1. Dance Movement Therapy
in the Metaverse: A New
Frontier for Mental Health
• Brief Introduction
• Dr Petar Radanliev
• Oxford e-Research Centre,
Department of Engineering Science
• Background in wearable sensors and
data analytics
• Interest in merging wearable
technology with mental health
applications and physical disabilities.

2. 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.
The well-being of participants.
•Beck Anxiety Inventory (BAI) (the BAI9 is a scale to measure the severity of anxiety), Generalized Anxiety Disorder 7-item scale
(GAD-7)
•Penn State Worry Questionnaire (PSWQ).
•Other methodologies for data collection included Kruskal-Wallis H, Mann-Whitney U test, Pearson’s Chi-Square Test, etc.

3. Methodology
Overview
• Qualitative evidence synthesis and meta-analyses of primary quantitative data.
• Exploration of different alternative therapies with an emphasis on Dance Movement
Therapy.
• Data Collection and Analysis in the Dance Studio: BioX Sensor Band 2.0 | Output
• Data Collection and Analysis in Extended Realities
• Testbed equipped with cutting-edge XR technologies: VR, AR, and MR
headsets, complemented by haptic gloves, body sensors, and advanced
wearables.
• Physical Activity Correlation
• Skin Conductance
• Heart Rate Variability (HRV) Meta-analysis
• Photoplethysmography (PPG) Metrics
• Data collection, cleaning, and analysis | Tools & Methods
• Collecting, Cleaning, Analysing.
• Analytical Rigour | Data Dimensions | Sample Size Estimation
• Optimal Number of Participants

4. 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.
• 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.

5. Traditional
approach: AI
and ML
models
• Support Vector Machines (SVMs): used to classify different types
of dance movements - classification and regression tasks.
• Random Forests: used to improve the accuracy of dance
movement classification and prediction.
• Convolutional Neural Networks (CNNs): used to process video
data, capturing spatial hierarchies in dance movements.
• Recurrent Neural Networks (RNNs) and its variants (LSTM, GRU):
used for sequential data like time-series movement data.
• Decision trees, used to identify the factors that contribute to
different types of feelings and emotions in different 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.

6. 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. This was useful for analysing
specific dance postures or movements.
• Hidden Markov Models (HMMs): In this study, HMMs are used to recognise patterns
and sequences in dance movements.
• Clustering Algorithms: In this study, K-means or hierarchical clustering, are employed
to group similar dance movements and sequences.
• Dynamic Time Warping (DTW): A method to measure similarity between two
sequences that might not align perfectly, such as dance sequences.

7. 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.
• Physical Intensity Matching
• Ensuring Privacy
• Integrating Wearables and
Sensors
• Additional Categories in
Literature
• AI/ML Algorithms in Extended
Reality (XR) Devices

8. 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.
• Holistic Approach
• Personalised Exercise Selection
• Continuous Feedback Loop

9. Why Am I the
Perfect
Candidate?
• Practical experience in wearable sensors, data
collection, and analysis – directly aligns with the job
description.
• Profound understanding of data privacy and ethical
considerations.
• Proven expertise in harnessing technology for health
applications.
• Passionate about blending wearable technology with
health data science, exemplified by my recent project.
• Research Oversight by a Hybrid Researcher
• Outdoor Physical Activities Using Wearables

10. References
and Databases
• References: