Instrumentation & Measurement Magazine 24-6 - 22

DTCoach: Your Digital Twin
Coach on the Edge During
COVID-19 and Beyond
Rogelio Gámez Díaz, Fedwa Laamarti and Abdulmotaleb El Saddik
A
Digital Twin (DT) is a digital replica of a living
or non-living entity, called " real twin. " Data is
collected from the real twin and analyzed using Artificial
Intelligence (AI), which subsequently provides the real
twin with valuable feedback. One of the most promising applications
for humans is the DT for health and well-being [1].
Although the DT technology has been widely adopted in industries
such as the manufacturing industry, where it has
proven highly beneficial, its use in the domain of health is in
its infancy. Few researchers have addressed the DT for health.
Such is the work in [2] where a DT is proposed for heart disease
detection, or in [3] that presents an ecosystem of the DT in the
domain of well-being where the real twin's physical activity is
measured by the digital twin, which then provides feedback in
real-time to the real twin.
Since the end of 2019, the entire world has been having a
fierce battle with COVID-19. It has led many countries to apply
a lockdown, a situation in which people can only go out
to get necessities such as groceries. Sedentarism has become
the norm for many people in many countries across the globe.
An always-ready smart coach on the smartphone would be a
suitable method to staying active while in quarantine. The reason
is that during this period, professional coaches cannot be
physically present and coaching videos found on the internet
are not nearly as good as having a coach that people can interact
with.
A traditional smart coach solution would often involve
many measurements using complex sensors. These sensors
include RGB-D (depth) camera sets to measure subjects'
posture, physiological sensors to measure the trainees body
movements [4], etc. This type of instrumentation needs supplementary
material, such as high-end processors, and may
not be accessible to the average user. Consequently, we propose
the use of camera feeds as the sources for measurement
on which we base the pose estimation using machine learning
algorithms.
The ubiquity of smart devices such as smartphones and the
current advances in AI in terms of pose estimation has made
possible the existence of Smart Coaches on the edge. Born from
22
the rise of e-learning [5] as a competent learning platform, a
digital twin coach not only serves as an accompanying educator,
but as a mentor who can measure our performance over
time and make the necessary adjustments based on our capabilities
as unique individuals. Indeed, the digital twin for health
and well-being [6] is a technology that offers a person-centered
approach. For this reason, we propose a customizable digital
twin coach for physical activities focused on Edge Computing
(EC). This system is based on performance measurement.
It optimizes Machine Learning (ML) algorithms such as Deep
Learning (DL) for pose estimation while efficiently running on
small and portable devices such as smartphones.
We should highlight that the proposed system is not
trained on a specific physical exercise. Instead, it allows the
users to choose their training regime. We also trained a Shallow
Neural Network (SNN) to learn from the context of the
Trainee-Coach routine used to fix problematic keypoint coordinates
through post-processing.
Related Work
The research in [4] provided several guidelines for Instrumentation
and Measurement research in medical, biomedical,
and healthcare papers. In the field of sports, this principle is
well applied to the research done in [7]. This paper proposed
a lightweight approach to analyze sport videos to detect the
number of athletes in frame. Combining edge and color features,
they identified the location and number of athletes in
the video.
In 2014, a team at Google published DeepPose [8] architecture
that used a 7-layer Deep Neural Network (DNN) to
predict human pose in two dimensions. In [9], researchers proposed
a Convolutional Neural Network (CNN) architecture
called stacked hourglass, for its shape of hourglasses on top of
each other. There is also the work of [10], in which the authors
tried to solve the problem of real-time multi-person pose estimation
using a two stage CNN architecture. In the first stage,
they found confidence maps, while in the second, they predicted
Part Affinity Fields (PAF), which help associate body
parts to possible candidates.
IEEE Instrumentation & Measurement Magazine
1094-6969/21/$25.00©2021IEEE
September 2021

Instrumentation & Measurement Magazine 24-6

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