Nekhil R / Fall detection system for workers in industry Public
Target: Raspberry Pi 4 Clone this project

Test data

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Model variants
Test 'Unoptimized (float32)' model Test 'Quantized (int8)' model
Set the 'expected outcome' for each sample to the desired outcome to automatically score the impulse.

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Sample name Expected outcome F1 score Result
test2 - 0%
Show classification Show in data acquisition Show in feature explorer
Test - 0%
Show classification Show in data acquisition Show in feature explorer
Sample Standing 100%
Show classification Show in data acquisition Show in feature explorer
Sample Standing 100%
Show classification Show in data acquisition Show in feature explorer
Sample Standing 100%
Show classification Show in data acquisition Show in feature explorer
Sample Standing 100%
Show classification Show in data acquisition Show in feature explorer
Sample Standing 0%
Show classification Show in data acquisition Show in feature explorer
Sample Standing 100%
Show classification Show in data acquisition Show in feature explorer
Sample Standing 100%
Show classification Show in data acquisition Show in feature explorer
Sample Standing 100%
Show classification Show in data acquisition Show in feature explorer
Sample Standing 100%
Show classification Show in data acquisition Show in feature explorer
Sample Fall 100%
Show classification Show in data acquisition Show in feature explorer
Sample Fall 100%
Show classification Show in data acquisition Show in feature explorer
Sample Fall 100%
Show classification Show in data acquisition Show in feature explorer
Sample Fall 100%
Show classification Show in data acquisition Show in feature explorer
Sample Fall 100%
Show classification Show in data acquisition Show in feature explorer
Sample Fall 100%
Show classification Show in data acquisition Show in feature explorer
Sample Fall 100%
Show classification Show in data acquisition Show in feature explorer
Sample Fall 66%
Show classification Show in data acquisition Show in feature explorer
Sample Fall 100%
Show classification Show in data acquisition Show in feature explorer
Sample Fall 100%
Show classification Show in data acquisition Show in feature explorer
Sample Fall 100%
Show classification Show in data acquisition Show in feature explorer
Sample Fall 100%
Show classification Show in data acquisition Show in feature explorer
Sample Fall 100%
Show classification Show in data acquisition Show in feature explorer
Sample Fall 100%
Show classification Show in data acquisition Show in feature explorer
Sample Fall 100%
Show classification Show in data acquisition Show in feature explorer
Sample Fall 100%
Show classification Show in data acquisition Show in feature explorer
Sample Fall 100%
Show classification Show in data acquisition Show in feature explorer
Sample Fall 100%
Show classification Show in data acquisition Show in feature explorer
Sample Fall 100%
Show classification Show in data acquisition Show in feature explorer
Sample Fall 100%
Show classification Show in data acquisition Show in feature explorer
Sample Fall 100%
Show classification Show in data acquisition Show in feature explorer
Sample Standing 100%
Show classification Show in data acquisition Show in feature explorer
Sample Standing 100%
Show classification Show in data acquisition Show in feature explorer
Sample Standing 100%
Show classification Show in data acquisition Show in feature explorer
Sample Standing 100%
Show classification Show in data acquisition Show in feature explorer
Sample Standing 100%
Show classification Show in data acquisition Show in feature explorer
Sample Standing 100%
Show classification Show in data acquisition Show in feature explorer
Sample Standing 100%
Show classification Show in data acquisition Show in feature explorer
Sample Standing 100%
Show classification Show in data acquisition Show in feature explorer
Sample Standing 100%
Show classification Show in data acquisition Show in feature explorer
Sample Standing 100%
Show classification Show in data acquisition Show in feature explorer
Sample Standing 100%
Show classification Show in data acquisition Show in feature explorer
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Model testing output

Results

Model version:
Accuracy
90.70%

Confusion matrix

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Feature explorer

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