Abstract
Controlling home entertainment devices, like music and video, via an armband could free the user from using remote controls, but assessing their overall usability with mid-air and micro-gestures still represents an open research question today. For this purpose, this paper reports on results gained by jointly conducting and comparing two studies involving participants using a Thalmic Myo armband to control a NetFlix SmartTV and Spotify: (1) a gesture elicitation study to explore a richer set of user-defined gestures, to measure their effectiveness and the user subjective satisfaction of gesture interaction; (2) a System Usability Scale (SUS) to assess the overall usability of this setup and the subjective satisfaction for user-defined gestures.
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Bangor, A., Kortum, P., Miller, J.: Determining what individual SUS scores mean: adding an adjective rating scale. J. Usability Stud. 4(3), 114–123 (2009)
Bergold, J., Thomas, S.: Participatory research methods: a methodological approach in motion. Historical Social Research, pp. 191–222 (2012)
Brooke, J., et al.: SUS-A quick and dirty usability scale. In: Usability Evaluation in Industry, vol. 189, no. 194, pp. 4–7 (1996)
Chan, E., Seyed, T., Stuerzlinger, W., Yang, X.D., Maurer, F.: User elicitation on single-hand microgestures. In: Proceedings of the Conference on Human Factors in Computing Systems, pp. 3403–3414. ACM (2016)
Dalmazzo, D., Ramirez, R.: Air violin: a machine learning approach to fingering gesture recognition. In: 1st ACM SIGCHI International Workshop on Multimodal Interaction for Education, pp. 63–66. ACM (2017)
Dong, H., Danesh, A., Figueroa, N., El Saddik, A.: An elicitation study on gesture preferences and memorability toward a practical hand-gesture vocabulary for smart televisions. IEEE Access 3, 543–555 (2015)
Hewitt, J.: The myo gesture-control armband sense your muscle’s movements. ExtremeTech (online magazine) (2013)
Kerber, F., Puhl, M., Krüger, A.: User-independent real-time hand gesture recognition based on surface electromyography. In: 19th International Conference on HCI with Mobile Devices and Services, p. 36. ACM (2017)
Korkman, M.: NEPSY. A developmental neuropsychological assessment. Test materials and manual (1998)
Koskimäki, H., Siirtola, P., Röning, J.: Myogym: introducing an open gym data set for activity recognition collected using myo armband. In: International Joint Conference on Pervasive and Ubiquitous Computing, pp. 537–546. ACM (2017)
Lewis, J.R.: IBM computer usability satisfaction questionnaires: psychometric evaluation and instructions for use. Int. J. Hum. Comput. Interact. 7(1), 57–78 (1995)
Montero, F., López-Jaquero, V., Vanderdonckt, J., González, P., Lozano, M., Limbourg, Q.: Solving the mapping problem in user interface design by seamless integration in idealxml. In: Gilroy, S.W., Harrison, M.D. (eds.) Interactive Systems. Design, Specification, and Verification, pp. 161–172. Springer, Heidelberg (2006)
Montoya, M., Henao, O., Muñoz, J.: Muscle fatigue detection through wearable sensors: a comparative study using the myo armband. In: 18th International Conference on Human Computer Interaction, p. 30. ACM (2017)
Munroe, C., Meng, Y., Yanco, H., Begum, M.: Augmented reality eyeglasses for promoting home-based rehabilitation for children with cerebral palsy. In: The Eleventh ACM/IEEE International Conference on Human Robot Interaction, p. 565. IEEE Press (2016)
Rajavenkatanarayanan, A., Surathi, Y.V., Babu, A.R., Papakostas, M.: Myodrive: a new way of interacting with mobile devices. In: 9th ACM International Conference on PErvasive Technologies Related to Assistive Environments, p. 18. ACM (2016)
Sathiyanarayanan, M., Rajan, S.: Myo armband for physiotherapy healthcare: a case study using gesture recognition application. In: 8th International Conference on Communication Systems and Networks. pp. 1–6. IEEE (2016)
Tsai, W.L., Hsu, Y.L., Lin, C.P., Zhu, C.Y., Chen, Y.C., Hu, M.C.: Immersive virtual reality with multimodal interaction and streaming technology. In: 18th ACM International Conference on Multimodal Interaction, p. 416. ACM (2016)
Vanderdonckt, J., Roselli, P., Medina, J.L.P.: !FTL, an articulation-invariant stroke gesture recognizer with controllable position, scale, and rotation invariances. In: 20th International Conference on Multimodal Interaction, ICMI 2018, Boulder, CO, USA, October 16–20, 2018
Vatavu, R.D.: A comparative study of user-defined handheld vs. freehand gestures for home entertainment environments. J. Ambient Intell. Smart Environ. 5(2), 187–211 (2013)
Vatavu, R.D., Wobbrock, J.O.: Formalizing agreement analysis for elicitation studies: new measures, significance test, and toolkit. In: 33rd ACM Conference on Human Factors in Computing Systems, pp. 1325–1334. ACM (2015)
Wobbrock, J.O., Aung, H.H., Rothrock, B., Myers, B.A.: Maximizing the guessability of symbolic input. In: CHI’05 Extended Abstracts on Human Factors in Computing Systems, pp. 1869–1872. ACM (2005)
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Guérit, R., Cierro, A., Vanderdonckt, J., Pérez-Medina, J.L. (2019). Gesture Elicitation and Usability Testing for an Armband Interacting with Netflix and Spotify. In: Rocha, Á., Ferrás, C., Paredes, M. (eds) Information Technology and Systems. ICITS 2019. Advances in Intelligent Systems and Computing, vol 918. Springer, Cham. https://doi.org/10.1007/978-3-030-11890-7_60
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