Performance evaluation of different age groups for gestural interaction: a case study with Microsoft Kinect and Leap Motion

  • Diana Carvalho
  • Maximino Bessa
  • Luís Magalhães
  • Eurico Carrapatoso
Long Paper


With the thriving of different natural interaction paradigms—such as gesture-based interfaces—it becomes important to understand how these novel interfaces can influence users’ performance when it comes to their age. Recent advances made in human–computer interaction allow us to manipulate digital contents more intuitively; however, no work has yet been reported that systematically evaluates how gestural interfaces may influence the performance of different user groups. Different optical sensors, which allow human body acquisition with reliable accuracy, have been released, and with the appearance of such controllers for gesture recognition, it becomes important to understand if different age-related groups display similar performance levels concerning gestural interaction, or, on the other hand, if specific sensors could induce better results than others when dealing with users of different age brackets. In this article, we compare two gesture-sensing devices (Microsoft Kinect and Leap Motion) using the Fitts’ law model to evaluate target acquisition performance, with relation to three user groups: children, young adults and older adults. This case study involved 60 participants that were asked to perform a simple continuous selection task as quickly and accurately as possible using one of the devices for gestural recognition. Indeed, performance results showed statistically significant differences among the age groups in the selection task accomplished. However, when considering the users’ performance with regard to both input devices compared side by side, there were no significant differences in each group of users. We believe this situation could imply that the device itself might not have influenced the users’ performance, but actually the users’ age might. The participants feedback was interesting on account of their behaviors and preferences: Although there are no significant differences in performance, there could be when it comes to user preference.


Input devices Gestural interfaces Fitts’ law Performance evaluation/methodology Age Selection tasks Microsoft Kinect Leap Motion 



The authors would like to acknowledge the support and contribution of “Universidade de Trás-os-Montes e Alto Douro” and the schools that took part in this study: “Monsenhor Jerónimo do Amaral,” “Escola Secundária Morgado de Mateus,” and the studies center “Super-Heróis,” all in Vila Real, Portugal. Diana Carvalho has a Ph.D. fellowship granted by FCT—Fundação para a Ciência e a Tecnologia (SFRH/BD/81541/2011). This work is also supported by the project “R&D Project DOUROTUR - Tourism and technological innovation in the Douro/NORTE-01-0145-FEDER-000014” is financed by the European Regional Development Fund (FEDER), under the North Portugal Regional Operational Programme (2014/2020).


  1. 1.
    Accot, J., Zhai, S.: Performance evaluation of input devices in trajectory-based tasks. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems the CHI is the Limit—CHI ’99, pp. 466–472. ACM Press, New York (1999). doi: 10.1145/302979.303133.
  2. 2.
    Anastasiou, D.: Gestures in assisted living environments. In: Efthimiou, E., Kouroupetroglou, G., Fotinea, S.E. (eds.) Gesture and Sign Language in Human–Computer Interaction and Embodied Communication, Lecture Notes in Computer Science, vol. 7206. Springer, Berlin (2012). doi: 10.1007/978-3-642-34182-3.
  3. 3.
    Antle, A.N., Droumeva, M., Ha, D.: Hands on what? In: Proceedings of the 8th International Conference on Interaction Design and Children—IDC ’09, p. 80. ACM Press, New York (2009). doi: 10.1145/1551788.1551803
  4. 4.
    Beyer, H.: Tukey, John W.: Exploratory Data Analysis. Addison-Wesley Publishing Company Reading, Mass.—Menlo Park, Cal., London, Amsterdam, Don Mills, Ontario, Sydney, 1977, XVI, 688 S. Biometr. J. 23(4), 413–414 (1981). doi: 10.1002/bimj.4710230408
  5. 5.
    Bill Buxton.: CES 2010: NUI with Bill Buxton. (2010)
  6. 6.
    Blake, J.: The natural user interface revolution. In: Natural User Interfaces in .NET, Chapter 1, pp. 1–43. Manning Publications Co, Greenwich (2012)Google Scholar
  7. 7.
    Bobeth, J., Schrammel, J., Deutsch, S., Klein, M., Drobics, M., Hochleitner, C., Tscheligi, M.: Tablet, gestures, remote control? In: Proceedings of the 2014 ACM International Conference on Interactive Experiences for TV and Online Video—TVX ’14, pp. 139–146. ACM Press, New York (2014). doi: 10.1145/2602299.2602315.
  8. 8.
    Carvalho, D., Bessa, M., Oliveira, L., Guedes, C., Peres, E., Magalhães, L.: New interaction paradigms to fight the digital divide: a pilot case study regarding multi-touch technology. Proc. Comput. Sci. 14, 128–137 (2012). doi: 10.1016/j.procs.2012.10.015 CrossRefGoogle Scholar
  9. 9.
    Carvalho, D., Bessa, M., Peres, E., Magalhães, L., Guedes, C., Oliveira, L.: Developing a multi-touch serious game to fight the digital divide: the Portuguese ATM—a pilot case study. In: 7th Iberian Conference on Information Systems and Technologies (CISTI), pp. 1–6. IEEE, Madrid (2012)Google Scholar
  10. 10.
    Carvalho, D., Bessa, M., Magalhães, L.: Different interaction paradigms for different user groups: an evaluation regarding content selection. In: Proceedings of the XV International Conference on Human Computer Interaction—Interaccion’14, p. 40. ACM, Puerto de la Cruz (2014). doi: 10.1145/2662253.2662293
  11. 11.
    Carvalho, D., Bessa, M., Magalhaes, L., Carrapatoso, E.: Interaction paradigms versus age-related user profiles: an evaluation on content selection. IEEE Lat. Am. Trans. 13(2), 532–539 (2015). doi: 10.1109/TLA.2015.7055575 CrossRefGoogle Scholar
  12. 12.
    Chen, W.: Gesture-Based Applications for Elderly People, Chapter Gesture-Ba, pp. 186–195. Springer, Berlin (2013). doi: 10.1007/978-3-642-39330-3_20
  13. 13.
    Donker, A., Reitsma, P.: Young children’s ability to use a computer mouse. Comput. Educ. 48(4), 602–617 (2007). doi: 10.1016/j.compedu.2005.05.001.
  14. 14.
    Findlater, L., Froehlich, J.E., Fattal, K., Wobbrock, J.O., Dastyar, T.: Age-related differences in performance with touchscreens compared to traditional mouse input. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems—CHI ’13, p. 343. ACM Press, New York (2013). doi: 10.1145/2470654.2470703
  15. 15.
    Fitts, P.M.: The information capacity of the human motor system in controlling the amplitude of movement. J. Exp. Psychol. 47(6), 381–391 (1954). doi: 10.1037/h0055392.
  16. 16.
    Gerling, K., Livingston, I., Nacke, L., Mandryk, R.: Full-body motion-based game interaction for older adults. In: Proceedings of the 2012 ACM Annual Conference on Human Factors in Computing Systems—CHI ’12, p. 1873. ACM Press, New York (2012). doi: 10.1145/2207676.2208324.
  17. 17.
    Hilliges, O., Izadi, S., Wilson, A.D., Hodges, S., Garcia-Mendoza, A., Butz, A.: Interactions in the air. In: Proceedings of the 22nd Annual ACM Symposium on User Interface Software and Technology—UIST ’09, p. 139. ACM Press, New York (2009). doi: 10.1145/1622176.1622203
  18. 18.
    Hoaglin, D.C., Iglewicz, B.: Fine-tuning some resistant rules for outlier labeling. J. Am. Stat. Assoc. 82(400), 1147–1149 (1987)CrossRefGoogle Scholar
  19. 19.
    Holzinger, A. (ed.): HCI and Usability for Medicine and Health Care, Lecture Notes in Computer Science, vol. 4799. Springer, Berlin (2007). doi: 10.1007/978-3-540-76805-0.
  20. 20.
    Inai, Y.: Age-related differences in pointing movements in restricted visual tasks and their design implication. In: 2013 ICME International Conference on Complex Medical Engineering, pp. 439–443. IEEE (2013). doi: 10.1109/ICCME.2013.6548286
  21. 21.
    ISO.: ISO 9241-9 International standard: ergonomic requirements for office work with visual display terminals (VDTs)—Part 9: Requirements for non-keyboard input devices: International Organization for Standardization. Tech. Rep. (2000)Google Scholar
  22. 22.
    ISO.: ISO 9241–400:2007: ergonomics of human–system interaction—Part 400: Principles and requirements for physical input devices: International Organization for Standardization. Tech. rep, International Organization for Standardization (2007)Google Scholar
  23. 23.
    Jain, J., Lund, A., Wixon, D.: The future of natural user interfaces. In: Proceedings of the 2011 Annual Conference Extended Abstracts on Human Factors in Computing Systems—CHI EA ’11, p. 211. ACM Press, New York (2011). doi: 10.1145/1979742.1979527
  24. 24.
    Joiner, R., Messer, D., Light, P., Littleton, K.: It is best to point for young children: a comparison of children’s pointing and dragging. Comput. Hum. Behav. 14(3), 513–529 (1998). doi: 10.1016/S0747-5632(98)00021-1 CrossRefGoogle Scholar
  25. 25.
    Jonsson, I.M., Nass, C., Min Lee, K.: Mixing personal computer and handheld interfaces and devices: effects on perceptions and attitudes. Int. J. Hum. Comput. Stud. 61(1), 71–83 (2004). doi: 10.1016/j.ijhcs.2003.11.005 CrossRefGoogle Scholar
  26. 26.
    Kjeldsen, R., Kender, J.: Toward the use of gesture in traditional user interfaces. In: Proceedings of the Second International Conference on Automatic Face and Gesture Recognition, pp. 151–156 (1996)Google Scholar
  27. 27.
    Królak, A., Strumiłło, P.: Eye-blink detection system for human–computer interaction. Univ. Access Inf. Soc. 11(4), 409–419 (2011). doi: 10.1007/s10209-011-0256-6.
  28. 28.
    Labs T.: Myo gesture control armband—wearable technology (2016).
  29. 29.
    Lane, A.E., Ziviani, J.M.: Factors influencing skilled use of the computer mouse by school-aged children. Comput. Educ. 55(3), 1112–1122 (2010). doi: 10.1016/j.compedu.2010.05.008 CrossRefGoogle Scholar
  30. 30.
    Lemos, GCEMP.: Habilidades cognitivas e rendimento escolar entre o 5. e 12. anos de escolaridade. Ph.D. thesis, Universidade do Minho (2006)Google Scholar
  31. 31.
    Lievens, J., Van Daele, T.: Touch teach learn. Unlocking the potential of touch enabled mobile devices for higher education. In: INTED 2015 Proceedings 9th International Technology, Education and Development Conference, pp. 5298–5304 (2015)Google Scholar
  32. 32.
    Likert, R.: A technique for the measurement of attitudes. Arch. Psychol. 140, 55 (1932)Google Scholar
  33. 33.
    MacKenzie, I.S.: Fitts’ law as a research and design tool in human–computer interaction. Hum. Comput. Interact. 7(1), 91–139 (1992). doi: 10.1207/s15327051hci0701_3.
  34. 34.
    MacKenzie, I.S.: Movement time prediction in human–computer interfaces, pp. 483–492 (1995).
  35. 35.
    MacKenzie, I.S., Kauppinen, T., Silfverberg, M.: Accuracy measures for evaluating computer pointing devices. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems—CHI ’01, pp. 9–16. ACM Press, New York (2001). doi: 10.1145/365024.365028.
  36. 36.
    Mayer, R.E., Moreno, R.: Nine ways to reduce cognitive load in multimedia learning. Educ. Psychol. 38(1), 43–52 (2003). doi: 10.1207/S15326985EP3801_SlahUndXX6 CrossRefGoogle Scholar
  37. 37.
    Neerincx, M.A., Cremers, A.H.M., Kessens, J.M., van Leeuwen, D.A., Truong, K.P.: Attuning speech-enabled interfaces to user and context for inclusive design: technology, methodology and practice. Univ. Access Inf. Soc. 8(2), 109–122 (2008). doi: 10.1007/s10209-008-0136-x.
  38. 38.
    Newell, A., Card, S.: The prospects for psychological science in human–computer interaction. Hum. Comput. Interact. 1(3), 209–242 (1985). doi: 10.1207/s15327051hci0103_1 CrossRefGoogle Scholar
  39. 39.
    Nielsen, J.: Noncommand user interfaces. Commun. ACM 36(4), 83–99 (1993). doi: 10.1145/255950.153582.
  40. 40.
    Osborne, J.W., Overbay, A.: The power of outliers (and why researchers should always check for them). Pract. Assess. Res. Eval. 9(6) (2004).
  41. 41.
    Oviatt, S.L.: Multimodal interfaces. In: Sears, A., Jacko, J.A. (eds.) The human–computer Interaction Handbook: Fundamentals, Evolving Technologies and Emerging Applications, Multimodal Interfaces, 2nd edn, pp. 413–432. CRC Press, Boca Raton (2007)CrossRefGoogle Scholar
  42. 42.
    PrioVR.: Priovr // suit up. game on. (2016)
  43. 43.
    Ren, Z., Meng, J., Yuan, J., Zhang, Z.: Robust hand gesture recognition with kinect sensor. In: Proceedings of the 19th ACM International Conference on Multimedia—MM ’11, p. 759. ACM Press, New York (2011). doi: 10.1145/2072298.2072443.
  44. 44.
    Rohs, M., Oulasvirta, A., Suomalainen, T.: Interaction with magic lenses. In: Proceedings of the 2011 Annual Conference on Human Factors in Computing Systems—CHI ’11, p. 2725. ACM Press, New York (2011). doi: 10.1145/1978942.1979343.
  45. 45.
    Sambrooks, L., Wilkinson, B.: Comparison of gestural, touch, and mouse interaction with Fitts’ law. In: Proceedings of the 25th Australian Computer–Human Interaction Conference on Augmentation, Application, Innovation, Collaboration—OzCHI ’13, pp. 119–122. ACM Press, New York (2013). doi: 10.1145/2541016.2541066.
  46. 46.
    Schapira, E., Sharma, R.: Experimental evaluation of vision and speech based multimodal interfaces. In: Proceedings of the 2001 Workshop on Percetive User Interfaces—PUI ’01, p. 1. ACM Press, New York (2001). doi: 10.1145/971478.971481.
  47. 47.
    Schubö, A., Vesper, C., Wiesbeck, M., Stork, S.: Movement Coordination in Applied Human–Human and Human–Robot Interaction, Chapter Movement C, pp. 143–154. Springer, Berlin (2007). doi: 10.1007/978-3-540-76805-0_12
  48. 48.
    Shrawankar, U., Thakare, V.: Speech user interface for computer based education system. In: 2010 International Conference on Signal and Image Processing (CSIP), pp. 148–152 (2010)Google Scholar
  49. 49.
    Sodnik, J., Dicke, C., Tomažič, S., Billinghurst, M.: A user study of auditory versus visual interfaces for use while driving. Int. J. Hum. Comput. Stud. 66(5), 318–332 (2008). doi: 10.1016/j.ijhcs.2007.11.001 CrossRefGoogle Scholar
  50. 50.
    Soukoreff, R.W., MacKenzie, I.S.: Towards a standard for pointing device evaluation, perspectives on 27 years of Fitts’ law research in HCI. Int. J. Hum. Comput. Stud. 61(6), 751–789 (2004). doi: 10.1016/j.ijhcs.2004.09.001 CrossRefGoogle Scholar
  51. 51.
    Stephanidis, C.: User interfaces for all: new perspectives into human–computer interaction. In: Stephanidis, C. (eds.) User Interfaces for All: Concepts, Methods, and Tools, Chapter 1, p. 760. Lawrence Erlbaum Associates, Mahwah (2001)Google Scholar
  52. 52.
    Tecchia, F., Alem, L., Huang, W.: 3D helping hands. In: Proceedings of the 11th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and Its Applications in Industry—VRCAI ’12, p. 323. ACM Press, New York (2012). doi: 10.1145/2407516.2407590.
  53. 53.
    Toomim, M., Kriplean, T., Pörtner, C., Landay, J.: Utility of human–computer interactions. In: Proceedings of the 2011 Annual Conference on Human Factors in Computing Systems—CHI ’11, p. 2275. ACM Press, New York (2011). doi: 10.1145/1978942.1979277.
  54. 54.
    van Dam, A.: Post-WIMP user interfaces. Commun. ACM 40(2), 63–67 (1997). doi: 10.1145/253671.253708 CrossRefGoogle Scholar
  55. 55.
    Walter, R., Bailly, G., Müller, J.: StrikeAPose. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems—CHI ’13, p. 841. ACM Press, New York (2013). doi: 10.1145/2470654.2470774.
  56. 56.
    Weichert, F., Bachmann, D., Rudak, B., Fisseler, D.: Analysis of the accuracy and robustness of the leap motion controller. Sensors (Basel Switz) 13(5), 6380–6393 (2013). doi: 10.3390/s130506380.
  57. 57.
    Wigdor, D., Fletcher, J., Morrison, G.: Designing User Interfaces for Multi-touch and Gesture Devices, pp. 2755–2758. ACM, London (2009). doi: 10.1145/1520340.1520399
  58. 58.
    Zhai, S., Kong, J., Ren, X.: Speed-accuracy tradeoff in Fitts’ law tasks—on the equivalency of actual and nominal pointing precision. Int. J. Hum. Comput. Stud. 61(6), 823–856 (2004). doi: 10.1016/j.ijhcs.2004.09.007 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Diana Carvalho
    • 1
    • 2
  • Maximino Bessa
    • 1
    • 2
  • Luís Magalhães
    • 1
  • Eurico Carrapatoso
    • 1
  1. 1.INESC TECPortoPortugal
  2. 2.UTADVila RealPortugal

Personalised recommendations