Instrumentation & Measurement Magazine 24-6 - 26

Fig. 4. Illustrating the Weighted Coordinate Function.
used to process the coach
video routine. Model B and
C are slightly more robust
models used for testing
purposes to compare accuracy
and time performance
(Table 2). The objective of
having these two models
is to check if it is better to
have a heavier model versus
having our approach
using an SNN. The stride,
quantization bytes, and
layer size multiplier parameters
are explained in
https://github.com/tensorflow/tfjs-models/tree/
master/posenet.
For
the 5 videos used,
Table 3 shows the time in
rounded seconds it took
to process each one for different
configurations of
the PoseNet model. The
last column of this table
shows the time it took to
process using PoseNet plus
the trained SNN; 4 seconds
were added on average.
We can use the R2
score
Fig. 5. Points of comparison for " left wrist, " which are the parent joints: left elbow (left); and the origins: shoulders
middle point and hips middle point (right).
may be doing incorrectly. We also present a summary
graph of the trainee's score throughout the video with
timestamps. This graph helps them recognize challenging
exercises or routines.
Video Test Results
For validation purposes, we also asked five participants to record
their training session using our proof-of-concept app.
We trimmed each video to 300 seconds long. We then used
four different configurations of the PoseNet model to process
these five videos. Let us note model A as the one being
used by our proof-of-concept app and model D as the one
to test how similar are our
predictions of model A, B,
C compared to our baseline
model D - ResNet. In
Table 4, we can see how
using our approach improves the R2
score of model A in a fraction
of the time-adding the SNN results in the best score in 4
of the 5 cases.
In Table 5, we see our approach's results with models B
and C. Almost all results improved except for video 3 model
B. It could have happened for several reasons. For example,
the participant may have been too close to the camera or in
such a way that some joints were not visible, the room may
have been dark, making it difficult to perform accurate measurements
of the body, or the participant just might not have
been performing the routine correctly. All these situations may
Table 2 - PoseNet model configurations used
Label
A
B
C
D
26
Model
MobileNetV1
MobileNetV1
MobileNetV1
ResNet50
Stride
16
16
8
16
IEEE Instrumentation & Measurement Magazine
Quantization Bytes
2
4
4
1
Layer Size
Multiplier
0.75
1.00
1.00
-
September 2021
https://github.com/tensorflow/tfjs-models/tree/master/posenet https://github.com/tensorflow/tfjs-models/tree/master/posenet https://github.com/tensorflow/tfjs-models/tree/master/posenet

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