Abstract
Context: Dance Movement Therapy (DMT) is a therapeutic modality that utilizes movement to promote holistic well-being. Current DMT assessment methods rely heavily on the subjective judgment of the DMT professional.
Objective: Our research aims to develop a framework composed of different components with specific functionalities that can be integrated with the DMT modality to improve the objectivity and efficiency of DMT evaluations.
Method: The DMT framework consists of an experimental protocol for data collection and a reference-supporting architecture that includes components for video analysis, physiological signal management, and evaluation tools. Artificial Intelligence (AI) based human pose estimation techniques are also employed to derive the DMT participants’ poses during the DMT sessions for more reliable movement analysis.
Results: Our preliminary results consist of demonstrating the effectiveness of the AI-based pose estimation tool, YOLO-NAS-Pose, in accurately detecting participants’ poses.
Conclusion: The proposed framework offers a promising approach to improving DMT practices by integrating and leveraging AI-based human pose estimation to evaluate participants’ movement in the DMT setting objectively. Future research will focus on refining the framework and developing user-friendly tools for widespread adoption in real DMT contexts.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
DMT is a complementary therapy with recognized professionals in accordance with current regulations. In Italy, DMT professionals are associated with the Italian Professional Association of Dance-Movement-Therapy (Associazione Professionale Italiana DMT, APID [4]). The APID provides various three-year professionalizing courses, at the end of which a DMT practitioner certificate is issued after discussing a thesis and passing a practical test in which the candidate performs a DMT session in his/her own setting.
- 2.
Information about BioHaness-3 Zephyr is available at: https://www.zephyranywhere.com/system/components..
- 3.
Information about Shimmer GSR+ is available at: https://shimmersensing.com/product/shimmer3-gsr-unit/.
- 4.
Information about ActiGraph wGT3X-BT is available at: https://theactigraph.com/actigraph-wgt3x-bt.
- 5.
Information about SOMI-1 is available at: https://instrumentsofthings.com/products/somi-1.
References
ADMP: ADMP - The Association for Dance Movement Psychotherapy UK: What is Dance Movement Psychotherapy? (2024). https://admp.org.uk/what-is-dance-movement-psychotherapy/. Accessed 04 Sept 2024
ADTA: ADTA-American dance therapy association (2024). https://www.adta.org/. Accessed 29 Aug 2024
Aharon, S., et al.: Deci-AI/super-gradients: 3.7.1 (2024). https://doi.org/10.5281/zenodo.10944954
APID: Associazione professionale italiana danzamovimentoterapia (2024). https://www.apid.it/. Accessed 29 Aug 2024
Bazarevsky, V.: BlazePose: on-device real-time body pose tracking. arXiv preprint arXiv:2006.10204 (2020)
Dunphy, K., Lebre, P., Dumaresq, E., Schoenenberger-Howie, S., Geipel, J., Koch, S.: Reliability and short version of the Dunphy Outcomes Framework (DOF): integrating the art and science of dance movement therapy. Arts Psychother. 85, 102063 (2023). https://doi.org/10.1016/j.aip.2023.102063. https://www.sciencedirect.com/science/article/pii/S0197455623000709
Dunphy, K., Mullane, S., Allen, L.: Developing an iPad app for assessment in dance movement therapy. Arts Psychother. 51, 54–62 (2016). https://doi.org/10.1016/j.aip.2016.09.001. https://www.sciencedirect.com/science/article/pii/S0197455615300253
EDMT: The European association of dance movement therapy (EADMT) (2024). https://eadmt.com/what-is-dance-movement-therapy-dmt. Accessed 29 Aug 2024
Google: MediaPipe Pose (nd). https://ai.google.dev/edge/mediapipe/solutions/vision/pose_landmarker. Accessed 03 Sept 2024
Kleinlooh, S., Samaritter, R., Van Rijn, R., Kuipers, G., Stubbe, J.: Dance movement therapy for clients with a personality disorder: a systematic review and thematic synthesis. Front. Psychol. 12, 581578 (2021)
Koch, S.C., Riege, R.F., Tisborn, K., Biondo, J., Martin, L., Beelmann, A.: Effects of dance movement therapy and dance on health-related psychological outcomes. A meta-analysis update. Front. Psychol. 10, 1806 (2019)
Lin, T.Y., et al.: Microsoft COCO: common objects in context (2015)
Paradisi, P., Raglianti, M., Sebastiani, L.: Online communication and body language. Front. Behav. Neurosci. 15, 709365 (2021)
de la Parra López, V., Panhofer, H.: Moments of meeting in DMT: characteristics and implications from the subjective experience of therapists. Am. J. Dance Ther. 45(1), 41–58 (2023)
Acknowledgments
This publication was produced with the co-funding European Union - Next Generation EU, in the context of The National Recovery and Resilience Plan, Investment 1.5 Ecosystems of Innovation, Project Tuscany Health Ecosystem (THE), CUP: B83C22003930001.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Daoudagh, S., Ignesti, G., Moroni, D., Sebastiani, L., Paradisi, P. (2025). Assessment of Dance Movement Therapy Outcomes: A Preliminary Proposal. In: Plácido da Silva, H., Cipresso, P. (eds) Computer-Human Interaction Research and Applications. CHIRA 2024. Communications in Computer and Information Science, vol 2371. Springer, Cham. https://doi.org/10.1007/978-3-031-83845-3_23
Download citation
DOI: https://doi.org/10.1007/978-3-031-83845-3_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-83844-6
Online ISBN: 978-3-031-83845-3
eBook Packages: Computer ScienceComputer Science (R0)