Kutluhan Aktar / AI-driven BLE Travel Emergency Assistant 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	F1 score
Call_Test_0	call	100%	Show classification Show in data acquisition Show in feature explorer
Danger_Test_2	danger	0%	Show classification Show in data acquisition Show in feature explorer
Call_Test_2	call	100%	Show classification Show in data acquisition Show in feature explorer
Danger_Test_1	danger	0%	Show classification Show in data acquisition Show in feature explorer
Stolen_Test_1	stolen	100%	Show classification Show in data acquisition Show in feature explorer
Danger_Test_0	danger	0%	Show classification Show in data acquisition Show in feature explorer
Stolen_Test_2	stolen	100%	Show classification Show in data acquisition Show in feature explorer
Assist_Test_2	assist	100%	Show classification Show in data acquisition Show in feature explorer
Assist_Test_1	assist	100%	Show classification Show in data acquisition Show in feature explorer
Assist_Test_0	assist	100%	Show classification Show in data acquisition Show in feature explorer
Fine_Test_0	fine	100%	Show classification Show in data acquisition Show in feature explorer
Call_Test_1	call	0%	Show classification Show in data acquisition Show in feature explorer
Stolen_Test_0	stolen	100%	Show classification Show in data acquisition Show in feature explorer
Fine_Test_1	fine	100%	Show classification Show in data acquisition Show in feature explorer
Fine_Test_2	fine	100%	Show classification Show in data acquisition Show in feature explorer

Model testing output

Results

Accuracy

73.33%

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.

Object detection

Score threshold

Should be numeric

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	Name
	Kutluhan Aktar

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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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Model testing output

Results

Accuracy

Confusion matrix

Feature explorer

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