Luis Miguel Capacho / Gestos_Nano_ESP32 Public

This lists all test data. You can manage this data through Data acquisition.

Test data

Set the 'expected outcome' for each sample to the desired outcome to automatically score the impulse.

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Sample name	Expected outcome	Length	Accuracy	Result
testing.6kaejhta	testing	10s		43 vertical	Show classification Show in data acquisition Show in feature explorer
testing.6kaeiq3t	testing	10s		39 sacudir, 3 uncertain, 1 giro	Show classification Show in data acquisition Show in feature explorer
testing.6kaei4cu	testing	10s		43 giro	Show classification Show in data acquisition Show in feature explorer
testing.6kaehd6k	testing	10s		43 quieto	Show classification Show in data acquisition Show in feature explorer
testing.6kaeg9mm	testing	10s		42 sacudir, 1 uncertain	Show classification Show in data acquisition Show in feature explorer
sacudir.6kadivt0	sacudir	10s	100%	43 sacudir	Show classification Show in data acquisition Show in feature explorer
sacudir.6kadifi8	sacudir	10s	100%	43 sacudir	Show classification Show in data acquisition Show in feature explorer
vertical.6kadbs9r	vertical	10s	100%	43 vertical	Show classification Show in data acquisition Show in feature explorer
vertical.6kadba9e	vertical	10s	95%	41 vertical, 2 uncertain	Show classification Show in data acquisition Show in feature explorer
giro.6kada56p	giro	10s	91%	39 giro, 4 uncertain	Show classification Show in data acquisition Show in feature explorer
giro.6kad9j08	giro	10s	95%	41 giro, 2 uncertain	Show classification Show in data acquisition Show in feature explorer
quieto.6kad8gcn	quieto	10s	100%	43 quieto	Show classification Show in data acquisition Show in feature explorer
quieto.6kad7jvp	quieto	10s	100%	43 quieto	Show classification Show in data acquisition Show in feature explorer

Model testing output

Results

Accuracy

97.67%

Metrics for Classifier

Metric	Value
Area under ROC Curve	1.00
Weighted average Precision	1.00
Weighted average Recall	1.00
Weighted average F1 score	1.00

Confusion matrix

Feature explorer

Every learning block has a threshold. This can be the minimum confidence that a neural network needs to have, or the maximum anomaly score before a sample is tagged as an anomaly. You can configure these thresholds to tweak the sensitivity of these learning blocks. This affects both live classification and model testing.

Classifier

Minimum confidence rating

Should be numeric

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	Name
	LMC Luis Miguel Capacho

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Target device

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Test data

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