https://doi.org/10.1145/3411764.3445734

  54. Ping H, Stoyanovich J, Howe B (2017) ACM Reference format: Haoyue Ping, Julia Stoyanovich, and Bill Howe . https://doi.org/10.1145/3085504.3091117

  55. Rad NM, Furlanello C (2017) Applying Deep Learning to Stereotypical Motor Movement Detection in Autism Spectrum Disorders. IEEE International Conference on Data Mining Workshops, ICDMW pp. 1235–1242 . https://doi.org/10.1109/ICDMW.2016.0178

  56. Raghuwanshi BS, Shukla S (2021) Classifying imbalanced data using SMOTE based class-specific kernelized ELM. International Journal of Machine Learning and Cybernetics 12(5):1255–1280. https://doi.org/10.1007/S13042-020-01232-1/TABLES/11

    Article  Google Scholar 

  57. Raknim P, Lan KC (2015). Case Study Gait Monitoring for Early Neurological Disorder Detection Using Sensors in a Smartphone: Validation and a Case Study of Parkinsonism. https://doi.org/10.1089/tmj.2015.0005

  58. Rankin D, Black M, Bond R, Wallace J, Mulvenna M, Epelde G (2020) Reliability of Supervised Machine Learning Using Synthetic Data in Health Care: Model to Preserve Privacy for Data Sharing. JMIR Med Inform 2020;8(7):e18910 https://medinform.jmir.org/2020/7/e189108(7), e18910 . https://doi.org/10.2196/18910

  59. Sakai A, Minoda Y, Morikawa K (2017) Data augmentation methods for machine-learning-based classification of bio-signals. BMEiCON 2017 - 10th Biomedical Engineering International Conference 2017-Janua, 1–4 . https://doi.org/10.1109/BMEICON.2017.8229109

  60. Santos JF, Brosh N, Falk TH, Zwaigenbaum L, Bryson SE, Roberts W, Smith IM, Szatmari P, Brian JA (2013) Very early detection of Autism Spectrum Disorders based on acoustic analysis of pre-verbal vocalizations of 18-month old toddlers. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 7567–7571. IEEE . https://doi.org/10.1109/ICASSP.2013.6639134. http://ieeexplore.ieee.org/document/6639134/

  61. Shan Y, Li S, Chen T (2020) Respiratory signal and human stress: non-contact detection of stress with a low-cost depth sensing camera. International Journal of Machine Learning and Cybernetics 11(8):1825–1837. https://doi.org/10.1007/S13042-020-01074-X/TABLES/8

    Article  Google Scholar 

  62. Triantafyllidis AK, Tsanas A (2019). Applications of machine learning in real-life digital health interventions: Review of the literature. https://doi.org/10.2196/12286. https://www.jmir.org/2019/4/e12286

  63. Troiano GM, Pedersen EW, Hornbaek K (2014) User-Defined Gestures for Elastic. Deformable Displays. https://doi.org/10.1145/2598153.2598184

  64. Vabalas A, Gowen E, Poliakoff E, Casson AJ (2019) Kinematic features of a simple and short movement task to predict autism diagnosis. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society . https://doi.org/10.1109/EMBC.2019.8857307

  65. Vargas-Cuentas NI, Roman-Gonzalez A, Gilman RH, Barrientos F, Ting J, Hidalgo D, Jensen K, Zimic M (2017) Developing an eye-tracking algorithm as a potential tool for early diagnosis of autism spectrum disorder in children. PLOS ONE 12(11):e0188826. https://doi.org/10.1371/journal.pone.0188826

    Article  Google Scholar 

  66. Waheed A, Goyal M, Gupta D, Khanna A, Al-Turjman F, Pinheiro PR (2020) CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection. IEEE Access 8:91916–91923. https://doi.org/10.1109/ACCESS.2020.2994762

    Article  Google Scholar 

  67. Wedyan Mohammad AAJ (2016) Upper Limb Motor Coordination based Early Diagnosis in High Risk Subjects for Autism . https://doi.org/10.1109/SSCI.2016.7849893

  68. Westeyn T, Vadas K, Xuehai Bian, Starner T, Abowd G (2005) Recognizing Mimicked Autistic Self-Stimulatory Behaviors Using HMMs. In: Ninth IEEE International Symposium on Wearable Computers (ISWC’05), pp. 164–169. IEEE . https://doi.org/10.1109/ISWC.2005.45. http://ieeexplore.ieee.org/document/1550802/

  69. Xu Y, Warschauer M (2019) Young Children’s Reading and Learning with Conversational Agents. In: Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems, pp. 1–8. ACM, New York, NY, USA . https://doi.org/10.1145/3290607.3299035

  70. Yaneva V (2018) Detecting Autism Based on Eye-Tracking Data from Web Searching Tasks (di)

  71. Yaneva V, Ha LA, Eraslan S, Yesilada Y, Mitkov R (2020) Detecting High-Functioning Autism in Adults Using Eye Tracking and Machine Learning. IEEE Transactions on Neural Systems and Rehabilitation Engineering 28(6):1254–1261. https://doi.org/10.1109/TNSRE.2020.2991675. https://ieeexplore.ieee.org/document/9082703/

  72. Zunino A, Morerio P, Cavallo A, Ansuini C, Podda J, Battaglia F, Veneselli E, Becchio C, Murine V (2018) Video Gesture Analysis for Autism Spectrum Disorder Detection. pp. 3421–3426 . https://doi.org/10.1109/ICPR.2018.8545095. https://ieeexplore.ieee.org/document/8545095/

  73. Zutlevics TL (2016) Could providing financial incentives to research participants be ultimately self-defeating? Research Ethics 12(3):137–148. https://doi.org/10.1177/1747016115626756

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. CICESE, Ensenada, Baja California, Mexico

    Ivonne Monarca, Edgar Chavez & Monica Tentori

  2. Chapman University, Orange, CA, USA

    Franceli L. Cibrian

Authors
  1. Ivonne Monarca
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Franceli L. Cibrian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Edgar Chavez
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Monica Tentori
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Monica Tentori.

Additional information

Publisher's Note

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

List of features

List of features

Footnote 5

Population

PFSDX

movementMax

ModaX

Gender

PFMaxY

movementMin

ModaY

TotalGestures

PFMinY

movementMean

peakMax

TotalPush

PFMeanY

movementSD

peakMin

TotalDrag

PFSDY

hgstxMin

peakMean

DurationAverage

PFMaxZ

hgstxMax

peakSD

DurationMax

PFMinZ

hgstxMean

movementMax

DurationMin

PFMeanZ

hgstyMin

movementMin

DurationSD

PFSDZ

hgstyMax

movementMean

MeanVelocityX

DistanciaMax

hgstyMean

movementSD

MinVelocityX

DistanciaMin

AreaGesMax

hgstxMin

MaxVelocityX

DistanciaMean

AreaGesMin

hgstxMax

SDVelocityX

DistanciaSD

AreaGesMax

hgstxMean

MeanvelocityY

VelocidadMax

timmingMax

hgstyMin

MinVelocityY

VelocidadMin

timmingMin

hgstyMax

MaxVelocityY

VelocidadMean

timmingMean

hgstyMean

SDVelocityY

AcMax

timmingSD

AreaGesMax

MeanDepth

AcMin

anchoGestoMax

AreaGesMin

MaxDepth

ACMean

anchoGestoMin

AreaGesMax

MinDeht

ACSD

anchoGestoMean

timmingMax

SDDepth

fuerzaPro

altoGestoMax

timmingMin

PIMaxX

strength Mean

altoGestoMin

timmingMean

PiMinX

strengthMax

altoGestoMean

timmingSD

PIMeanX

FoceMin

fuerzak

anchoGestoMax

PISDX

strengthSD

strengthKMin

anchoGestoMin

PIMaxY

peakMax

strengthKMax

anchoGestoMean

PIMinY

peakMin

strengthKMean

altoGestoMax

PiMeanY

peakMean

strengthKSD

altoGestoMin

PISDY

peakSD

DurstrengthMax

altoGestoMean

PIMaxZ

mdt

DurstrengthMin

meanX

PIMinZ

maxDepthTimeax

DurstrengthMean

maxX

PIMeanZ

minDepthTime

DurstrengthSD

minX

PISDZ

MeanDepthTime

DIMax

meanY

PFMaxX

SDDepthTime

DIMin

maxY

PFMinX

AreaMax

DIMean

minY

PFMeanX

AreaMin

DISD

 

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Monarca, I., Cibrian, F.L., Chavez, E. et al. Using a small dataset to classify strength-interactions with an elastic display: a case study for the screening of autism spectrum disorder. Int. J. Mach. Learn. & Cyber. 14, 151–169 (2023). https://doi.org/10.1007/s13042-022-01554-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-022-01554-2

Keywords

Search

Navigation