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A Comprehensive Framework of Usability Issues Related to the Wearable Devices

  • Jayden KhakurelEmail author
  • Jari Porras
  • Helinä Melkas
  • Bo Fu
Chapter
  • 19 Downloads
Part of the EAI/Springer Innovations in Communication and Computing book series (EAISICC)

Abstract

Wearable devices have the potential to be used for monitoring, augmenting, assisting, delivering content, and tracking in both individual and organizational contexts. Despite this potential, previous studies indicate that the abandonment rate is quite high relative to the usage rate due to usability factors. This chapter provides a comprehensive systematic literature review on the usability issues related to wearable devices, as well as recommendations for overcoming the identified problems. It also investigates and presents a survey of the existing usability evaluation methods used to identify and evaluate the usability of wearable devices, including their strengths and limitations. As such, we present a categorization framework that gives an overview of the overall usability issues that act as the barriers to user adoption and a summary of which types of usability issues are associated with which type of device category. The chapter has the potential to inform and assist researchers, practitioners, and application developers as they work toward developing, implementing, and evaluating wearable devices and their associated interfaces, and this, in turn, may assist with sustained engagement among users.

Keywords

Usability Usability of wearable devices Wearable devices Smartwatch Pedometer VR AR Usability Evaluation Methods Systematic Literature Review 

References

  1. 1.
    Khakurel, J., Melkas, H., & Porras, J. (2018). Tapping into the wearable device revolution in the work environment: A systematic review. Information Technology and People, 31, 791–818.  https://doi.org/10.1108/ITP-03-2017-0076.CrossRefGoogle Scholar
  2. 2.
    Motti, V. G., & Caine, K. (2014). Human factors considerations in the design of wearable devices. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 58, 1820–1824.  https://doi.org/10.1177/1541931214581381.CrossRefGoogle Scholar
  3. 3.
    Rapp, A., & Cena, F. (2016). Personal informatics for everyday life: How users without prior self-tracking experience engage with personal data. International Journal of Human Computer Studies, 94, 1–17.  https://doi.org/10.1016/j.ijhcs.2016.05.006.CrossRefGoogle Scholar
  4. 4.
    Lee, J., Kim, D., Ryoo, H.-Y., & Shin, B.-S. (2016). Sustainable wearables: Wearable technology for enhancing the quality of human life. Sustainability, 8, 466.  https://doi.org/10.3390/su8050466.CrossRefGoogle Scholar
  5. 5.
    Clawson, J., Pater, J. A., Miller, A. D., et al. (2015). No longer wearing: Investigating the abandonment of personal health-tracking technologies on craigslist. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing – UbiComp ’15 (pp. 647–658). New York, NY: ACM Press.Google Scholar
  6. 6.
    Lazar, A., Koehler, C., Tanenbaum, J., & Nguyen, D. H. (2015). Why we use and abandon smart devices. In Proc eedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing – UbiComp ’15 (pp. 635–646).  https://doi.org/10.1145/2750858.2804288.CrossRefGoogle Scholar
  7. 7.
    Endeavor Partners. (2014). Inside wearables – Part 2.Google Scholar
  8. 8.
    Blevis, E. (2007). Sustainable interaction design: Invention & disposal, renewal & reuse. In Conference on Human Factors in Computing Systems – CHI ’07 (p. 503).  https://doi.org/10.1145/1240624.1240705.CrossRefGoogle Scholar
  9. 9.
    Lin, Y., Breugelmans, J., Iversen, M., & Schmidt, D. (2017). An Adaptive Interface Design (AID) for enhanced computer accessibility and rehabilitation. International Journal of Human Computer Studies, 98, 14–23.  https://doi.org/10.1016/j.ijhcs.2016.09.012.CrossRefGoogle Scholar
  10. 10.
    Piwek, L., Ellis, D. A., Andrews, S., & Joinson, A. (2016). The rise of consumer health wearables: Promises and barriers. PLoS Medicine, 13.  https://doi.org/10.1371/journal.pmed.1001953.Google Scholar
  11. 11.
    Abbas, S. Q. (2010). Development of a quality design framework for usable User Interfaces. International Journal of Computational Science and Engineering, 02, 1763–1767.Google Scholar
  12. 12.
    Trivedi, M. C. (2012). Role of context in usability evaluations: A review. Advanced Computing: An International Journal, 3, 69–78.  https://doi.org/10.5121/acij.2012.3208.CrossRefGoogle Scholar
  13. 13.
    ISO. (2009). ISO 9241-210:2009. Ergonomics of human system interaction – Part 210: Human-centred design for interactive systems.Google Scholar
  14. 14.
    Gafni, R. (2009). Usability issues in mobile-wireless information systems. Issues in Informing Science and Information Technology, 6, 754–769.Google Scholar
  15. 15.
    Gandy, M., Ross, D., & Starner, T. E. (2003). Universal design: Lessons for wearable computing. IEEE Pervasive Computing, 2, 19–23.  https://doi.org/10.1109/MPRV.2003.1228523.CrossRefGoogle Scholar
  16. 16.
    Liu, X., Vega, K., Maes, P., & Paradiso, J. A. (2016). Wearability factors for skin interfaces. In Proceedings of the 7th Augmented Human International Conference 2016 on – AH ’16 (pp. 1–8). New York, NY: ACM Press.Google Scholar
  17. 17.
    Stone D, Jarrett C, Woodroffe M, Minocha S (2005) User interface design and evaluation, 1st. Interactive technologies. Morgan Kaufmann. 704 pages. ISBN-10: 0120884364, ISBN-13:978-0120884360.Google Scholar
  18. 18.
    Petersen, K., Feldt, R., Mujtaba, S., & Mattsson, M. (2008). Systematic mapping studies in software engineering. In EASE’08: Proceedings of the 12th International Conference on Evaluation and Assessment in Software Engineering (pp. 68–77).  https://doi.org/10.1142/S0218194007003112.CrossRefGoogle Scholar
  19. 19.
    Steiger, E., de Albuquerque, J. P., & Zipf, A. (2015). An advanced systematic literature review on spatiotemporal analyses of twitter data. Transactions in GIS, 19, 809–834.Google Scholar
  20. 20.
    Alves, V., Niu, N., Alves, C., & Valença, G. (2010). Requirements engineering for software product lines: A systematic literature review. Information and Software Technology, 52, 806–820.Google Scholar
  21. 21.
    Akhavian, R., & Behzadan, A. (2016). Wearable sensor-based activity recognition for data-driven simulation of construction workers’ activities. In Proceedings - Winter Simulation Conference (pp. 3333–3344).Google Scholar
  22. 22.
    Rapp, A., & Cena, F. (2015). Affordances for self-tracking wearable devices (pp. 141–142). ISWC.  https://doi.org/10.1145/2802083.2802090.
  23. 23.
    Tomberg, V., Scholz, T., & Kelle, S. (2015). Universal access in human-computer interaction. Access to interaction. Cham: Springer.Google Scholar
  24. 24.
    Dhawale, P. P., & Wellington, R. J. (2015). Identifying the characteristics of usability that encourage prolonged use of an Activity Monitor. In New Zealand Conference on Human-Computer Interaction (pp. 39–42).  https://doi.org/10.1145/2808047.2808056.CrossRefGoogle Scholar
  25. 25.
    Jiang, H., Chen, X., Zhang, S., et al. (2015). Software for wearable devices: Challenges and opportunities. In Proceedings – International Computer Software and Applications Conference (pp. 592–597).Google Scholar
  26. 26.
    Kitchenham, B., & Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. Technical report, Keele University and University of Durham.Google Scholar
  27. 27.
    Engström, E., & Runeson, P. (2011). Software product line testing – A systematic mapping study. Information and Software Technology, 53, 2–13.  https://doi.org/10.1016/j.infsof.2010.05.011.CrossRefGoogle Scholar
  28. 28.
    Budgen, D., & Brereton, P. (2006). Performing systematic literature reviews in software engineering. In Proceeding of the 28th International Conference on Software Engineering – ICSE ’06 (p. 1051). New York, NY: ACM Press.Google Scholar
  29. 29.
    Tosi, D., & Morasca, S. (2015). Supporting the semi-automatic semantic annotation of web services: A systematic literature review. Information and Software Technology, 61, 16–32.Google Scholar
  30. 30.
    Petticrew, M., & Roberts, H. (2006). Systematic reviews in the social sciences: A practical guide. Wiley-Blackwell. ISBN: 978-1-405-12110-1Google Scholar
  31. 31.
    Kitchenham, B., Pretorius, R., Budgen, D., et al. (2010). Systematic literature reviews in software engineering – A tertiary study. Information and Software Technology, 52, 792–805.  https://doi.org/10.1016/j.infsof.2010.03.006.CrossRefGoogle Scholar
  32. 32.
    Welsh, E. (2002). Dealing with data: Using NVivo in the qualitative data analysis process. Forum Qualitative Social Research, 3, Art 26.  https://doi.org/10.17169/fqs-3.2.865.CrossRefGoogle Scholar
  33. 33.
    NVIVO. (2018). NVIVO. http://www.qsrinternational.com/nvivo/what-is-nvivo. Accessed 1 Feb 2018.
  34. 34.
    Elsevier. (2008). Mendeley. https://www.mendeley.com/
  35. 35.
    Ozkan, B. C. (2004). Using NVivo to analyze qualitative classroom data on constructivist learning. Environments, 9, 589–603.Google Scholar
  36. 36.
    Ivory, M. Y., & Hearst, M. A. (2001). The state of the art in automating usability evaluation of user interfaces. ACM Computing Surveys, 33, 470–516.  https://doi.org/10.1145/503112.503114.CrossRefGoogle Scholar
  37. 37.
    Kaewkannate, K., & Kim, S. (2016). A comparison of wearable fitness devices. BMC Public Health.  https://doi.org/10.1186/s12889-016-3059-0.
  38. 38.
    Impellizzeri, F. M., & Bizzini, M. (2012). Systematic review and meta-analysis: A primer. International Journal of Sports Physical Therapy, 7, 493–503.Google Scholar
  39. 39.
    Nowell, L. S., Norris, J. M., White, D. E., & Moules, N. J. (2017). Thematic analysis: Striving to meet the trustworthiness criteria. International Journal of Qualitative Methods, 16.  https://doi.org/10.1177/1609406917733847.Google Scholar
  40. 40.
    Janidarmian, M., Fekr, A. R., Radecka, K., & Zilic, Z. (2017). A comprehensive analysis on wearable acceleration sensors in human activity recognition. Sensors (Switzerland), 17.  https://doi.org/10.3390/s17030529.Google Scholar
  41. 41.
    McHugh, M. L. (2012). Interrater reliability: The kappa statistic. Biochemia Medica, 276–282.  https://doi.org/10.11613/BM.2012.031.
  42. 42.
    Hosseini, M., Shahri, A., Phalp, K., et al. (2015). Crowdsourcing: A taxonomy and systematic mapping study. Computer Science Review, 17, 43–69.  https://doi.org/10.1016/j.cosrev.2015.05.001.MathSciNetCrossRefGoogle Scholar
  43. 43.
    Ally, M., & Gardiner, M. (2012). Application and device characteristics as drivers for smart mobile device adoption and productivity. International Journal of Organizational Behavior, 17, 35–47.Google Scholar
  44. 44.
    Liu, C.-C., Wu, D.-W., Jou, M., & Tsai, S.-J. (2010). Development of a sensor network system for industrial technology education. In Communications in Computer and Information Science (pp. 369–374).Google Scholar
  45. 45.
    Dennis A, Wixom BH, Roth RM (2012) Systems analysis and design, 6th ed. Wiley. (October 29, 2014). November 3 2014, ASIN: B00P6SS8OG https://www.amazon.com/Systems-Analysis-Design-Alan-Dennis-ebook/dp/B00P6SS8OG.
  46. 46.
    Leinonen, T., Purrna, J., Ngua, K., & Hayes, A. (2013). Scenarios for peer-to-peer learning in construction with emerging forms of collaborative computing. In International Symposium on Technology and Society, Proceedings (pp. 59–71).Google Scholar
  47. 47.
    Rauschnabel, P. A., Hein, D. W. E., He, J., et al. (2016). Fashion or technology? A fashnology perspective on the perception and adoption of augmented reality smart glasses. i-com, 15.  https://doi.org/10.1515/icom-2016-0021.
  48. 48.
    Oh, S., So, H., & Gaydos, M. (2017). Hybrid augmented reality for participatory learning: The hidden efficacy of the multi-user game-based simulation. IEEE Transactions on Learning Technologies, 11, 1.  https://doi.org/10.1109/TLT.2017.2750673.CrossRefGoogle Scholar
  49. 49.
    Wichrowski, M., Koržinek, D., & Szklanny, K. (2015). Google glass development in practice. In Proceedings of the Mulitimedia, Interaction, Design and Innnovation on ZZZ – MIDI ’15 (pp. 1–12). New York, NY: ACM Press.Google Scholar
  50. 50.
    Kim, K. J. (2017). Shape and size matter for smartwatches: Effects of screen shape, screen size, and presentation mode in wearable communication. Journal of Computer Communication, 22, 124–140.  https://doi.org/10.1111/jcc4.12186.CrossRefGoogle Scholar
  51. 51.
    Pulli, P., Hyry, J., Pouke, M., & Yamamoto, G. (2012). User interaction in smart ambient environment targeted for senior citizen. Medical & Biological Engineering & Computing, 50, 1119–1126.  https://doi.org/10.1007/s11517-012-0906-8.CrossRefGoogle Scholar
  52. 52.
    Harrington, C., Wood, R., Breuer, J., et al. (2011). Using a unified usability framework to dramatically improve the usability of an EMR module. American Medical Informatics Association Annual Symposium Proceedings, 2011, 549–558.Google Scholar
  53. 53.
    Laarni, J., Heinilä, J., Häkkinen, J., et al. (2009). Supporting situation awareness in demanding operating environments through wearable user interfaces. In Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics) 5639 LNAI (pp. 13–21).  https://doi.org/10.1007/978-3-642-02728-4_2.CrossRefGoogle Scholar
  54. 54.
    Delabrida, S. E., DAngelo, T., Oliveira, R. A. R., & Loureiro, A. A. F. (2015). Building wearables for geology. In 2015 Brazilian Symposium on Computing Systems Engineering (SBESC) (pp. 148–153). IEEE.Google Scholar
  55. 55.
    Laramee, B., Laramee, R. S., & Ware, C. (2002). Rivalry and interference with a head mounted display. ACM Transactions on Computer-Human Interaction, 9, 238–251.  https://doi.org/10.1145/568513.568516.CrossRefGoogle Scholar
  56. 56.
    Laramee, R. S., & Ware, C. (2001). Visual interference with a transparent head mounted display. In CHI ’01 extended abstracts on Human factors in computer systems – CHI ’01 (p. 323). New York, NY: ACM Press.Google Scholar
  57. 57.
    McGill, M., Murray-Smith, R., Boland, D., & Brewster, S. A. (2015). A dose of reality. In Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems – CHI EA ’15 (pp. 177–177). New York, NY: ACM Press.Google Scholar
  58. 58.
    Shih, P. C., Han, K., Poole, E. S., et al. (2015). Use and adoption challenges of wearable activity trackers. iConference Proc 1–12.Google Scholar
  59. 59.
    Holzinger, A., Searle, G., Pruckner, S., et al. (2010). Perceived usefulness among elderly people: Experiences and lessons learned during the evaluation of a wrist device. In Procceedings of the 4th International ICST Conference on Pervasive Computer Technology and Healthcare (pp. 1–5).  https://doi.org/10.4108/ICST.PERVASIVEHEALTH2010.8912.CrossRefGoogle Scholar
  60. 60.
    Costanza, E., Inverso, S., & Pavlov, E. (2006). eye-q: Eyeglass peripheral display for subtle intimate notifications. In Proc 8th … (pp. 211–218).  https://doi.org/10.1145/1152215.1152261.CrossRefGoogle Scholar
  61. 61.
    Jacob, R. J. K. (2000). New human-computer interaction techniques. In J. Hyona, & R. Radach (Eds.), The mind’s eye: Cognitive and applied aspects of eye movement research. Elsevier Science BV. ISBN: 0–444–51020–6.Google Scholar
  62. 62.
    Neto, L. d. S. B., Maike, L. V. R. M., et al. (2015). A wearable face recognition system built into a smartwatch and the blind and low vision users. Lecture Notes in Business Information Processing, 241, 515–528.  https://doi.org/10.1007/978-3-319-29133-8.CrossRefGoogle Scholar
  63. 63.
    Lawo, M., Herzog, O., & Witt, H. (2007). An industrial case study on wearable computing applications. In Proceedings of the 9th International Conference on Human Computer Interaction with Mobile Devices and Services – MobileHCI ’07 (pp. 448–451).  https://doi.org/10.1145/1377999.1378052.CrossRefGoogle Scholar
  64. 64.
    Han, T., Han, Q., Annett, M., et al. Frictio: Passive kinesthetic force feedback for smart ring output. In Proceedings of the UIST 2017 (Vol. 2017).Google Scholar
  65. 65.
    Brun, D., Ferreira, S. M., Gouin-Vallerand, C., & George, S. (2016). CARTON project: Do-it-yourself approach to turn a smartphone into a smart eyewear. In Proceedings of the 14th International Conference on Advances in Mobile Computing & Multimedia (pp. 128–136).  https://doi.org/10.1145/3007120.3007134.CrossRefGoogle Scholar
  66. 66.
    “Claire”, L. S., & Starner, T. (2010). BuzzWear: Alert perception in wearable tactile displays on the wrist. In Proceedings of the 28th International Conference on Human Factors in Computing Systems – CHI ’10 (pp. 433–442).  https://doi.org/10.1145/1753326.1753392.CrossRefGoogle Scholar
  67. 67.
    Levin-Sagi, M., Pasher, E., Carlsson, V., et al. (2007). A comprehensive human factors analysis of wearable computers supporting a hospital ward round. IEEE Xplore, 1–12.Google Scholar
  68. 68.
    Manabe, H., & Fukumoto, M. (2011). Tap control for headphones without sensors. In Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology – UIST ’11 (p. 309).  https://doi.org/10.1145/2047196.2047236.CrossRefGoogle Scholar
  69. 69.
    Zhang, Y., & Rau, P. P. (2015). Playing with multiple wearable devices: Exploring the influence of display, motion and gender. Computers in Human Behavior, 50, 148–158.  https://doi.org/10.1016/j.chb.2015.04.004.CrossRefGoogle Scholar
  70. 70.
    Grant, A. (2017). Is my data valid? In: QS labs. http://quantifiedself.com/2017/08/validating-self-tracking-devices/. Accessed 30 Mar 2018.
  71. 71.
    Rodríguez, I., Cajamarca, G., Herskovic, V., et al. (2017). Helping elderly users report pain levels: A study of user experience with mobile and wearable interfaces. Mobile Information Systems, 2017, 1–12.  https://doi.org/10.1155/2017/9302328.CrossRefGoogle Scholar
  72. 72.
    Ross, D. A., & Blasch, B. B. (2002). Development of a wearable computer orientation system. Personal and Ubiquitous Computing, 6, 49–63.  https://doi.org/10.1007/s007790200005.CrossRefGoogle Scholar
  73. 73.
    Rasche, P., Wille, M., Theis, S., et al. (2015). Activity tracker and elderly. In Computing Information Technology Ubiquitous Computing Communications Dependable, Auton Secur Comput Pervasive Intell Comput (CIT/IUCC/DASC/PICOM), 2015 IEEE International Conference on IS – SN – VO – VL (pp. 1411–1416).  https://doi.org/10.1109/CIT/IUCC/DASC/PICOM.2015.211.CrossRefGoogle Scholar
  74. 74.
    Ye, H., Malu, M., Oh, U., & Findlater, L. (2014). Current and future mobile and wearable device use by people with visual impairments. Chi, 2014, 3123–3132.  https://doi.org/10.1145/2556288.2557085.CrossRefGoogle Scholar
  75. 75.
    Altenhoff, B., Vaigneur, H., & Caine, K. (2015). One step forward, two steps back: The key to wearables in the field is the app. In Proceedings of the 9th International Conference on Pervasive Computing Technologies for Healthcare (pp. 241–244).  https://doi.org/10.4108/icst.pervasivehealth.2015.259049.CrossRefGoogle Scholar
  76. 76.
    Ananthanarayan, S., Sheh, M., Chien, A., et al. (2014). Designing wearable interfaces for knee rehabilitation. In Proceedings of the 8th International Conference on Pervasive Computing Technologies for Healthcare. ICST.Google Scholar
  77. 77.
    Ferri, J., Lidón-Roger, J. V., Moreno, J., et al. (2017). A wearable textile 2D touchpad sensor based on screen-printing technology. Materials (Basel), 10.  https://doi.org/10.3390/ma10121450.Google Scholar
  78. 78.
    Savindu, H. P., Iroshan, K. A., Panangala, C. D., et al. (2017). BrailleBand: Blind support haptic wearable band for communication using braille language. In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (pp. 1381–1386). Canada: Cybern Banff Center, Banff.  https://doi.org/10.1109/SMC.2017.8122806.CrossRefGoogle Scholar
  79. 79.
    Chen, S., Lan, Y.-C., Zheng, Y.-R., et al. (2015). Usability of a low-cost wearable health device for physical activity and sleep duration in healthy adults. In Proceedings of the 2015 Work Pervasive Wireless Healthcare – MobileHealth ’15 (pp. 35–38).  https://doi.org/10.1145/2757290.2757298.CrossRefGoogle Scholar
  80. 80.
    Thorpe, J. R., Rønn-Andersen, K. V. H., Bień, P., et al. (2016). Pervasive assistive technology for people with dementia: A UCD case. Healthcare Technology Letters, 3, 297–302.  https://doi.org/10.1049/htl.2016.0057.CrossRefGoogle Scholar
  81. 81.
    Wulf, L., Garschall, M., Himmelsbach, J., & Tscheligi, M. (2014). Hands free – Care free. In Proceedings of the 8th Nordic Conference on Human-Computer Interaction Fun, Fast, Foundational – NordiCHI ’14 (pp. 203–206). New York, NY: ACM Press.Google Scholar
  82. 82.
    Carter, S., Marlow, J., Komori, A., & Mäkelä, V. (2016). Bringing mobile into meetings: Enhancing distributed meeting participation on smartwatches and mobile phones. In Proceedings of the 8th International Conference on Human-Computer Interaction with Mobile Devices and Services (pp. 407–417).  https://doi.org/10.1145/2935334.2935355.CrossRefGoogle Scholar
  83. 83.
    Ahanathapillai, V., Amor, J. D., & James, C. J. (2015). Assistive technology to monitor activity, health and wellbeing in old age: The wrist wearable unit in the USEFIL project. Technology and Disability, 27, 17–29.  https://doi.org/10.3233/TAD-150425.CrossRefGoogle Scholar
  84. 84.
    Sultan, N. (2015). Reflective thoughts on the potential and challenges of wearable technology for healthcare provision and medical education. International Journal of Information Management, 35, 521–526.  https://doi.org/10.1016/j.ijinfomgt.2015.04.010.CrossRefGoogle Scholar
  85. 85.
    Yang, P., Hanneghan, M., Qi, J., et al. (2015). Improving the validity of lifelogging physical activity measures in an internet of things environment. In 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (pp. 2309–2314). IEEE.Google Scholar
  86. 86.
    Koskimäki, H., Mönttinen, H., Siirtola, P., et al. (2017). Early detection of migraine attacks based on wearable sensors. In Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computer – UbiComp ’17 (pp. 506–511).  https://doi.org/10.1145/3123024.3124434.CrossRefGoogle Scholar
  87. 87.
    Albrecht, U. V., Von Jan, U., Kuebler, J., et al. (2014). Google glass for documentation of medical findings: Evaluation in forensic medicine. Journal of Medical Internet Research, 16.  https://doi.org/10.2196/jmir.3225.Google Scholar
  88. 88.
    Rahmati, A., Qian, A., & Zhong, L. (2007). Understanding human-battery interaction on mobile phones. In Proceedings of the 9th International Conference on Human Computer Interaction with Mobile Devices and Services – MobileHCI ‘07 (pp. 265–272). New York, NY: ACM Press.Google Scholar
  89. 89.
    Brewster, S., Lumsden, J., Bell, M., et al. (2003). Multimodal “eyes-free” interaction techniques for wearable devices. In Proceedings of the Conference on Human Factors in Computing Systems – CHI ’03 (p. 473).Google Scholar
  90. 90.
    Spagnolli, A., Guardigli, E., Orso, V., et al. (2014). Measuring user acceptance of wearable symbiotic devices: Validation study across application scenarios. In Lecture Notes in Computer Science (LNCS), including its subseries Lecture Notes in Artificial Intelligence (LNAI) and Lecture Notes in Bioinformatics (LNBI) (Vol. 8820, pp. 87–98).  https://doi.org/10.1007/978-3-319-13500-7_7.CrossRefGoogle Scholar
  91. 91.
    Oakley, I., Sunwoo, J., & Cho, I.-Y. (2008). Pointing with fingers, hands and arms for wearable computing. In Proc CHI Ext Abstr (pp. 3255–3260).  https://doi.org/10.1145/1358628.1358840.CrossRefGoogle Scholar
  92. 92.
    Ju, A. L., & Spasojevic, M. (2015). Smart jewelry. In Proceedings of the 2015 Workshop on Future Mobile User Interfaces – FutureMobileUI ’15 (pp. 13–15). New York, NY: ACM Press.Google Scholar
  93. 93.
    Goto, M., Kimata, H., Toyoshi, M., et al. (2015). A wearable action support system for business use by context-aware computing based on web schedule. In Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computer – UbiComp ’15 (pp. 53–56).  https://doi.org/10.1145/2800835.2800863.CrossRefGoogle Scholar
  94. 94.
    Kondo, Y., Takahashi, S., & Tanaka, J. (2015). Information select and transfer between touch panel and wearable devices using human body communication. In M. Kurosu (Ed.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 208–216). Cham: Springer.Google Scholar
  95. 95.
    Nirjon, S., Gummeson, J., Gelb, D., & Kim, K.-H. (2015). TypingRing: A wearable ring platform for text input. In Proceedings of the 13th Annual International Conference on Mobile Systems, Applications, and Services – MobiSys ’15 (pp. 227–239).  https://doi.org/10.1145/2742647.2742665.CrossRefGoogle Scholar
  96. 96.
    Abbate, S., Avvenuti, M., & Light, J. (2014). Usability Study of a wireless monitoring system among Alzheimer’s disease elderly population. International Journal of Telemedicine and Applications, 2014.  https://doi.org/10.1155/2014/617495.Google Scholar
  97. 97.
    Fang, Y.-M., & Chang, C.-C. (2016). Users’ psychological perception and perceived readability of wearable devices for elderly people. Behaviour and Information Technology, 35, 225–232.  https://doi.org/10.1080/0144929X.2015.1114145.CrossRefGoogle Scholar
  98. 98.
    Mizuno, T., & Kume, Y. (2014). Development of a glasses-like wearable device to measure nasal skin temperature. Communications in Computer and Information Science, 435, 338–342.  https://doi.org/10.1007/978-3-319-07854-0.CrossRefGoogle Scholar
  99. 99.
    Yoo, J., Nockhwan, K., Jeongho, K., & Hwan, R. J. (2014). Preliminary guidelines to build a wearable health monitoring system for patients: Focusing on a wearable device with a wig. Communications in Computer and Information Science, 435, 338–342.  https://doi.org/10.1007/978-3-319-07854-0.CrossRefGoogle Scholar
  100. 100.
    Yurtman, A., & Barshan, B. (2017). Activity recognition invariant to sensor orientation with wearable motion sensors. Sensors (Switzerland), 17.  https://doi.org/10.3390/s17081838.
  101. 101.
    Klingeberg, T., & Schilling, M. (2012). Mobile wearable device for long term monitoring of vital signs. Computer Methods and Programs in Biomedicine, 106, 89–96.  https://doi.org/10.1016/j.cmpb.2011.12.009.CrossRefGoogle Scholar
  102. 102.
    Liang, Z., Nagata, Y., Martell, M. A. C., & Nishimura, T. (2016). Nurturing wearable and mHealth technologies for self-care: Mindset, tool set and skill set. In 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom) 2016 (pp. 6–10).  https://doi.org/10.1109/HealthCom.2016.7749432.CrossRefGoogle Scholar
  103. 103.
    Masai, K., Kunze, K., Sugiura, Y., et al. (2017). Evaluation of facial expression recognition by a smart eyewear for facial direction changes, repeatability, and positional drift. ACM Transactions on Interactive Intelligent Systems, 7, 1–23.  https://doi.org/10.1145/3012941.CrossRefGoogle Scholar
  104. 104.
    Young, K. A. (2005). Direct from the source: The value of “think-aloud” data in understanding learning. Journal of Educational Enquiry, 6, 19–33.Google Scholar
  105. 105.
    Hafiz, P., Miskowiak, K. W., Kessing, L. V., et al. (2019). The internet-based cognitive assessment tool: System design and feasibility study. JMIR Formative Research, 3, e13898.  https://doi.org/10.2196/13898.CrossRefGoogle Scholar
  106. 106.
    Kashimoto, Y., Firouzian, A., Asghar, Z., et al. (2016). Twinkle Megane: Near-eye LED indicators on glasses in tele-guidance for elderly. 2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops). Google Scholar
  107. 107.
    Sin, A. K., Zaman, H. B., Ahmad, A., & Sulaiman, R. (2015). Evaluation of wearable device for the elderly (W-Emas). In H. Badioze Zaman, P. Robinson, A. F. Smeaton, et al. (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 119–131). Cham: Springer. https://link.springer.com/10.1007/978-3-319-25939-0_11.Google Scholar
  108. 108.
    Bower, M., & Sturman, D. (2015). What are the educational affordances of wearable technologies? Computers in Education, 88, 343–353.  https://doi.org/10.1016/j.compedu.2015.07.013.CrossRefGoogle Scholar
  109. 109.
    Angelini, L., Caon, M., Carrino, S., et al. (2013). Designing a desirable smart bracelet for older adults. In Proceedings of the 2013 ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication – UbiComp ‘13 Adjun (pp. 425–434).  https://doi.org/10.1145/2494091.2495974.CrossRefGoogle Scholar
  110. 110.
    Kumar, M., & Noble, C. H. (2016). Beyond form and function: Why do consumers value product design? Journal of Business Research, 69, 613–620.  https://doi.org/10.1016/j.jbusres.2015.05.017.CrossRefGoogle Scholar
  111. 111.
    Mugge, R., & Schoormans, J. P. L. (2012). Product design and apparent usability. The influence of novelty in product appearance. Applied Ergonomics, 43, 1081–1088.  https://doi.org/10.1016/j.apergo.2012.03.009.CrossRefGoogle Scholar
  112. 112.
    Reinecke, K., & Bernstein, A. (2011). Improving performance, perceived usability, and aesthetics with culturally adaptive user interfaces. ACM Transactions on Computer-Human Interaction, 18, 1–29.  https://doi.org/10.1145/1970378.1970382.CrossRefGoogle Scholar
  113. 113.
    Hinckley, K., Pausch, R., Goble, J. C., & Kassell, N. F. (1994). A survey of design issues in spatial input. In Proceedings of the 7th Annual ACM Symposium on User Interface Software and Technology – UIST ’94 (pp. 213–222). New York, NY: ACM Press.Google Scholar
  114. 114.
    Hwang, S., Song, J., & Gim, J. (2015). Harmonious haptics: Enhanced tactile feedback using a mobile and a wearable device. In Ext Abstr ACM CHI’15 Conference on Human Factors in Computing Systems (Vol. 2, pp. 295–298).  https://doi.org/10.1145/2702613.2725428.CrossRefGoogle Scholar
  115. 115.
    Tomberg, V., & Kelle, S. (2018). Universal design based evaluation framework for design of wearables (pp. 105–116).Google Scholar
  116. 116.
    Wentzel, D. J., Velleman, E. M., & van der, G. D. T. (2016). Wearables for all: development of guidelines to stimulate accessible wearable technology design. In 13th Web for All Conference. Part 34.Google Scholar
  117. 117.
    Kim, H., Kim, J., Lee, Y., et al. (2002). An empirical study of the use contexts and usability problems in mobile Internet. In Proceedings of the Annual Hawaii International Conference on System Sciences (pp. 1767–1776).Google Scholar
  118. 118.
    Yoon, S. H., Huo, K., & Ramani, K. (2016). Wearable textile input device with multimodal sensing for eyes-free mobile interaction during daily activities. Pervasive and Mobile Computing, 33, 17–31.  https://doi.org/10.1016/j.pmcj.2016.04.008.CrossRefGoogle Scholar
  119. 119.
    Swan, M. (2015). Connected car: Quantified self becomes quantified car. Journal of Sensor and Actuator Networks, 4, 2–29.  https://doi.org/10.3390/jsan4010002.CrossRefGoogle Scholar
  120. 120.
    Cai, L., & Zhu, Y. (2015). The challenges of data quality and data quality assessment in the big data era. Data Science Journal, 14, 2.  https://doi.org/10.5334/dsj-2015-002.CrossRefGoogle Scholar
  121. 121.
    Xia, X., Shihab, E., Kamei, Y., et al. (2016). Predicting crashing releases of mobile applications. In Proceedings of the 10th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement – ESEM ’16 (pp. 1–10).Google Scholar
  122. 122.
    Fogg, B. J. (2003). Persuasive technology: Using computers to change what we think and do. Persuas Technol Using Comput to Chang What We Think Do, 5, 283.  https://doi.org/10.4017/gt.2006.05.01.009.00.CrossRefGoogle Scholar
  123. 123.
    Zadra, J. R., & Clore, G. L. (2011). Emotion and perception: The role of affective information. Wiley Interdisciplinary Reviews: Cognitive Science, 2, 676–685.  https://doi.org/10.1002/wcs.147.CrossRefGoogle Scholar
  124. 124.
    Haas, H., Yin, L., Wang, Y., & Chen, C. (2016). What is LiFi? Journal of Lightwave Technology, 34, 1533–1544.  https://doi.org/10.1109/JLT.2015.2510021.CrossRefGoogle Scholar
  125. 125.
    Sharma, V., Rajput, S., & Sharma, P. K. (2016). Light fidelity (Li-Fi): An effective solution for data transmission (p. 020061).Google Scholar
  126. 126.
    Xu, T., Guo, A., Ma, J., & Wang, K. I.-K. (2017). Feature-based temporal statistical Modeling of data streams from multiple wearable devices. In 2017 IEEE 15th International Conference on Dependable, Autonomic and Secure Computing, 15th International Conference on Pervasive Intelligence and Computing, 3rd International Conference on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech) (pp. 119–126). IEEE.Google Scholar
  127. 127.
    Cenedese, A., Susto, G. A., & Terzi, M. (2017). A parsimonious approach for activity recognition with wearable devices: An application to cross-country skiing. In 2016 European Control Conference, ECC 2016 (pp. 2541–2546).Google Scholar
  128. 128.
    Shen, C., Xie, Y., Zhu, B., et al. (2017). Wearable woven supercapacitor fabrics with high energy density and load-bearing capability. Scientific Reports, 7.  https://doi.org/10.1038/s41598-017-14854-3.
  129. 129.
    Hannan, M. A., Mutashar, S., Samad, S. A., & Hussain, A. (2014). Energy harvesting for the implantable biomedical devices: Issues and challenges. Biomedical Engineering Online, 13.Google Scholar
  130. 130.
    Jeong, H., Kim, H., Kim, R., et al. (2017). Smartwatch wearing behavior analysis. Proceedings of the ACM Interactive, Mobile, Wearable Ubiquitous Technology, 1, 1–31.  https://doi.org/10.1145/3131892.CrossRefGoogle Scholar
  131. 131.
    Google Inc. (2012). The new multi-screen world study. Google Think Insights.Google Scholar
  132. 132.
    Ismail, N. A., Ahmad, F., Kamaruddin, N. A., & Ibrahim, R. (2016). A review on usability issues in mobile applications. IOSR Journal of Mobile Computing & Application, 3, 47–52.  https://doi.org/10.9790/0050-03034752.CrossRefGoogle Scholar
  133. 133.
    Ito, K., Sugano, S., Takeuchi, R., et al. (2013). Usability and performance of a wearable tele-echography robot for focused assessment of trauma using sonography. Medical Engineering & Physics, 35, 165–171.  https://doi.org/10.1016/j.medengphy.2012.04.011.CrossRefGoogle Scholar
  134. 134.
    Nielsen, J. (2012). Usability 101: Introduction to usability. https://www.nngroup.com/articles/usability-101-introduction-to-usability/
  135. 135.
    Papadopoulos, K. (2014). The impact of individual characteristics in self-esteem and locus of control of young adults with visual impairments. Research in Developmental Disabilities, 35, 671–675.  https://doi.org/10.1016/j.ridd.2013.12.009.CrossRefGoogle Scholar
  136. 136.
    Cecere, G., Corrocher, N., & Battaglia, R. D. (2015). Innovation and competition in the smartphone industry: Is there a dominant design? Telecommunications Policy, 39, 162–175.  https://doi.org/10.1016/j.telpol.2014.07.002.CrossRefGoogle Scholar
  137. 137.
    Ayyal Awwad, A. (2017). Localization to bidirectional languages for a visual programming environment on smartphones. International Journal of Computer Science Issues, 14, 1–13.  https://doi.org/10.20943/01201703.113.CrossRefGoogle Scholar
  138. 138.
    Collins, R. (1990). Culture, communication and national identity: The case of Canadian television. Toronto: University of Toronto Press.Google Scholar
  139. 139.
    Smith, A., & Yetim, F. (2004). Global human-computer systems: Cultural determinants of usability. Interacting with Computers, 16, 1–5.  https://doi.org/10.1016/j.intcom.2003.11.001.CrossRefGoogle Scholar
  140. 140.
    Khakurel, J., Porras, J., & Melkas, H. (2019). Human-centered design components in spiral model to improve mobility of older adults. In S. Paiva (Ed.), Mobile solutions and their usefulness in everyday life (pp. 83–104). Cham: Springer.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Jayden Khakurel
    • 1
    Email author
  • Jari Porras
    • 2
  • Helinä Melkas
    • 2
  • Bo Fu
    • 3
  1. 1.Research Center for Child Psychiatry, University of TurkuTurkuFinland
  2. 2.LUT UniversityLappeenrantaFinland
  3. 3.California State UniversityLong BeachUSA

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