Cover not available
Article published In:
Interaction Studies
Vol. 16:2 (2015) ► pp.303–339

Follow your heart

Heart rate controlled music recommendation for low stress air travel

| Beijing, China |
| Department of Industrial Design, Eindhoven University of Technology |
| Department of Industrial Design, Eindhoven University of Technology |

Long distance travel is an unusual activity for humans. The economical cabin environment (low air circulation, limited space, low humidity, etc.) during the long haul flights causes discomfort and even stress for many passengers. In-flight video and music systems are commonly available to improve the comfort level of the passengers. However, current in-flight music systems do not explore how the content can be used to reduce passengers stress. Most of these systems are designed and implemented assuming a homogeneous passenger group that has similar tastes and desires. In this paper, we present a heart rate controlled in-flight music recommendation system for reducing the stress during air travel. The system recommends personalized music playlists to the passengers and attempts to keep their heart rate in a normal range with these playlists. Experiments in a simulated long haul flight cabin environment find that the passengers’ stress can indeed be significantly reduced through listening to the recommended music playlists.

Keywords: stress, music recommendation, bradycardia, tachycardia, heart rate control, stress reduction, bio feedback, heart rate variability
Article outline
  • 1.Introduction
  • 2.Related work
    • 2.1Current in-flight music systems
    • 2.2Current music recommendation methods
  • 3.Framework
    • 3.1User scenario
    • 3.2Adaptive framework
      • 3.2.1Heart rate as an indicator of stress
      • 3.2.2Music
      • 3.2.3User profile
      • 3.2.4Relation between music, heart rate and stress
      • 3.2.5Adaptive inference
      • 3.2.6Interaction
      • 3.2.7Preference learning
  • 4.Implementation
  • 5.User experiment
    • 5.1Setup
    • 5.2Test subjects
    • 5.3Procedure
    • 5.4Experiment variables
      • 5.4.1Variables
      • 5.4.2Data acquisition
        • 5.4.2.1Control variables
        • 5.4.2.2Simulation quality
        • 5.4.2.3Independent variable
        • 5.4.2.4Dependent variables
  • 5.5Results
    • 5.5.1Flight simulation quality
    • 5.5.2Control variables
    • 5.5.3Heart rate control
    • 5.5.4Stress reduction
      • 5.5.4.1Stress scale
      • 5.5.4.2Stress level
  • 6.Limitations
  • 7.Conclusions
  • Acknowledgement
  • Notes
  • References
Published online: 26 November 2015
DOI logo
https://doi.org/10.1075/is.16.2.12liu
References
Abrazhevich, D., Markopoulos, P., & Rauterberg, M
(2009) Designing internet-based payment systems: Guidelines and empirical basis. Human-Computer Interaction, 24(4), 408–443. DOI logoGoogle Scholar
Atluri, M
(2008) Does music affect blood pressure and heart rate? Project Number J1103, California State Science Fair.Google Scholar
Baltrunas, L., Kaminskas, M., Ludwig, B., Moling, O., Ricci, F., Aydin, A., Lke, K.-H., & Schwaiger, R
(2011) Incarmusic: Context-aware music recommendations in a car. In E-Commerce and web technologies, (pp. 89–100). Springer. DOI logoGoogle Scholar
Bartenwerfer, H
(1969) Einige praktische konsequenzen der aktivierungstheorie. Zeitschrift fur experimentelle und angewandte Psychologie, 161, 195–222.Google Scholar
Bartneck, C., & Hu, J
(2005) Presence in a distributed media environment. In L. Terrenghi, I. Lindt, A. Butz, & M. Kuniavsky (eds.), User experience design for pervasive computing, pervasive 2005, München, Munich, Germany: Ludwig-Maximilians-Universitat. Retrived on October 27, 2014. from http://www.fluidum.org/events/experience05/cameraready/bartneck.pdfGoogle Scholar
Basilico, J., & Hofmann, T
(2004) Unifying collaborative and content-based filtering. In 21st International conference on Machine learning (ICML) , 65–72. ACM. DOI logo
Bernardi, L., Porta, C., & Sleight, P
(2006) Cardiovascular, cerebrovascular, and respiratory changes induced by different types of music in musicians and non-musicians: The importance of silence. Heart, 92(4), 445–452. DOI logoGoogle Scholar
Cano, P., Koppenberger, M., & Wack, N (2005) Content-based music audio recommendation. In 13th Annual ACM International Conference on Multimedia (MULTIMEDIA ‘05) , ­211–212. New York, USA: ACM. DOI logo
Çataltepe, Z., & Altinel, B
(2007) Hybrid music recommendation based on different dimensions of audio content and an entropy measure. In 15th European Signal Processing Conference (EU-SIPCO) , 936–940.
Chai, W., & Vercoe, B
(2000) Using user models in music information retrieval systems. In 1st Annual International Symposium on Music Information Retrieval (ISMIR 2000) , Retrieved on October 27, 2014, from http://ciir.cs.umass.edu/music2000/posters/chai.pdf
Collins, S., Karasek, R., & Costas, K
(2005) Job strain and autonomic indices of cardiovascular disease risk. American Journal of Industrial Medicine, 48(3): 182–193. DOI logoGoogle Scholar
Dumur, E., Barnard, Y., & Boy, G
(2004) Designing for comfort. Human Factors in Design, 111–127.Google Scholar
Elahi, M., Ricci, F., & Rubens, N
(2013) Active learning strategies for rating elicitation in collaborative filtering: A system-wide perspective. ACM Transactions on Intelligent Systems and Technology (TIST), 5(1), 13. DOI logoGoogle Scholar
Elsenbruch, S., Harnish, M., & Orr, W
(1999) Heart rate variability during waking and sleep in healthy males and females. Sleep, 22(8): 1067–1071. DOI logoGoogle Scholar
Elwess, L., & Vogt, F
(2005) Heart rate and stress in a college setting. Bioscience, 31(4), 20–23.Google Scholar
Golbandi, N., Koren, Y., & Lempel, R
(2011) Adaptive bootstrapping of recommender systems using decision trees. In Proceedings of the Fourth ACM International Conference on Web Search and Data Mining , 595–604. ACM. DOI logo
Haas, M., Rijsdam, J., Thomee, B., & Lew, M
(2004) Relevance feedback: Perceptual learning and retrieval in bio-computing, photos, and video. In MIR ‘04: Proceedings of the 6th ACM SIGMM International Workshop on Multimedia Information Retrieval , 151–156, New York, NY, USA, ACM. DOI logo
Hoi, S., Lyu, M., & Jin, R
(2006) A unified log-based relevance feedback scheme for image retrieval. IEEE Transactions on Knowledge and Data Engineering, 18(4), 509–524. DOI logoGoogle Scholar
Hu, J
(2006) Design of a distributed architecture for enriching media experience in home theaters. Phd thesis, Department of Industrial Design, Eindhoven University of Technology.Google Scholar
Hu, J., & Bartneck, C
(2008) Culture matters – a study on presence in an interactive movie. CyberPsychology and Behavior, 11(5), 529–535. DOI logoGoogle Scholar
Iwanaga, M
(1995) Relationship between heart rate and preference for tempo of music. Perceptual and Motor Skills, 81(2), 435–440. DOI logoGoogle Scholar
Kaminskas, M., & Ricci, F
(2012) Contextual music information retrieval and recommendation: State of the art and challenges. Computer Science Review, 6(2), 89–119. DOI logoGoogle Scholar
Karjalainen, M
(1985) A new auditory model for the evaluation of sound quality of audio systems. In Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP’85 , vol. 101, 608–611. IEEE. DOI logo
Knees, P., Pohle, T., Schedl, M., & Widmer, G
(2006) Combining audio-based similarity with web-based data to accelerate automatic music playlist generation. In Proceedings of the 8th ACM International Workshop on Multimedia Information Retrieval , 147–154. ACM. DOI logo
Knight, W., & Rickard, N (2001) Relaxing music prevents stress-induced increases in subjective anxiety, systolic blood pressure, and heart rate in healthy males and females. Journal of Music Therapy, 38(4), 254–272. DOI logoGoogle Scholar
Koenemann, J
(1996) Supporting interactive information retrieval through relevance feedback. In CHI ‘96: Conference Companion on Human Factors in Computing Systems , 49–50, New York, NY, USA. ACM. DOI logo
Kunz, M.G
(2008) Zombie Notes Bradycardia – Heart Blocks. Dickson Keanaghan, LLC.
Liu, H
(2010) Biosignal controlled recommendation in entertainment systems. Phd thesis, Department of Industrial Design, Eindhoven University of Technology.Google Scholar
Liu, H., Hu, J., & Rauterberg, M
(2008) Airsf: A new entertainment adaptive framework for stress free air travels. In Proceedings of the 2008 International Conference on Advances in Computer Entertainment Technology , 183–186. ACM. DOI logo
(2009a) Bio-feedback based in-flight music system design to promote heart health. In International Conference on Machine Learning and Cybernetics (ICMLC) , 446–450. Baoding, China. Citeseer.
(2009b) Music playlist recommendation based on user heartbeat and music preference. In International Conference on Computer Technology and Development (ICCTD’09) , vol. 11, 545–549. IEEE. DOI logo
Masuhr, J., Klompmaker, F., Reimann, C., & Nebe, K
(2008) Designing context-aware in-car information systems. In Proceedings of the 5th Annual International Conference on Mobile and Ubiquitous Systems: Computing, Networking, and Services (Mobiquitous ‘08) , 1–8. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering). DOI logo
Miluk-Kolasa, B., Matejek, M., & Stupnicki, R
(1996) The effects of music listening on changes in selected physiological parameters in adult pre-surgical patients. Journal of Music Therapy, 33(8), 208–218. DOI logoGoogle Scholar
Mokbel, M., & Levandoski, J
(2009) Toward context and preference-aware location-based services. In Eighth ACM International Workshop on Data Engineering for Wireless and Mobile Access , 25–32. ACM. DOI logo
Rauterberg, G
(2006) Hci as an engineering discipline: To be or not to be!? African Journal of Information and Communication Technology, 2(4), 163–184. DOI logoGoogle Scholar
Rentfrow, P., & Gosling, S
(2003) The do re mi’s of everyday life: The structure and personality correlates of music preferences. Journal of Personality and Social Psychology, 84(6), 1236–1256. DOI logoGoogle Scholar
Rubens, N., Kaplan, D., & Sugiyama, M
(2011) Active learning in recommender systems. In Recommender Systems Handbook, 735–767. Springer. DOI logoGoogle Scholar
Sarwar, B., Karypis, G., Konstan, J., & Reidl, J
(2001) Item-based collaborative filtering recommendation algorithms. In 10th International Conference on World Wide Web (WWW ‘01) , 285–295, New York. ACM. DOI logo
Siddiqi, R
(2011) The world of 21st century in-flight entertainment. Aviation & Tourism (FRIDAY, 19 AUGUST 2011) in The Independent.Google Scholar
Steelman, V
(1991) Relaxing to the beat: Music therapy in perioperative nursing. Today’s OR Nurse, 13(7), 18.Google Scholar
Stratton, V., & Zalanowski, A
(1984) The relationship between music, degree of liking, and self-reported relaxation. Journal of Music Therapy, 21(4), 184–192. DOI logoGoogle Scholar
Su, J., Yeh, H., Yu, P., & Tseng, V (2010) Music recommendation using content and context information mining. Intelligent Systems, IEEE, 25(1), 16–26. DOI logoGoogle Scholar
Su, X., & Khoshgoftaar, T
(2009) A survey of collaborative filtering techniques. Advances in Artificial Intelligence, 2009, 1–19 (Article ID 421425). DOI logoGoogle Scholar
Suh, Y., Park, Y., Yoon, H., Chang, Y., & Woo, W
(2007) Context-aware mobile ar system for personalization, selective sharing, and interaction of contents in ubiquitous computing environments. In J. Jacko (ed.), Human-Computer Interaction. Interaction Platforms And Techniques, Lecture Notes in Computer Science, 966–974. Springer Berlin/Heidelberg. DOI logoGoogle Scholar
Taelman, J., Vandeput, S., Spaepen, A., & Huffel, S
(2009) Influence of mental stress on heart rate and heart rate variability. In J. Sloten, P. Verdonck, M. Nyssen, & J. Haueisen (eds.), 4th European Conference of the International Federation for Medical and Biological Engineering, volume 22 of IFMBE Proceedings , 1366–1369. Springer Berlin Heidelberg. DOI logoGoogle Scholar
White, J., & Shaw, C
(1991) Music therapy: A means of reducing anxiety in the myocardial infarction patient. Wisconsin Medical Journal, 90(7), 434–437.Google Scholar
Witmer, B., & Singer, M
(1998) Measuring presence in virtual environments: A presence questionnaire. Presence, 7(3), 225–240. DOI logoGoogle Scholar
Woerndl, W., Schueller, C., & Wojtech, R
(2007) A hybrid recommender system for context-aware recommendations of mobile applications. In 2007 IEEE 23rd International Conference on Data Engineering Workshop , 871–878. Istanbul. DOI logo
World Health Organization
(2005) Travel by air: Health considerations, in International travel and health: Situation as on 1 January 2005. World Health Organization. DOI logoGoogle Scholar
Yoshii, K., Goto, M., Komatani, K., Ogata, T., & Okuno, H
(2006) Hybrid collaborative and content-based music recommendation using probabilistic model with latent user preferences. In 7th International Conference on Music Information Retrieval (ISMIR) , 296–301.
Cited by

Cited by 5 other publications

Chaudhari, Kinjal & Ankit Thakkar
2020. A Comprehensive Survey on Travel Recommender Systems. Archives of Computational Methods in Engineering 27:5 ► pp. 1545 ff. DOI logo
Feng, Yuan, Ruud van Reijmersdal, Suihuai Yu, Matthias Rauterberg, Jun Hu & Emilia Barakova
2018. Dynamorph: Montessori Inspired Design for Seniors with Dementia Living in Long-Term Care Facilities. In Intelligent Technologies for Interactive Entertainment [Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 215], ► pp. 49 ff. DOI logo
Lee, Wei-Po, Chun-Ting Chen, Jhih-Yuan Huang & Jhen-Yi Liang
2017. A smartphone-based activity-aware system for music streaming recommendation. Knowledge-Based Systems 131 ► pp. 70 ff. DOI logo
Motamedi, Elham
2021. Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization, ► pp. 304 ff. DOI logo
Motamedi, Elham & Marko Tkalčič
2023. User-centric item characteristics for personalized multimedia systems: A systematic review. Intelligenza Artificiale 17:2 ► pp. 207 ff. DOI logo

This list is based on CrossRef data as of 31 march 2024. Please note that it may not be complete. Sources presented here have been supplied by the respective publishers. Any errors therein should be reported to them.