Abstract
The smoothness of smartphone operations is essential to user perceptual experience. However, the underlying mechanism of how smoothness can impact user experience has not been elucidated. In this paper, we conducted two experiments to explore factors that may potentially affect user perceptual experience of smoothness in smartphone operation and examined how these factors contributed. In experiment 1, ten participants were invited and they were sensitive to the smoothness of the smartphone. Participants used swiping gesture to manipulate 27 sequences with different forms of frame loss on the microblog simulation interface, and gave mean opinion scores (MOS) according to self perception. According to the experimental results, three factors that affect smoothness perception were found: single frame-loss number (SFLN), frame-loss time (FLT), and frame-loss interval (FLI). But the effect of each factor was effective only in some conditions. In experiment 2, 20 participants gave their MOS of 84 sequences, and these sequences were divided into three parts by three factors of experiment 1. Participants’ electrophysiological data was also collected to verify the validity of the participants’ scores. The results of an analysis of variance and Student–Newman–Keuls (SNK) test showed that a SFLN, FLT, and their interaction results significantly affected the user perception of smoothness in smartphone operation. Specifically, the effect of a SFLN was more significant than that of FLT. The effect of the FLI on user perceptual experience was relatively low, but the interactive effect of FLT and interval was significant. Finally, regression analyses were conducted to obtain the fitting formulas. Our research results reflect some preliminary suggestions, so as to guide developers to configure the performance of smart phones, in order to control the frame loss of smart phones and avoid bad user evaluation.
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References
Albert, W., Tullis, T.: Measuring the User Experience: Collecting, Analyzing, and Presenting Usability Metrics. Newnes, Oxford (2013)
Benjamini, Y., Hochberg, Y.: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B (Methodol.) 57(1), 289–300 (1995)
Bernhaupt, R., Drouet, D., Pirker, M.: Absolute indirect touch interaction: impact of haptic marks and animated visual feedback on usability and user experience. In: International Conference on Human-Centred Software Engineering, pp. 251–269. Springer (2018)
Carroll, J.M.: Human–computer interaction: psychology as a science of design. Annu. Rev. Psychol. 48(1), 61–83 (1997)
Chomeya, R.: Quality of psychology test between Likert scale 5 and 6 points. J. Soc. Sci. 6(3), 399–403 (2010)
Chu, ET-H., Lin, C-H.: Mobench: a software tool for measuring smoothness of mobile browsers. In: 2018 International Symposium on Computer, Consumer and Control (IS3C) , pp. 18–21. IEEE, Taichung, Taiwan. https://doi.org/10.1109/IS3C.2018.00014 (2018)
Copcu, H.I.M., Cheng, H.I.: The quality of contextual experience of multimedia on the smartphone. Int. J. Emerg. Sci. Eng. (IJESE) 3, 30–33 (2015)
Diniz, P.C., Rinard, M.C.: Eliminating synchronization overhead in automatically parallelized programs using dynamic feedback. ACM Trans. Comput. Syst. (TOCS) 17(2), 89–132 (1999)
Fabius, A.: Display buffering methods and systems. US Patent App. 16/027,525 (2019)
Fitzpatrick, B.: Writing zippy android apps. In: Google I/O Developers Conference (2010)
Forlizzi, J., Ford, S.: The building blocks of experience: an early framework for interaction designers. In: Proceedings of the 3rd Conference on Designing Interactive Systems: Processes, Practices, Methods, and Techniques, pp. 419–423 (2000)
Ge, Y., Chen, Y., Liu, Y., Li, W., SUN, X.: Electrophysiological measures applied in user experience studies. Adv. Psychol. Sci. 22(6), 959–967 (2014)
Han, H., Yu, J., Zhu, H., Chen, Y., Li, M.: E3: Energy-efficient engine for frame rate adaptation on smartphones. In: Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems (2013a)
Han, H., Yu, J., Zhu, H., Chen, Y., Yang, J., Xue, G., Zhu, Y., Li, M.: E3: Energy-efficient engine for frame rate adaptation on smartphones. In: Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems, pp. 1–14 (2013b)
Hao, T., Chen-Xi, L., Jia-Hao, S., Meng-Yun, MA.: Mobile video lag-time proportion experience based on physiological signal. Packaging Engineering (2017)
Harding, C., Srikukenthiran, S., Zhang, Z., Nurul Habib, K., Miller, E.: On the user experience and performance of smartphone apps as personalized travel survey instruments: Results from an experiment in toronto. In: Proceedings of the 11th International Conference on Transport Survey Methods (ISCTSC), Estrel, QC, Canada, pp. 24–29 (2017)
Haslett, M.: Dynamic feedback system and method for providing dynamic feedback. US Patent App. 15/693,614 (2019)
Hogan, L.C.: Designing for Performance: Weighing Aesthetics and Speed. O’Reilly Media, Inc, Newton (2014)
Houde, S., Hill, C.: What do prototypes prototype? In: Handbook of Human–Computer Interaction, pp. 367–381. Elsevier (1997)
Hudson, S.E., Mohamed, S.P.: Interactive specification of flexible user interface displays. ACM Trans. Inf. Syst. 8(3), 269–288 (1990)
Hulusić, V., Czanner, G., Debattista, K., Sikudova, E., Dubla, P., Chalmers, A.: Investigation of the beat rate effect on frame rate for animated content. In: Proceedings of the 25th Spring Conference on Computer Graphics, pp. 151–159 (2009)
Hwang, C., Pushp, S., Koh, C., Yoon, J., Liu, Y., Choi, S., Song, J.: Raven: Perception-aware optimization of power consumption for mobile games. In: Proceedings of the 23rd Annual International Conference on Mobile Computing and Networking, pp. 422–434 (2017)
ITU-T RECOMMENDATION P.: Subjective video quality assessment methods for multimedia applications. International Telecommunication Union (1999)
Janzen, B.F., Teather, R.J.: Is 60 fps better than 30? The impact of frame rate and latency on moving target selection. In: CHI’14 Extended Abstracts on Human Factors in Computing Systems, pp. 1477–1482. Association for Computing Machinery (2014)
Kirakowski, J.: The software usability measurement inventory: background and usage. Usability evaluation in industry, pp. 169–178 (1996)
Li, X., Li, G., Cui, X.: Retriple: reduction of redundant rendering on android devices for performance and energy optimizations. In: 2020 57th ACM/IEEE Design Automation Conference (DAC), pp. 1–6. IEEE (2020)
Li, X.F., Wang, Y., Wu, J., Jiang, K., Liu, B.W.: Mobile os architecture trends. Intel Technol. J. 16(4) (2012)
Likert, R.: A technique for the measurement of attitudes. Archives of Psychology. 22(140), 55 (1932)
Lin, YD., Chu, ETH., Chang, E., Lai, YC.: Smoothed graphic user interaction on smartphones with motion prediction. IEEE Trans. Syst. Man Cybern. Syst. 50(4): 1429–1441 (2017)
Lin, Y.-D., Chu, E.T.-H., Wen, C.-L., Lai, Y.-C., Chen, I.-C.: Benchmarking handheld graphical user interface: smoothness quality of experience. Comput. Electr. Eng. 68, 76–91 (2018)
Liu, T., Wang, Y., Boyce, J.M., Yang, H., Wu, Z.: A novel video quality metric for low bit-rate video considering both coding and packet-loss artifacts. IEEE J. Sel. Top. Signal Process. 3(2), 280–293 (2009)
Mandryk, R.L., Inkpen, K.M., Calvert, T.W.: Using psychophysiological techniques to measure user experience with entertainment technologies. Behav. Inf. Technol. 25(2), 141–158 (2006)
McCarthy, J.D., Sasse, M.A., Miras, D.: Sharp or smooth? comparing the effects of quantization vs. frame rate for streamed video. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 535–542 (2004)
McMillan, S.J., Hwang, J.S.: Measures of perceived interactivity: an exploration of the role of direction of communication, user control, and time in shaping perceptions of interactivity. J. Advert. 31(3), 29–42 (2002)
Moran, P., Smith, C.: The correlation between relatives on the supposition of Mendelian inheritance. Trans. R. Soc. Edinb. 52, 399–438 (1918)
Nilsson, E.: A recipe for responsiveness: strategies for improving performance in android applications. Independent thesis, Advanced level. Umeå University, Umeå, Sweden (2016)
Ntuen, C.A., Goings, M., Reddin, M., Holmes, K.: Comparison between 2-d and 3-d using an autostereoscopic display: the effects of viewing field and illumination on performance and visual fatigue. Int. J. Ind. Ergon. 39(2), 388–395 (2009)
Qu, QX., Zhang, L., Chao, WY., Duffy, V.: User experience design based on eye-tracking technology: a case study on smartphone apps. In: Advances in Applied Digital Human Modeling and Simulation, pp. 303–315. Springer (2017)
Rilvan, MA., Chao, J., Hossain, MS.: Capacitive swipe gesture based smartphone user authentication and identification. In: 2020 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA), pp. 1–8. IEEE (2020)
Roussou, M., Katifori, A.: Flow, staging, wayfinding, personalization: evaluating user experience with mobile museum narratives. Multimodal Technol. Interact. 2(2), 32 (2018)
Rowe, DW., Sibert, J., Irwin, D.: Heart rate variability: Indicator of user state as an aid to human-computer interaction. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 480–487 (1998)
Seferidis, V., Ghanbari, M., Pearson, D.: Forgiveness effect in subjective assessment of packet video. Electron. Lett. 28(21), 2013–2014 (1992)
Shijian, L., Shangshang, Z.: User experience oriented software interface design of handheld mobile devices. J Comput Aided Des Comput Graph. 22(6), 1034–1035 (2010)
Sillars, D.: High Performance Android Apps: Improve Ratings With Speed, Optimizations, and Testing. O’Reilly Media, Inc, Newton (2015)
Soares, MM., Vitorino, DF., Marçal, MA.: Application of digital infrared thermography for emotional evaluation: a study of the gestural interface applied to 3d modeling software. In: International Conference on Applied Human Factors and Ergonomics, pp. 201–212. Springer (2018)
Sundar, S.S., Bellur, S., Oh, J., Xu, Q., Jia, H.: User experience of on-screen interaction techniques: an experimental investigation of clicking, sliding, zooming, hovering, dragging, and flipping. Hum. Comput. Interact. 29(2), 109–152 (2014)
Tan, Z., Tan, X.: User-Oriented Research on Perceivable Indicators of Smartphone Interactive Operation Performance. Springer, Berlin (2018)
Tan, Z., Zhu, J., Chen, J., Li, F.: The effects of response time on user perception in smartphone interaction. In: International Conference on Applied Human Factors and Ergonomics, pp. 342–353. Springer (2018)
Tan, Z., Zhu, J., Chen, J., Li, F.: The effects of response time on user perception in smartphone interaction. In: Advances in Usability, User Experience and Assistive Technology, pp. 342–353. Springer International Publishing, Cham (2019)
Verkasalo, H.: Analysis of smartphone user behavior. In: 2010 Ninth International Conference on Mobile Business and 2010 Ninth Global Mobility Roundtable (ICMB-GMR), pp. 258–263. IEEE (2010)
Wang, Y., Rountev, A.: Profiling the responsiveness of android applications via automated resource amplification. In: 2016 IEEE/ACM International Conference on Mobile Software Engineering and Systems (MOBILESoft), pp. 48–58. IEEE (2016)
Ward, R.D., Marsden, P.H.: Physiological responses to different web page designs. Int. J. Hum Comput Stud. 59(1–2), 199–212 (2003)
Wenjun, H., Xiaoyu, G., Tiemeng, L.: Customer satisfaction evaluation model based on pupil size changes. Space Med. Med. Eng. 5, 001 (2013)
Yoon, H.J.: A study on the performance of android platform. Int. J. Comput. Sci. Eng. 4(4), 532 (2012)
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The research was supported by National Natural Science Foundation of China (61402159, 51605154) and Customer business group of Huawei Technology Co., Ltd.
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Tan, Z., Dai, N., Su, Y. et al. How does frame-loss affect users’ perception of smoothness?. CCF Trans. Pervasive Comp. Interact. 3, 199–221 (2021). https://doi.org/10.1007/s42486-021-00059-1
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DOI: https://doi.org/10.1007/s42486-021-00059-1