Interaction Design Study for Movement-based Meditation Applications

Mindfulness and meditation practices have been studied for their benefits to mental and physical well-being. The aim of this PhD is to explore interactive meditation applications that utilized real-time and non-disruptive feedback to support alternative mindfulness practices. We used frameworks in the Attention Regulation Framework to design the applications. In the first study, we explored kinetic meditation as it might give more opportunities for more novel research in mindfulness practice and interaction between the users and the interactive kinetic meditation applications. The preliminary result showed positive responses and we will use the feedback to inform the next studies. The findings of this PhD will provide more information about designing interactive mindfulness applications.

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Author and article information

Contributors

Ulfa Octaviani

Prof. Nick Bryan-Kinns

Conference

Publication date: July 2022

Publication date (Print): July 2022

Pages: 1-2

Affiliations

[0001]Queen Mary University of London

London, UK.

Article

DOI: 10.14236/ewic/HCI2022.53

SO-VID: cdbd7a91-e368-486c-92bb-5af07ccbb0fb

License:

This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Conference name: 35th International BCS Human-Computer Interaction Conference

Conference acronym: HCI2022

Conference number: 35

Conference location: Keele, Staffordshire

Conference date: July 11th to 13th, 2022

Conference sponsor: Electronic Workshops in Computing (eWiC)

Conference theme: Towards a Human-Centred Digital Society

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1477-9358 BCS Learning & Development

Self URI (article page): https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/HCI2022.53

Self URI (journal page): https://ewic.bcs.org/

REFERENCES

Headspace. (2022). Headspace (Version 4.93.0) [Mobile app]. Google play store: Https://play.google.com/store/apps/details?id=com.getsomeheadspace.android&hl=en_GB
Li, B., & Han, L. (2013, October). Distance weighted cosine similarity measure for text classification. In International conference on intelligent data engineering and automated learning (pp. 611-618). Springer, Berlin, Heidelberg.
Liu, S. (2021). Cosine similarity. Posenet Similarity. (retrieved on 01 May 2020).
Liu, J., Zheng, Y., Wang, K., Bian, Y., Gai, W., & Gao, D. (2020). A real-time interactive tai chi learning system based on vr and motion capture technology. Procedia Computer Science, 174, 712-719.
Niksirat, K. S., Silpasuwanchai, C., Cheng, P., & Ren, X. (2019). Attention regulation framework: designing self-regulated mindfulness technologies. ACM Transactions on Computer-Human Interaction (TOCHI), 26(6), 1-44.
Papandreou, G., Zhu, T., Chen, L. C., Gidaris, S., Tompson, J., & Murphy, K. (2018). Personlab: Person pose estimation and instance segmentation with a bottom-up, part-based, geometric embedding model. In Proceedings of the European conference on computer vision (ECCV) (pp. 269-286).
Priyanka, S. K. (2021). A cross--sectional study of mental wellbeing with practice of yoga and meditation during COVID-19 pandemic. Journal of Family Medicine and Primary Care, 10(4), 1576
Radanliev, P., & De Roure, D. (2021). Alternative mental health therapies in prolonged lockdowns: narratives from Covid-19. Health and Technology, 11(5), 1101-1107.
Sigal, L. (2020). Human pose estimation. Computer Vision: A Reference Guide, 1-20.
Terzimehić, N., Häuslschmid, R., Hussmann, H., & Schraefel, M. C. (2019, May). A review & analysis of mindfulness research in HCI: Framing current lines of research and future opportunities. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1-13).
Yildiran, H., & Holt, R. R. (2015). Thematic analysis of the effectiveness of an inpatient mindfulness group for adults with intellectual disabilities. British journal of learning disabilities, 43(1), 49-54.

Comments

Comment on this article

[1] Headspace. (2022). Headspace (Version 4.93.0) [Mobile app]. Google play store: Https://play.google.com/store/apps/details?id=com.getsomeheadspace.android&hl=en_GB

[2] Li, B., & Han, L. (2013, October). Distance weighted cosine similarity measure for text classification. In International conference on intelligent data engineering and automated learning (pp. 611-618). Springer, Berlin, Heidelberg.

[3] Liu, S. (2021). Cosine similarity. Posenet Similarity. (retrieved on 01 May 2020).

[4] Liu, J., Zheng, Y., Wang, K., Bian, Y., Gai, W., & Gao, D. (2020). A real-time interactive tai chi learning system based on vr and motion capture technology. Procedia Computer Science, 174, 712-719.

[5] Niksirat, K. S., Silpasuwanchai, C., Cheng, P., & Ren, X. (2019). Attention regulation framework: designing self-regulated mindfulness technologies. ACM Transactions on Computer-Human Interaction (TOCHI), 26(6), 1-44.

[6] Papandreou, G., Zhu, T., Chen, L. C., Gidaris, S., Tompson, J., & Murphy, K. (2018). Personlab: Person pose estimation and instance segmentation with a bottom-up, part-based, geometric embedding model. In Proceedings of the European conference on computer vision (ECCV) (pp. 269-286).

[7] Priyanka, S. K. (2021). A cross--sectional study of mental wellbeing with practice of yoga and meditation during COVID-19 pandemic. Journal of Family Medicine and Primary Care, 10(4), 1576

[8] Radanliev, P., & De Roure, D. (2021). Alternative mental health therapies in prolonged lockdowns: narratives from Covid-19. Health and Technology, 11(5), 1101-1107.

[9] Sigal, L. (2020). Human pose estimation. Computer Vision: A Reference Guide, 1-20.

[10] Terzimehić, N., Häuslschmid, R., Hussmann, H., & Schraefel, M. C. (2019, May). A review & analysis of mindfulness research in HCI: Framing current lines of research and future opportunities. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1-13).

[11] Yildiran, H., & Holt, R. R. (2015). Thematic analysis of the effectiveness of an inpatient mindfulness group for adults with intellectual disabilities. British journal of learning disabilities, 43(1), 49-54.

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