應用模糊C平均分群演算法與HRV提升治療型音樂的使用者經驗

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。

關鍵字

音樂治療；模糊 C 平均分群演算法；心跳變異性；使用者經驗；個人認知

並列摘要

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.

並列關鍵字

Music Therapy ； Fuzzy C-Means (FCM) Clustering Algorithm ； Personal Cognition ； Heart Rate Variability (HRV) ； User Experience (UX)

參考文獻

[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.

國際替代計量

應用模糊C平均分群演算法與HRV提升治療型音樂的使用者經驗

全文下載

主題瀏覽