Sumanth_M_N07 / MPU6500_IMU 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
Accident.6up99blr	Accident	10s	100%	10 Accident	Show classification Show in data acquisition Show in feature explorer
Accident.6up96iid	Accident	10s	90%	9 Accident, 1 uncertain	Show classification Show in data acquisition Show in feature explorer
Accident.6up8302c	Accident	10s	50%	5 Accident, 5 uncertain	Show classification Show in data acquisition Show in feature explorer
Accident.6up7toc1	Accident	10s	100%	10 Accident	Show classification Show in data acquisition Show in feature explorer
Accident.6up7q528	Accident	10s	100%	10 Accident	Show classification Show in data acquisition Show in feature explorer
Accident.6up7k5bq	Accident	10s	80%	8 Accident, 2 uncertain	Show classification Show in data acquisition Show in feature explorer
Up_and_Down.6up7j1f3	Up_and_Down	10s	100%	10 Up_and_Down	Show classification Show in data acquisition Show in feature explorer
Up_and_Down.6up7igf8	Up_and_Down	10s	100%	10 Up_and_Down	Show classification Show in data acquisition Show in feature explorer
Up_and_Down.6up7i0f0	Up_and_Down	10s	100%	10 Up_and_Down	Show classification Show in data acquisition Show in feature explorer
Right_and_Left.6up5qtct	Right_and_Left	10s	0%	10 uncertain	Show classification Show in data acquisition Show in feature explorer
Front_and_Back.6up5kk20	Front_and_Back	10s	100%	10 Front_and_Back	Show classification Show in data acquisition Show in feature explorer
Front_and_Back.6up5jnia	Front_and_Back	10s	100%	10 Front_and_Back	Show classification Show in data acquisition Show in feature explorer
Front_and_Back.6up5j5ok	Front_and_Back	10s	100%	10 Front_and_Back	Show classification Show in data acquisition Show in feature explorer
Idle.6up5ca28	Idle	10s	100%	10 Idle	Show classification Show in data acquisition Show in feature explorer
Idle.6up573bg	Idle	10s	100%	10 Idle	Show classification Show in data acquisition Show in feature explorer

Model testing output

Results

Accuracy

88.00%

Metrics for Classifier

Metric	Value
Area under ROC Curve	1.00
Weighted average Precision	0.98
Weighted average Recall	0.98
Weighted average F1 score	0.98

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

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	S Sumanth_M_N07

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Test data

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