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Building an intelligent Pullup-Classifier - Iterations, Learnings, Thoughts

OMM TECHtalks #1 www.tech-talks.eu

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OMM Solutions
TECHtalk #1
www.omm-solutions.de
30.03.2017 < OMM Solutions GmbH > 1
www.tech-talks.eu
Einmal im Monat ist TECHtalk Zeit! First come first served!
< OMM Solutions GmbH > 2
Talk: Building an intelligent Pullup-Classifier
Speaker: Olaf Horstmann
3< OMM Solutions GmbH >
30.03.2017 < OMM Solutions GmbH > 4
• should COUNT the pullups
• should detect WHO
• should classify the QUALITY
The Goal
30.03.2017 < OMM Solutions GmbH > 5
Image from: http://www.instructables.com/id/Pull-Up-Counter/
Existing solutions
30.03.2017 < OMM Solutions GmbH > 6
30.03.2017 < OMM Solutions GmbH > 7
✓ COUNT
× WHO
× QUALITY
Can we use it?
30.03.2017 < OMM Solutions GmbH > 8
The Pressure-Sensor
Iteration #1
30.03.2017 < OMM Solutions GmbH > 9
Let‘s try it!
30.03.2017 < OMM Solutions GmbH > 10
OKAY…
30.03.2017 < OMM Solutions GmbH > 11
The Ultrasonic-Sensor
Iteration #2
30.03.2017 < OMM Solutions GmbH > 12
30.03.2017 < OMM Solutions GmbH > 13
30.03.2017 < OMM Solutions GmbH > 14
• Only works on perpendicular angles (+/-15°)
• Only works on „flat“ surfaces (hair or clothes = bad)
• You can hear it when it is quiet
BUT…
30.03.2017 < OMM Solutions GmbH > 15
An optical sensor
Iteration #3
30.03.2017 < OMM Solutions GmbH > 16
30.03.2017 < OMM Solutions GmbH > 17
30.03.2017 < OMM Solutions GmbH > 18
Success
30.03.2017 < OMM Solutions GmbH > 19
• Cost more than the other sensors
• Inaccurate/slow > 2m
• Might not work very well in direct sunlight (did not test)
Any Drawbacks?
30.03.2017 < OMM Solutions GmbH > 20
• Collected data from different people
• Prepate the data
• Implement an AI to classify the data
Now what?
30.03.2017 < OMM Solutions GmbH > 21
Collecting/Comparing Data
30.03.2017 < OMM Solutions GmbH > 22
• Algorithm to detect individual pullups (=> count)
• Label each data-set manually (=> who & quality)
Data-Preparation
30.03.2017 < OMM Solutions GmbH > 23
Data-Preparation
30.03.2017 < OMM Solutions GmbH > 24
• Throw in the raw data (~200 pullups)
• Train it
• See what happens
• => accuracy ~60%
With Tensorflow
AI Iteration #1
30.03.2017 < OMM Solutions GmbH > 25
• More data (a lot more!)
• Adjust the network (not really better)
• Use meta-data instead of raw data
How can we improve this?
30.03.2017 < OMM Solutions GmbH > 26
• Using ~15 different meta-data-points
• => accuracy ~85%
AI Iteration #2
30.03.2017 < OMM Solutions GmbH > 27
• Normalize data (high values are bad!)
• …and more data!
…but there‘s still room for improvements
It‘s Okay…
30.03.2017 < OMM Solutions GmbH > 28
• Normalized data
• => accuracy at +90%
AI Iteration #3
30.03.2017 < OMM Solutions GmbH > 29
„The Product“
30.03.2017 < OMM Solutions GmbH > 30
• Data preparation is probably the most important step
• Visualizing the data helps analyzing it
• Less data can be compensated by transforming the data first
• Having more classes can help with the accuracy of the whole model
• More data is always better
Learnings
30.03.2017 < OMM Solutions GmbH > 31
Vielen Dank für Eure Aufmerksamkeit!
32< OMM Solutions GmbH >
Ihr persönlicher Ansprechpartner
Fragen oder Interesse?
< OMM Solutions GmbH > 33
Olaf Horstmann
Technology & Innovation
OMM Solutions GmbH
Vor dem Lauch 4
70567 Stuttgart
Germany
oh@omm-solutions.de
+49 (0)711 75 86 46 04
34< OMM Solutions GmbH >
www.omm-solutions.de
OMM Solutions GmbH
Vor dem Lauch 4
70567 Stuttgart
Geschäftsführer
Martin Allmendinger
Malte Horstmann
Olaf Horstmann
Kontakt
Telefon: +49 711 6747 051-0
E-Mail: info@omm-solutions.de
Umsatzsteuer-ID: DE295716572
Sitz der Gesellschaft: Stuttgart
Amtsgericht Stuttgart, HRB 749562
Impressum

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Building an intelligent Pullup-Classifier - Iterations, Learnings, Thoughts