Instrumentation & Measurement Magazine 24-6 - 27

Video
1
2
3
4
5
Table 3 - Seconds it took to process each video for the 4 PoseNet model configurations
C
A
147
145
159
147
145
B
220
221
231
216
216
539
492
508
488
493
D
931
925
930
893
886
A + SNN
151
149
163
152
149
Table 4 - R2
Video
1
2
3
4
5
A
0.152
0.269
0.157
0.159
0.461
Score comparison to Model D - ResNet50
B
0.225
0.312
0.347
0.367
0.442
C
-0.138
-0.260
-0.077
-0.179
-0.219
A + SNN
0.306
0.317
0.238
0.374
0.533
Improvement
over A
102%
18%
52%
135%
16%
Table 5 - R2
Video
1
2
3
4
5
Score comparison to Model D - ResNet50 using our approach for models B and C
B
0.225
0.312
0.347
0.367
0.442
B + SNN
0.355
0.339
0.316
0.452
0.483
Improvement
over B
58%
9%
-9%
23%
9%
insert bias into the SNN. In the next section, we will discuss
three main limitations that could affect the DTCoach system's
performance.
Limitations
◗ Our approach is similar to transfer learning; that is, we
depend on pre-trained networks' performance. Therefore,
we chose PoseNet, a robust pre-trained model
collection by Google.
◗ There is also the issue of data measurements. Since our
training data was measured in an uncontrolled environment
using our proof-of-concept app, we depend on how
the people use the system. Suppose people are intentionally
not following the instructions of the video coach. In
that case, if they are not in a well-lit room or are not standing
far enough for the camera to see the whole body, the
trained SNN can pick on this behavior and produce bad
results.
◗ Also, the performance improves as more data is collected,
so the model may not perform well on the first training
runs.
September 2021
C
-0.138
-0.260
-0.077
-0.179
-0.219
C + SNN
0.0195
-0.1043
-0.0047
0.0157
0.0109
Improvement
over C
114%
60%
94%
109%
105%
Conclusions and Future Work
We developed a solution to measure, monitor, and record
trainee physical exercises. For this, video measurements were
made using the trainee's camera feed, to derive performance
using a pose estimation approach.
We applied pose estimation on the edge to develop a digital
twin coaching system with success. For this, we proposed
using an SNN to learn from the trainee-coach exercises to
improve pose estimation. The idea behind our approach is
to take advantage of the Digital Twin Architecture to apply
context to our pose estimation models. We compared our
approach of using our own SNN in tandem with several PoseNet
models with successful results without compromising
the overall process's time complexity since we want to have
it as light and fast as possible. We also showed how applying
trainee-coach pose synchronization before our training
improves our model. We then propose a methodology that
takes Pose Net's pose estimation keypoints to generate an
APR of the joints. The similarity score between the coach and
the trainee is shown on screen so the latter can see their performance
in real-time.
IEEE Instrumentation & Measurement Magazine
27

Instrumentation & Measurement Magazine 24-6

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