Dance Movement Therapy in the Metaverse: A Fusion of Virtual Rhythms and Real Healing In the vast expanse of the digital universe, where pixels and avatars reign supreme, there lies an unexpected sanctuary of healing: dance. The metaverse, a realm of virtual reality (VR), augmented reality (AR), and mixed reality (MR), is not just a playground for gamers and tech enthusiasts. It's emerging as a therapeutic space where the age-old art of dance is being reimagined. As our physical and digital worlds intertwine, dance in the metaverse is not only a testament to the evolution of art but also a beacon of hope for those grappling with mental health challenges. This immersive dance movement therapy, blending the boundaries of the real and virtual, offers not just an exhilarating physical exercise but also a transformative journey for the mind. Dive with us into this rhythmic odyssey, where every move is a step towards wellness.

1. Dance Movement Therapy
and Wearable Sensors
Dr Petar Radanliev
University of Oxford

2. Dance Movement
Therapy in the
Metaverse: A New
Frontier for Mental
Health

3. Brief
Introduction

4. Oxford e-
Research Centre,
Department of
Engineering
Science

5. Background
in wearable
sensors and
data analytics

6. Problem Background
Mental health issues,
especially anxiety and
depression, are rising
globally
A need for non-
pharmacological
interventions
Potential of integrating
alternative therapies in
extended reality
environments, such as
the Metaverse

7. The well-
being of
participants
Ensured safety of the participants by
integrating established anxiety measures,
such as the Beck Anxiety Inventory ,
Generalized Anxiety Disorder 7-item
scale and the Penn State Worry
Questionnaire
Other methodologies for data collection
included qualitative case study research

8. Methodology
Overview
A blend of qualitative
evidence synthesis and
meta-analyses of primary
quantitative data
Exploration of different
alternative therapies with an
emphasis on Dance
Movement Therapy

9. Data Collection
and Analysis in
the Dance Studio:
BioX Sensor Band
2.0 | Output

10. Inertial
Measurement
Unit raw data
of arm motion,
leg motion

11. Force Sensitive
Resistor raw
data of arm/leg
muscle strength

12. Data
Collection and
Analysis in
Extended
Realities
The primary data acquisition
hinges predominantly on
quantitative metrics
harvested from an array of
wearable medical devices
and body-worn sensors; all
centralised within a Testbed
Heart Rate Variability Meta-
analysis: HRV, coupled with
spontaneous heart rate
fluctuations, offers
invaluable insights into the
workings of the autonomic
system, especially
concerning anxiety-afflicted
patients who aren't
simultaneously grappling
with cardiovascular ailments
Photoplethysmography
Metrics: HRV is also
quantified using PPG, a
method geared towards
capturing shifts in
microvascular perfusion by
inundating the skin with light
and subsequently
quantifying either the
transmitted or reflected
intensities

13. Data collection,
cleaning, and
analysis | Tools
& Methods

14. Application
Programming
Interfaces and
Software
Development Kit

15. Wireless
transmission

16. Cleaning

17. Pandas

18. Sci-kit learn

19. TensorFlow
and Keras

20. Matplotlib
and Seaborn

21. Analytical
Rigour
POTENTIAL CONFOUNDING
FACTORS, ESPECIALLY IN SKIN
CONDUCTANCE MEASUREMENTS
PROTOCOLS TO COUNTERACT
THE IMPACT OF SWEAT

22. Data
Dimensions
A comprehensive classification and
understanding of the data are
established
This includes the differentiation
between data types, classifications,
and scales, ensuring appropriate
and meaningful data analysis
techniques

23. Sample Size
Estimation

24. Optimal
Number of
Participants

25. My Role in
Data
Collection
Engagement in primary research
data collection
Use of wearable sensors to
measure and analyse movement
and biofeedback during Dance
Movement Therapy
Ensured data was securely stored,
prioritising user privacy

26. Data Collection and Analysis
UTILISED WEARABLE SENSORS TO
GATHER DATA ON PARTICIPANTS’
MOVEMENTS, PHYSIOLOGICAL
RESPONSES, AND EMOTIONAL FEEDBACK
APPLIED RIGOROUS DATA-CLEANING
TECHNIQUES
ANALYSED RESULTS IN THE CONTEXT OF
MENTAL HEALTH IMPROVEMENT,
PRIMARILY FOCUSING ON ANXIETY AND
DEPRESSION

27. Traditional
approach: AI
and ML
models
Convolutional Neural Networks : used
to process video data, capturing
spatial hierarchies in dance
movements
DeepDance model uses a
combination of CNNs and RNNs to
learn the temporal and spatial patterns
of dance movements
The DeepDance model has been
shown to be effective in classifying
different types of dance movements,
as well as in predicting the outcome of
a dance performance

28. Experimental
approach: AI
and ML
models
Time Series Analysis: In this study,
dance movements are captured as
time series data
Algorithms specifically designed for
analysing such data include ARIMA
or LSTM networks
Human Pose Estimation Algorithms:
In this study, these algorithms are
used to detect and track the human
body's key points in a sequence

29. Findings:
problem/domain
importance
Dance Movement Therapy in
extended reality environments
shows potential as a beneficial
alternative therapy
Highlighted data privacy and
ethical considerations -
essential for user trust and
legal compliance
Emphasised the need for
securely storing metadata

30. Physical Intensity Matching
Novelty: Alternative
therapies should
match the physical
intensity of DMT
New Knowledge:
UT2 training, akin to
light jogging, can
mirror DMT's
physiological benefits

31. Ensuring
Privacy
Novelty: The importance of
participant privacy in
emulating DMT's therapeutic
benefit of free expression
New Knowledge: Exercises
suggested for closed-door
rooms can enhance
participants' comfort

32. Integrating
Wearables
and Sensors
Novelty: The integration of wearables
like accelerometers and smartwatches
to gauge therapy's physiological
impact
New Knowledge: Wearables can offer
real-time insights into participants'
physiological responses
Novelty: The integration of other
therapeutic areas such as eye-
movement, whole-body-motion, heart-
rate, breathing-rate, and energy-
expenditure estimation

33. Implications for
Practice:
problem/domain
importance
Extended reality environments like the
Metaverse could transform mental
health practices
Extended reality introduces efficient,
engaging, and effective non-
pharmacological interventions for
various mental health conditions
Instead of seeking a linear relationship
between the number of therapies and
their effectiveness, the research
hypothesizes a cumulative advantage

34. Personalised
Exercise
Selection
Novelty: The need for
customised exercises based on
individual preferences and
physical abilities
New Knowledge: Offering a
menu of cardio exercises
ensures flexibility and increased
engagement in the therapy
Novelty: The dynamic nature of
alternative therapies, adapting
strategies based on participant
feedback

35. Why Am I
the Perfect
Candidate?
Practical experience in wearable sensors, data
collection, and analysis – directly aligns with the
job description
Novelty: The requirement for a hybrid researcher
familiar with ICO guidelines, data privacy,
cybersecurity, information engineering, XR, DMT,
and alternative therapies
New Knowledge: This integrated expertise is
crucial for navigating the intersection of emotion
analysis technologies and mental health
therapies

36. References
and
Databases

37. References

38. Databases

39. Databases

40. Databases