Support for Employees with ASD in the Workplace Using a Bluetooth Skin Resistance Sensor–A Preliminary Study - Publication - MOST Wiedzy


Support for Employees with ASD in the Workplace Using a Bluetooth Skin Resistance Sensor–A Preliminary Study


The application of a Bluetooth skin resistance sensor in assisting people with Autism Spectrum Disorders (ASD), in their day-to-day work, is presented in this paper. The design and construction of the device are discussed. The authors have considered the best placement of the sensor, on the body, to gain the most accurate readings of user stress levels, under various conditions. Trial tests were performed on a group of sixteen people to verify the correct functioning of the device. Resistance levels were compared to those from the reference system. The placement of the sensor has also been determined, based on wearer convenience. With the Bluetooth Low Energy block, users can be notified immediately about their abnormal stress levels via a smartphone application. This can help people with ASD, and those who work with them, to facilitate stress control and make necessary adjustments to their work environment.


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artykuł w czasopiśmie wyróżnionym w JCR
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SENSORS no. 18(10), pages 1 - 14,
ISSN: 1424-8220
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Bibliographic description:
Tomczak M. T., Wójcikowski M., Listewnik P., Pankiewicz B., Majchrowicz D., Szczerska M.: Support for Employees with ASD in the Workplace Using a Bluetooth Skin Resistance Sensor–A Preliminary Study// SENSORS. -Vol. 18(10), iss. 3530 (2018), s.1-14
Digital Object Identifier (open in new tab) 10.3390/s18103530
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  1. Shore, L.M.; Chung-Herrera, B.G.; Dean, M.A.; Holcombe Ehrhart, K.; Jung, D.I.; Randel, A.E.; Singh, G. Diversity in organizations: Where are we now and where are we going? Hum. Resour. Manag. Rev. 2009, 19, 117-133. [CrossRef] open in new tab
  2. Roberge, M.-É.; Van Dick, R. Recognizing the benefits of diversity: When and how does diversity increase group performance? Hum. Resour. Manag. Rev. 2010, 20, 295-308. [CrossRef] open in new tab
  3. McLaughlin, M.E.; Bell, M.P.; Stringer, D.Y. Stigma and acceptance of persons with disabilities. Group Organ. Manag. 2004, 29, 302-333. [CrossRef] open in new tab
  4. Jones, G.E. Advancement opportunity issues for persons with disabilities. Hum. Resour. Manag. Rev. 1997, 7, 56-76. [CrossRef] open in new tab
  5. Ismaili, J.; Ouazzani Ibrahimi, E.H. Mobile learning as alternative to assistive technology devices for special needs students. Educ. Inf. Technol. 2016, 22, 883-899. [CrossRef] open in new tab
  6. Wang, S. How Autism Can Help You Land a Job. The Wall Street Journal. 27 March 2016. Available online: land-a-job-wall-street-journal.56748/ (accessed on 2 January 2018). open in new tab
  7. Holland, R. Neurodiversity: The Benefits of Recruiting Employees with Cognitive Disabilities. Harvard Business School Working Knowledge. 11 July 2016. Available online: https://hbswk.hbs. edu/item/neurodiversity-the-benefits-of-recruiting-employees-with-cognitive-disabilities (accessed on 2 January 2018).
  8. Jones, K. Autistic Employees Can Give Companies an Edge in Innovative Thinking. The Guardian. 17 October 2016. Available online: autistic-employees-can-give-companies-an-edge-in-innovative-thinking (accessed on 2 January 2018).
  9. Pisano, G.P.; Austin, R.D. SAP SE: Autism at Work; Harvard Business School Case Study 616-042;
  10. Hendricks, D.R. Employment and adults with autism spectrum disorders: Challenges and strategies for success. J. Vocat. Rehabil. 2010, 32, 125-134. open in new tab
  11. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, 4th ed.; American Psychiatric Association: Washington, DC, USA, 2000. open in new tab
  12. Fulceri, F.; Tonacci, A.; Lucaferro, A.; Apicella, F.; Narzisi, A.; Vincenti, G.; Muratori, G.; Contaldo, A. Interpersonal motor coordination during joint actions in children with and without autism spectrum disorder: The role of motor information. Res. Dev. Disabil. 2018, 80, 13-23. [CrossRef] [PubMed] open in new tab
  13. Toth, K.; Munson, J.; Meltzoff, A.N.; Dawson, G. Early predictors of communication development in young children with autism spectrum disorder: Joint attention, imitation, and toy play. J. Autism Dev. Disord. 2006, 36, 993-1005. [CrossRef] [PubMed] open in new tab
  14. Colombi, C.; Liebal, K.; Tomasello, M.; Young, G.; Warneken, F.; Rogers, S.J. Examining correlates of cooperation in autism: Imitation, joint attention, and understanding intentions. Autism Int. J. Res. Pract. 2009, 13, 143-163. [CrossRef] [PubMed] open in new tab
  15. Billeci, L.; Narzisi, A.; Tonacci, A.; Sbriscia-Fioretti, B.; Serasini, L.; Fulceri, F.; Apicella, F.; Sicca, F.; Calderoni, S.; Muratori, F. An integrated EEG and eye-tracking approach for the study of responding and initiating joint attention in Autism Spectrum Disorders. Sci. Rep. 2017, 7, 13560. [CrossRef] [PubMed] open in new tab
  16. Mundy, P.; Gomes, A. Individual differences in joint attention skill development in the second year. Infant Behav. Dev. 1998, 21, 469-482. [CrossRef] open in new tab
  17. Ikeda, E.; Hinckson, H.; Crageloh, C. Assessment of quality of life in children and youth with autism spectrum disorder: A critical review. Qual. Life Res. 2014, 23, 1069-1085. [CrossRef] [PubMed] open in new tab
  18. Schroeder, J.; Cappadocia, M.; Bebko, J.; Pepler, D.; Weiss, J. Shedding light on a pervasive problem: A review of research on bullying experiences among children with autism spectrum disorders. J. Autism Dev. Disord. 2014, 44, 1520-1534. [CrossRef] [PubMed] open in new tab
  19. Howlin, P.H.; Moss, P. Adults with Autism Spectrum Disorders. Can. J. Psychiatry 2012, 57, 275-283. [CrossRef] [PubMed] open in new tab
  20. Ohl, A.; Sheff, M.G.; Little, S.; Nguyen, J.; Paskor, K.; Zanjirian, A. Predictors of employment status among adults with Autism Spectrum Disorder. Work 2017, 56, 345-355. [CrossRef] [PubMed] open in new tab
  21. Morris, M.R.; Begel, A.; Wiedermann, B. Understanding the Challenges Faced by Neurodiverse Software Engineering Employees: Towards a More Inclusive and Productive Technical Workforce. In Proceedings of the 17th International ACM SIGACCESS Conference on Computers & Accessibility (ASSETS '15), Lisbon, Portugal, 26-28 October 2015. [CrossRef] open in new tab
  22. Cabibihan, J.J.; Javed, H.; Aldosari, M.; Frazier, T.W.; Elbashir, H. Sensing Technologies for Autism Spectrum Disorder Screening and Intervention. Sensors 2017, 17, 46. [CrossRef] [PubMed] open in new tab
  23. DiPalma, S.; Tonacci, A.; Narzisi, A.; Domenici, C.; Pioggia, G.; Muratori, F.; Billeci, L.; The MICHELANGELO Study Group. Monitoring of autonomic response to sociocognitive tasks during treatment in children with Autism Spectrum Disorders by wearable technologies: A feasibility study. Comput. Biol. Med. 2017, 85, 143-152. [CrossRef] [PubMed] open in new tab
  24. Billeci, L.; Tonacci, A.; Narzisi, A.; Manigrasso, Z.; Varanini, M.; Fulceri, F.; Lattarulo, C.; Calderoni, S.; Muratori, F. Heart Rate Variability during a Joint Attention Task in Toddlers with Autism Spectrum Disorders. Front. Physiol. 2018, 9, 467. [CrossRef] [PubMed] open in new tab
  25. Burke, R.V.; Andersen, M.N.; Bowen, S.L.; Howard, M.R.; Allen, K.D. Evaluation of two instruction methods to increase employment options for adults with autism spectrum disorders. Res. Dev. Disabil. 2010, 31, 1223-1233. [CrossRef] [PubMed] open in new tab
  26. Fletcher, R.R.; Dobson, K.; Goodwin, M.S.; Eydgahi, H.; Wilder-Smith, O.; Fernholz, D.; Kuboyama, Y.; Hedman, E.B.; Poh, M.Z.; Picard, R.W. iCalm: Wearable sensor and network architecture for wirelessly communicating and logging autonomic activity. IEEE Trans. Inf. Technol. Biomed. 2010, 14, 215-223. [CrossRef] [PubMed] open in new tab
  27. McCarthy, C.; Pradhan, N.; Redpath, C.; Adler, A. Validation of the Empatica E4Wristband. In Proceedings of the 2016 IEEE EMBS International Student Conference (ISC), Ottawa, ON, Canada, 29-31 May 2016; pp. 1-4. open in new tab
  28. Jędrzejewska-Szczerska, M.; Karpienko, K.; Landowska, A. System supporting behavioral therapy for children with autism. J. Innov. Opt. Health Sci. 2015, 8. [CrossRef] open in new tab
  29. Landowska, A.; Karpienko, K.; Wróbel, M.; Jędrzejewska-Szczerska, M. Selection of physiological parameters for optoelectronic system supporting behavioral therapy of autistic children. Proc. SPIE 2014, 9290, 92901Q. [CrossRef] open in new tab
  30. Kołakowska, A.; Landowska, A.; Anzulewicz, A.; Sobota, K. Automatic recognition of therapy progress among children with autism. Sci. Rep. 2017, 7. [CrossRef] [PubMed] open in new tab
  31. Kołakowska, A.; Landowska, A.; Wróbel, M.R.; Zaremba, D.; Czajak, D.; Anzulewicz, A. Applications for investigating therapy progress of autistic children. Ann. Comput. Sci. Inf. Syst. 2016, 8, 1693-1697. [CrossRef] open in new tab
  32. Landowska, A.; Smiatacz, M. Mobile Activity Plan Applications for Behavioral Therapy of Autistic Children. Man-Mach. Interact. 2016, 4, 115-125. open in new tab
  33. Shapsough, S.; Hesham, A.; Elkhorazaty, Y.; Zualkernan, I.A.; Aloul, F. Emotion Recognition Using Mobile Phones. In Proceedings of the IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom), Munich, Germany, 14-16 September 2016; pp. 276-281. open in new tab
  34. Muaremi, A.; Arnrich, B.; Tröster, G. Towards Measuring Stress with Smartphones and Wearable Devices during Workday and Sleep. BioNanoScience 2013, 3, 172-183. [CrossRef] [PubMed] open in new tab
  35. Yin, X.; Shen, W.; Samarabandu, J.; Wang, X. Human Activity Detection Based on Multiple Smart Phone Sensors and Machine Learning Algorithms. In Proceedings of the IEEE 19th International Conference on Computer Supported Cooperative Work in Design, Calabria, Italy, 6-8 May 2015; pp. 582-587. open in new tab
  36. Bhagya Rekha, S.; Venkateswara Rao, M. Methodical Activity Recognition and Monitoring of a Person through Smart Phone and Wireless Sensors. In Proceedings of the IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI-2017), Chennai, India, 21-22 September 2017;
  37. Sano, A.; Phillips, A.J.; Yu, A.Z.; McHill, A.W.; Taylor, S.; Jaques, N.; Czeisler, C.A.; Klerman, E.B.; Picard, R.W. Recognizing Academic Performance, Sleep Quality, Stress Level, and Mental Health using PersonalityTraits, Wearable Sensors and Mobile Phones. In Proceedings of the IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN), Cambridge, MA, USA, 9-12 June 2015. [CrossRef] open in new tab
  38. Sneha, H.R.; Rafi, M.; Manoj Kumar, M.V.; Likewin, T.; Annappa, B. Smartphone Based Emotion Recognition and Classification. In Proceedings of the Second International Conference on Electrical, Computer and Communication Technologies (ICECCT), Coimbatore, India, 22-24 February 2017. [CrossRef] open in new tab
  39. Shi, D.; Chen, X.; Wei, J.; Yang, R. User Emotion Recognition Based on Multi-Class Sensors of Smartphone. In Proceedings of the IEEE International Conference on Smart City/SocialCom/SustainCom together with DataCom 2015 and SC2 2015, Chengdu, China, 19-21 December 2015; pp. 478-485. [CrossRef] open in new tab
  40. Chang, K.; Fisher, D.; Canny, J.; Hartmann, B. How's my mood and stress? An efficient speech analysis library for unobtrusive monitoring on mobile phones. In Proceedings of the BodyNets '11 Proceedings of the 6th International Conference on Body Area Networks, Beijing, China, 7-8 November 2011. open in new tab
  41. LiKamWa, R.; Liu, Y.; Lane, N.; Zhong, L. Can your smartphone infer your mood. In Proceedings of the PhoneSense Workshop, Seattle, WA, USA, 1-4 November 2011; Available online: bdb5a9a5d6c9b37193e0c2e9cb198f3edbccf6c2 (accessed on 2 January 2018).
  42. Lu, H.; Frauendorfer, D.; Rabbi, M.; Mast, M.S.; Chittaranjan, G.T.; Campbell, A.T.; Perez, D.G.; Choudhury, T. StressSense: Detecting stress in unconstrained acoustic environments using smartphones. In Proceedings of the ACM Ubiquitous Computing (UbiComp), Pittsburgh, PA, USA, 5-8 September 2012. open in new tab
  43. Salai, M.; Vassányi, I.; Kósa, I. Stress Detection Using Low Cost Heart Rate Sensors. J. Healthc. Eng. 2016, 2016, 5136705. [CrossRef] [PubMed] open in new tab
  44. Andeoli, A.; Gravina, R.; Giannantonio, R.; Pierleoni, P.; Fortino, G. SPINE-HRV: A BSN-based Toolkit for Heart Rate Variability Analysis in the Time-Domain, Wearable and Autonomous Biomedical Devices and Systems for Smart Environments: New issues and Characterization. Lect. Notes Electr. Eng. 2010, 75, 369-389. [CrossRef] open in new tab
  45. Han, L.; Zhang, Q.; Chen, X.; Zhan, Q.; Yang, T.; Zhao, Z. Detecting work-related stress with a wearable device. Comput. Ind. 2017, 90, 42-49. [CrossRef] open in new tab
  46. Handri, S.; Nomura, S.; Kurosawa, Y.; Yajima, K.; Ogawa, N.; Fukumura, Y. User Evaluation of Student's Physiological Response Towards E-Learning Courses Material by Using GSR Sensor. In Proceedings of the 9th IEEE/ACIS International Conference on Computer and Information Science, Yamagata, Japan, 18-20 August 2010. open in new tab
  47. Villarejo, M.V.; García Zapirain, B.; Méndez Zorrilla, M. A Stress Sensor Based on Galvanic Skin Response (GSR) Controlled by ZigBee. Sensors 2012, 12, 6075-6101. [CrossRef] [PubMed] open in new tab
  48. Okkesim, S.; Asyali, M.H.; Kara, S.; Kaya, M.G.; Ardic, I. Evaluation of anxiety related changes in skin conductance and blood volume pulse signals during coronary angiography. In Proceedings of the 14th National Biomedical Engineering Meeting, Balcova, Izmir, Turkey, 20-22 May 2009. [CrossRef] open in new tab
  49. Healey, J.A.; Picard, R.W. Detecting stress during real-world driving tasks using physiological sensors. IEEE Trans. Intell. Transp. Syst. 2005, 6, 156-166. [CrossRef] open in new tab
  50. Gjoreski, M.; Gjoreski, H.; Lustrek, M.; Gams, M. Continuous Live Stress Monitoring with a Wristband. In Proceedings of the ECAI 2016: 22nd European Conference on Artificial Intelligence, The Hague, The Netherlands, 29 August-2 September 2016; Volume 285, pp. 1803-1803. [CrossRef] open in new tab
  51. Sano, A.; Picard, R.W. Stress Recognition using Wearable Sensors and Mobile Phones. In Proceedings of the Humaine Association Conference on Affective Computing and Intelligent Interaction, Geneva, Switzerland, 2-5 September 2013; pp. 671-676. [CrossRef] open in new tab
  52. Bao, J.; Li, W.; Tao, X.; Cao, Y.; Shou, X.; Yang, H. Study on Fear Emotion Recognition Based on Traditional Chinese Medicine and Body Sensor Network. In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, Manchester, UK, 13-16 October 2013; pp. 368-373. [CrossRef] open in new tab
  53. Bluetooth SIG. Specification of the Bluetooth System. Covered Core Package Version 4.0; The Bluetooth Special Interest Group: Kirkland, WA, USA, 2010.
  54. Jędrzejewska-Szczerska, M.; Wierzba, P.; AbouChaaya, A.; Bechelany, M.; Miele, P.; Viter, R.; Mazikowski, A.; Karpienko, K.; Wróbel, M.S. ALD thin ZnO layer as an active medium in a fiber-optic Fabry-Perotinterferometr. Sens. Actuators A Phys. 2015, 221, 88-94. [CrossRef] open in new tab
  55. Majchrowicz, D.; Hirsch, M.; Wierzba, P.; Bechelany, M.; Viter, R.; Jędrzejewska-Szczerska, M. Application of Thin ZnO ALD Layersin Fiber-Optic Fabry-Pérot Sensing Interferometers. Sensors 2016, 16, 416. [CrossRef] [PubMed] open in new tab
  56. Chan, M.; Esteve, D.; Escriba, C.; Campo, E. A review of smart homes-Present state and future Challenges. Comput. Methods Programs Biomed. 2008, 91, 55-81. [CrossRef] [PubMed] open in new tab
  57. Hensel, W.F. People with autism spectrum disorder in the workplace: An expanding legal frontier. Civ. Lib. Law Rev. 2017, 52, 73-102.
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