Using fuzzy c-means clustering based on integration of psychological and physiological data for therapeutic music design

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 literatures have advocated music as an efficacious way to reduce stress;...

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Bibliographic Details
Published in:Journal of industrial and production engineering Vol. 34; no. 5; pp. 382 - 397
Main Authors: Chiu, Ming-Chuan, Hsu, Ya-Wen
Format: Journal Article
Language:English
Published: Abingdon Taylor & Francis 04.07.2017
Taylor & Francis Ltd
Subjects:
Alignment
Classification
Clustering
Cognition
Consistency
Disorders
Fuzzy c-means (FCM) clustering algorithm
Heart rate
Insomnia
Music
music therapy
personal cognition
Physiological effects
Physiology
Psychological stress
Psychology
Sleep
Stresses
Therapy
variability (HRV)
ISSN:2168-1015, 2168-1023
Online Access:Get full text
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Summary: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 literatures have 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. This study aims to investigate and identify the music characteristics which can relax people and which comprise the most soothing music for therapeutic application by utilizing fuzzy c-means clustering for classifying music for therapeutic music design. Our findings align with heart rate variability data to confirm the consistency in psychology and physiology. This study provides suggestions for music therapy service, indicating a strong positive direction for selecting music appropriate for therapeutic applications.
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ISSN:2168-1015
2168-1023
DOI:10.1080/21681015.2017.1324528