Rappy / Accelerometer_prj1 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
left-Right.29a7pdd0	left-Right	10s	Show classification Show in data acquisition Show in feature explorer
up-down.29acld3r	up-down	10s	Show classification Show in data acquisition Show in feature explorer
left-Right.29a7v3h9	left-Right	10s	Show classification Show in data acquisition Show in feature explorer
up-down.29acr5n1	up-down	10s	Show classification Show in data acquisition Show in feature explorer
idle.29af3ss9	idle	10s	Show classification Show in data acquisition Show in feature explorer
testing.29be2pco	testing	10s	Show classification Show in data acquisition Show in feature explorer
circle.29afj00u	circle	10s	Show classification Show in data acquisition Show in feature explorer
circle.29afl121	circle	10s	Show classification Show in data acquisition Show in feature explorer
idle.29af4m5j	idle	10s	Show classification Show in data acquisition Show in feature explorer
testing.29be5qc5	testing	2s	Show classification Show in data acquisition Show in feature explorer
testing.29be6krm	testing	2s	Show classification Show in data acquisition Show in feature explorer
testing.29be67iq	testing	2s	Show classification Show in data acquisition Show in feature explorer
testing.29be4q77	testing	2s	Show classification Show in data acquisition Show in feature explorer
testing.29be59ph	testing	2s	Show classification Show in data acquisition Show in feature explorer

Model testing output

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.

NN Classifier

Minimum confidence rating

Should be numeric

Anomaly detection

Minimum score before tagging as anomaly

Should be numeric

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	R Rappy

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Target device

Define your target device requirements to inform model optimizations and performance calculations. No device yet? Use the default settings which you can change at any time.

Target device

Processor family

Clock rate

| MHz

Max

Custom device name

Application budget

Specify the available RAM and ROM for the model's operation, along with the maximum allowed latency for your specific application. Not sure yet? Start with the defaults and modify them later on.

RAM

| KB

Max

ROM

| KB

Max

Latency

| ms

Max

YEARLY

$400 /month

billed annually

SAVE 15%

MONTHLY

$475 /month

billed monthly

Test data

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Model testing output

Results

Accuracy

Confusion matrix

Feature explorer

Test data

Model variants

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Sample length (ms)

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Model testing output

Results

Accuracy

Confusion matrix

Feature explorer

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