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Dance Movement Therapy
and Wearable Sensors
Dr Petar Radanliev
University of Oxford
Dance Movement
Therapy in the
Metaverse: A New
Frontier for Mental
Health
Brief
Introduction
Oxford e-
Research Centre,
Department of
Engineering
Science
Background
in wearable
sensors and
data analytics
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
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
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
Data Collection
and Analysis in
the Dance Studio:
BioX Sensor Band
2.0 | Output
Inertial
Measurement
Unit raw data
of arm motion,
leg motion
Force Sensitive
Resistor raw
data of arm/leg
muscle strength
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
Data collection,
cleaning, and
analysis | Tools
& Methods
Application
Programming
Interfaces and
Software
Development Kit
Wireless
transmission
Cleaning
Pandas
Sci-kit learn
TensorFlow
and Keras
Matplotlib
and Seaborn
Analytical
Rigour
POTENTIAL CONFOUNDING
FACTORS, ESPECIALLY IN SKIN
CONDUCTANCE MEASUREMENTS
PROTOCOLS TO COUNTERACT
THE IMPACT OF SWEAT
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
Sample Size
Estimation
Optimal
Number of
Participants
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
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
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
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
Novelty: Alternative
therapies should
match the physical
intensity of DMT
New Knowledge:
UT2 training, akin to
light jogging, can
mirror DMT's
physiological benefits
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
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
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
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
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
References
and
Databases
References
Databases
Databases
Databases

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