隨著今日科技的發展,物質的便利與享受常常伴隨著人們壓力的提升。研究顯示,聽音樂能夠有效地幫助人們平緩情緒。然而先前文獻通常難以將音樂特性與人們的心理感受和生理訊號做完整地結合。根據感知工程的概念，在進行符合使用者設計的理念過程中需將個人認知以及生理訊號資料納為考慮因素。故本文旨在探討能夠使人心理與生理皆感到舒緩的音樂特性並期望能提供保健用途。本研究應用模糊 C 平均分群演算法並進行實驗以對 15 首純音樂進行分群,結果顯示較小的振幅變異、速度介於 96 到 128BPM 以及正向的歌曲能提供最好的舒緩效果。若將此視為一種治療型音樂設計的新型服務,本研究亦應用問卷與心跳變異訊號來探討其在服務設計階段的使用者經驗。經由生理與心理的一致性,本研究提供了音樂治療設計這項服務開發的相關建議,結果顯示使用者對於此項服務持有新奇與正面的看法,而在實用性方面需要加強開發。此結果未來將應用於音樂治療及智慧型選曲系統領域中,透過給予聽者適當的音樂,使治療的效果達到預期的目標。

As people’s lives become easier because of conveniences and richer in material assets, they may tend to worsen in terms of mental stress, resulting in physical conditions such as insomnia and related sleep disorders. Prior literature has advocated music as an efficacious way to reduce stress; however, only a few studies have connected music features with a combination of personal cognition factors and physiological signals. Kansei engineering considered personal cognition factors and physiological signals in user-oriented design. Based on this, this study aims to investigate and identify the music characteristics which can relax people and which comprise the most soothing music for therapeutic application. Our research used fuzzy c-means (FCM) clustering during analysis and conducted experiments to classify music accordingly. Results show that music with a low amplitude variability (in the range of 96-to-128 beats per minute (BPM)) generates the optimal positive feelings that can soothe the body and mind. With an eye toward developing this methodology as a promising new service for therapeutic music design, this study employed questionnaires and heart rate variability (HRV) signals to examine user experience (UX) at the products/service design stages. Our findings align with HRV data to confirm the consistency in psychology and physiology. A majority of study participants indicated they were drawn to the pragmatic elements of the endeavor. This study provides suggestions for the development of this service in relation to music therapy design, based on the analysis of music elements that indicate a strong positive direction for selecting music appropriate for therapeutic applications.

[1.] Appelhans, B. M., & Luecken, L. J. (2006). Heart rate variability as an index of regulated emotional responding. Review of general psychology, 10(3), 229-240.
[2.] Baltes, P. B., & Schaie, K. W. (Eds.). (2013). Life-span developmental psychology: Personality and socialization. Elsevier.
[3.] Bassellier, G., & Benbasat, I. (2004). Business competence of information technology professionals: conceptual development and influence on IT-business partnerships. MIS quarterly, 673-694.
[4.] Battarbee, K., & Koskinen, I. (2005). Co-experience: user experience as interaction. CoDesign, 1(1), 5-18.
[5.] Blood, A. J., & Zatorre, R. J. (2001). Intensely pleasurable responses to music correlate with activity in brain regions implicated in reward and emotion. Proceedings of the National Academy of Sciences, 98(20), 11818-11823.
[6.] Brecheisen, S., Kriegel, H. P., Kunath, P., & Pryakhin, A. (2006). Hierarchical genre classification for large music collections. Proceeding of 2006 IEEE International Conference, 1385-1388.
[7.] Bruscia, K. E. (Eds.). (2012). Readings on Music Therapy Theory. Barcelona Publishers.
[8.] Bunt, L., & Stige, B. (Eds.) (2014). Music Therapy: An Art Beyond Words: An Art Beyond Words. Routledge.
[9.] Cevasco, A. M., Kennedy, R., & Generally, N. R. (2005). Comparison of movement-to-music, rhythm activities, and competitive games on depression, stress, anxiety, and anger of females in substance abuse rehabilitation. Journal of Music Therapy, 42(1), 64-80.
[10.] Chamorro‐Premuzic, T., & Furnham, A. (2007). Personality and music: Can traits explain how people use music in everyday life?. British Journal of Psychology, 98(2), 175-185.
[11.] Cheng, H. T., Yang, Y. H., Lin, Y. C., Liao, I. B., & Chen, H. H. (2008). Automatic chord recognition for music classification and retrieval. Proceeding of 2008 IEEE International Conference on ACM, 1505-1508.
[12.] Cohen, H., Neumann, L., Shore, M., Amir, M., Cassuto, Y., & Buskila, D. (2000). Autonomic dysfunction in patients with fibromyalgia: application of power spectral analysis of heart rate variability. Seminars in Arthritis and Rheumatism, 29(4), 217-227.
[13.] Coutinho, E., & Cangelosi, A. (2011). Musical emotions: predicting second-by-second subjective feelings of emotion from low-level psychoacoustic features and physiological measurements. Emotion, 11(4), 921.
[14.] Cowie, R., Douglas-Cowie, E., Tsapatsoulis, N., Votsis, G., Kollias, S., Fellenz, W., & Taylor, J. G. (2001). Emotion recognition in human-computer interaction. IEEE Signal Processing Magazine, 18(1), 32-80.
[15.] Creswell, J. W. (Eds.). (2013). Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications.
[16.] International Organization for Standardization. (2010). Ergonomics of Human-system Interaction: Part 210: Human-centred Design for Interactive Systems. ISO.
[17.] Dorland, W. A. N. (Eds.). (2011). Dorland's Illustrated Medical Dictionary32: Dorland's Illustrated Medical Dictionary. Elsevier Health Sciences.
[18.] Eerola, T. (2011). Are the emotions expressed in music genre-specific? An audio-based evaluation of datasets spanning classical, film, pop and mixed genres. Journal of New Music Research, 40(4), 349-366.
[19.] Ellis, R. J., & Thayer, J. F. (2010). Music and autonomic nervous system (dys) function. Music perception, 27(4), 317-326.
[20.] Force, T. (1996). Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Circulation, 93(5), 1043-1065.
[21.] Fulton, B., & Medlock, M. (2003). Beyond focus groups: Getting more useful feedback from consumers. Game Developer's Conference, San Jose, CA, March 6–8.
[22.] Gaston, E. T. (1951). Dynamic music factors in mood change. Music Educators Journal, 37(4), 42-44.
[23.] Gabrielsson, A., & Lindström, E. (Eds.). (2010). The role of structure in the musical expression of emotions. Handbook of music and emotion: Theory, research, applications, 367-400.
[24.] Godleski, J. J., Verrier, R. L., Koutrakis, P., Catalano, P., Coull, B., Reinisch, U., & Killingsworth, C. (2000). Mechanisms of morbidity and mortality from exposure to ambient air particles. Research Report (Health Effects Institute), (91), 5-88.
[25.] Gomez, P., & Danuser, B. (2007). Relationships between musical structure and psychophysiological measures of emotion. Emotion, 7(2), 377.
[26.] Grewe, O., Nagel, F., Kopiez, R., & Altenmüller, E. (2007). Emotions over time: synchronicity and development of subjective, physiological, and facial affective reactions to music. Emotion, 7(4), 774.
[27.] Guzzetta, C. E. (1989). Effects of relaxation and music therapy on patients in a coronary care unit with presumptive acute myocardial infarction. Heart & lung: The Journal of Critical Care, 18(6), 609-616.
[28.] Han, B. J., Rho, S., Jun, S., & Hwang, E. (2010). Music emotion classification and context-based music recommendation. Multimedia Tools and Applications,47(3), 433-460.
[29.] Hassenzahl, M., & Tractinsky, N. (2006). User experience-a research agenda. Behaviour & Information Technology, 25(2), 91-97.
[30.] Hamilton, A. (Eds.) (2007). Aesthetics and music. Bloomsbury Publishing.
[31.] Husain, G., Thompson, W. F., & Schellenberg, E. G. (2002). Effects of musical tempo and mode on arousal, mood, and spatial abilities. Music Perception, 20(2), 151-171.
[32.] Jindo, T., & Hirasago, K. (1997). Application studies to car interior of Kansei engineering. International journal of industrial ergonomics, 19(2), 105-114.
[33.] Juslin, P. N., & Laukka, P. (2004). Expression, perception, and induction of musical emotions: A review and a questionnaire study of everyday listening. Journal of New Music Research, 33(3), 217-238.
[34.] Juslin, P. N., & Sloboda, J. A. (2013). Music and emotion. The Psychology of Music, 583-645.
[35.] Kerr, W. A. (1942). Psychological effects of music as reported by 162 defense trainees. The Psychological Record, 205-212.
[36.] Kim J, Andre E (2008) Emotion recognition based on physiological changes in music listening. IEEE Trans Pattern Anal Mach Intell 30(12):2067-2083.
[37.] Kim, K. H., Bang, S. W., & Kim, S. R. (2004). Emotion recognition system using short-term monitoring of physiological signals. Medical and biological engineering and computing, 42(3), 419-427.
[38.] Krout, R. E. (2007). Music listening to facilitate relaxation and promote wellness: Integrated aspects of our neurophysiological responses to music. The arts in Psychotherapy, 34(2), 134-141.
[39.] Krumnhansl C (1997) An exploratory study of musical emotions and psychophysiology. Can J Exp Physiol 51(4):336-352.
[40.] Law, E. L. C., Roto, V., Hassenzahl, M., Vermeeren, A. P., & Kort, J. (2009). Understanding, scoping and defining user experience: a survey approach. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 719-728.
[41.] Levitin, D. J. (Eds.) (2011). This is your brain on music: Understanding a human obsession. Atlantic Books Ltd.
[42.] Mandel, M. I., & Ellis, D. P. (2008). Multiple-instance learning for music information retrieval. In ISMIR 2008: Proceedings of the 9th International Conference of Music Information Retrieval, 577-582.
[43.] Mandryk, R. L., Atkins, M. S., & Inkpen, K. M. (2006, April). A continuous and objective evaluation of emotional experience with interactive play environments. Proceedings of the SIGCHI conference on Human Factors in computing systems, 1027-1036.
[44.] Milliman, R. E. (1986). The influence of background music on the behavior of restaurant patrons. Journal of Consumer Research, 286-289.
[45.] Nagamachi, M., 1991. An image technology expert system and its application to design consultation. Int. J. Human-Comput. Interaction 3 (3), 267–279.
[46.] Nagamachi, M. (1995). Kansei engineering: a new ergonomic consumer-oriented technology for product development. International Journal of industrial ergonomics, 15(1), 3-11.
[47.] Nagamachi, M. (2002). Kansei engineering as a powerful consumer-oriented technology for product development. Applied ergonomics, 33(3), 289-294.
[48.] Nakada, K. (1997). Kansei engineering research on the design of construction machinery. International Journal of Industrial Ergonomics, 19(2), 129-146.
[49.] Nunnally, J. C., Bernstein, I. H., & Berge, J. M. T. (1967). Psychometric theory (Vol. 226). New York: McGraw-Hill.
[50.] Okada, K., Kurita, A., Takase, B., Otsuka, T., Kodani, E., Kusama, Y. & Mizuno, K. (2009). Effects of music therapy on autonomic nervous system activity, incidence of heart failure events, and plasma cytokine and catecholamine levels in elderly patients with cerebrovascular disease and dementia. International Heart Journal, 50(1), 95-110.
[51.] Pagulayan, R. J., Keeker, K., Wixon, D., Romero, R. L., & Fuller, T. (2002). User-centered design in games.
[52.] Rauschenberger, M., Schrepp, M., Cota, M. P., Olschner, S., & Thomaschewski, J. (2013). Efficient measurement of the user experience of interactive products. How to use the user experience questionnaire (ueq). example: spanish language version. IJIMAI, 2(1), 39-45.
[53.] Rogers, Y., Sharp, H., Preece, J., & Tepper, M. (2007). Interaction design: beyond human-computer interaction. netWorker: The Craft of Network Computing, 11(4), 34.
[54.] Sachs, C. (1953). Rhythm and tempo: A study in music history. New York, Norton.
[55.] Scaringella, N., Zoia, G., & Mlynek, D. (2006). Automatic genre classification of music content: a survey. Signal Processing Magazine, IEEE, 23(2), 133-141.
[56.] Scherer, K. R., & Oshinsky, J. S. (1977). Cue utilization in emotion attribution from auditory stimuli. Motivation and emotion, 1(4), 331-346.
[57.] Schütte, S., & Eklund, J. (2005). Design of rocker switches for work-vehicles—an application of Kansei Engineering. Applied ergonomics, 36(5), 557-567.
[58.] Thayer, R. E. (1989). The biopsychology of mood and arousal. Oxford University Press.
[59.] Vermeeren, A. P., Law, E. L. C., Roto, V., Obrist, M., Hoonhout, J., & Väänänen-Vainio-Mattila, K. (2010). User experience evaluation methods: current state and development needs. In Proceedings of the 6th Nordic Conference on Human-Computer Interaction: Extending Boundaries, 521-530.
[60.] Walton, K. L. (1988). What is Abstract about the Art of Music?. Journal of Aesthetics and Art Criticism, 351-364.
[61.] Wang, H.F.& Y.C. Lee,(eds), Special issue on Approximate Reasoning with Words, Fuzzy Sets and Systems 127(1), North Holland, 2002.
[62.] Watkins, G. R. (1997). Music therapy: proposed physiological mechanisms and clinical implications. Clinical Nurse Specialist, 11(2), 43-50.
[63.] Watson, K. B. (1942). The nature and measurement of musical meanings. Psychological Monographs: General and Applied, 54(2), i-43.
[64.] Watson, D., Anna, L., and Tellegen, C. A. (1988). Development and Validation of Brief Measures of Positive and Negative Affect: The PANAS Scales, Journal of Personality and Social Psychology, 54(6), 1063-1070.
[65.] Webster, G. D., & Weir, C. G. (2005). Emotional responses to music: Interactive effects of mode, texture, and tempo. Motivation and Emotion, 29(1), 19-39.
[66.] Weihs, C., Ligges, U., Mörchen, F., & Müllensiefen, D. (2007). Classification in music research. Advances in Data Analysis and Classification, 1(3), 255-291.
[67.] Winchell, R. J., & Hoyt, D. B. (1996). Spectral analysis of heart rate variability in the ICU: a measure of autonomic function. Journal of Surgical Research, 63(1), 11-16.
[68.] Yen, P. Y. (Eds.) (2010). Health information technology usability evaluation: methods, models, and measures. Columbia University.
[69.] Zentner, M., Grandjean, D., & Scherer, K. R. (2008). Emotions evoked by the sound of music: characterization, classification, and measurement. Emotion, 8(4), 494-521.