iui-group-l-name-zensiert/1-first-project/tdt

Notes on 1st project

The given data at iui-datalrelease1-sose2021-readonly/* represents sensor data from a pen.

Data	MinVal	MaxVal	Description
Millis	-	-	Timestamp from tablet (Unix time)
Acc1 X	-	32768	Front/Tip accelerometer (Direction: Left/Right)
Acc1 Y	-	32768	Front/Tip accelerometer (Direction: Up/Down)
Acc1 Z	-	32768	Front/Tip accelerometer (Direction: Back/Front)
Acc2 X	-	8192	Back accelerometer (Direction: Left/Right)
Acc2 Y	-	8192	Back accelerometer (Direction: Up/Down)
Acc2 Z	-	8192	Back accelerometer (Direction: Back/Front)
Gyro X	-	32768	Gyroscope sensor
Gyro Y	-	32768	Gyroscope sensor
Gyro Z	-	32768	Gyroscope sensor
Mag X	-	8192	Magnetometer
Mag Y	-	8192	Magnetometer
Mag Z	-	8192	Magnetometer
Force	-	4096	Force applied
Time	-	-	Time from start of "recording"

There were 100 participants.

The folder-structure is as follows:
/opt/iui-datarelease1-sose2021/{P}/split_letters_csv/{N}{A}.csv

Variable	Description
P	The ID of the participant
N	The N-th letter the participant wrote
A	The letter that was written

Each participants folder contains a calibration.txt, which contains the calibration data of the pen for the participant.

Sensor data was recorded at 100hz (100 recordings/s => 1 recording/ms).

Preprocessing

General

Since information has different scale (i.e. Acc1: [-32768;32768] and Acc2 [-8192;8192]) the information has to be valued differently based on their importance.

Millis

• Could be used for identifying each data entry -> needs to be normalized to the first entry of the data set to see the comlete timeline of the data

Acc1/Acc2/Gyro/Mag

todo

Force

• Sometimes sensor data was recorded even when there is no action -> we need to determine the area of interest

  • maybe sliding window, where window avg has to be certain threshold
  • general threshold aproach (filter out data below threshold)
  • more ideas welcome

• Data could be normalized by each users relative strength or data entry

Time

Neural Network

This segment are notes dedicated to the neural network itself.

Ideas

• Don't use batch normalization but normalize by maxval of sensor

Results

Test1 (72.99%)

thresh      = 100
leeway      = 5
epoch       = 20

model = Sequential()

model.add(Flatten(input_shape=X_filter[0].shape))

model.add(BatchNormalization())

model.add(Dense(1560, activation='relu'))

model.add(Dense(750, activation='relu'))

model.add(Dense(300, activation='relu'))

model.add(Dense(156, activation='relu'))

model.add(Dense(26, activation='softmax'))

Evaluate on test data
82/82 [==============================] - 0s 3ms/step - loss: 1.4249 - acc: 0.7299

Test2 (75.21%)

thresh      = 75
leeway      = 3
epoch       = 20

model = Sequential()

model.add(Flatten(input_shape=X_filter[0].shape))

model.add(BatchNormalization())

model.add(Dense(1560, activation='relu'))

model.add(Dense(750, activation='relu'))

model.add(Dense(300, activation='relu'))

model.add(Dense(156, activation='relu'))

model.add(Dense(26, activation='softmax'))

Evaluate on test data
82/82 [==============================] - 0s 2ms/step - loss: 1.5145 - acc: 0.7521

Test3 (36.96%)

thresh      = 10
leeway      = 3
epoch       = 20

model = Sequential()

model.add(Flatten(input_shape=X_filter[0].shape))

model.add(BatchNormalization())

model.add(Dense(1560, activation='relu'))

model.add(Dense(750, activation='relu'))

model.add(Dense(300, activation='relu'))

model.add(Dense(156, activation='relu'))

model.add(Dense(26, activation='softmax'))

Evaluate on test data
82/82 [==============================] - 0s 2ms/step - loss: 2.5231 - acc: 0.3696

Test4 (69.66%)

thresh      = 50
leeway      = 3
epoch       = 20

model = Sequential()

model.add(Flatten(input_shape=X_filter[0].shape))

model.add(BatchNormalization())

model.add(Dense(1560, activation='relu'))

model.add(Dense(750, activation='relu'))

model.add(Dense(300, activation='relu'))

model.add(Dense(156, activation='relu'))

model.add(Dense(26, activation='softmax'))

Evaluate on test data
82/82 [==============================] - 0s 2ms/step - loss: 1.6965 - acc: 0.6966

Test5 (73.13%)

thresh      = 60
leeway      = 3
epoch       = 20

model = Sequential()

model.add(Flatten(input_shape=X_filter[0].shape))

model.add(BatchNormalization())

model.add(Dense(1560, activation='relu'))

model.add(Dense(750, activation='relu'))

model.add(Dense(300, activation='relu'))

model.add(Dense(156, activation='relu'))

model.add(Dense(26, activation='softmax'))

Evaluate on test data
82/82 [==============================] - 0s 2ms/step - loss: 1.4886 - acc: 0.7313

Test6 (75.68%)

thresh      = 68
leeway      = 3
epoch       = 20

model = Sequential()

model.add(Flatten(input_shape=X_filter[0].shape))

model.add(BatchNormalization())

model.add(Dense(1560, activation='relu'))

model.add(Dense(750, activation='relu'))

model.add(Dense(300, activation='relu'))

model.add(Dense(156, activation='relu'))

model.add(Dense(26, activation='softmax'))

Evaluate on test data
82/82 [==============================] - 0s 2ms/step - loss: 1.4227 - acc: 0.7568

Test7 (76.07%)

thresh      = 68
leeway      = 3
epoch       = 30

model = Sequential()

model.add(Flatten(input_shape=X_filter[0].shape))

model.add(BatchNormalization())

model.add(Dense(1560, activation='relu'))

model.add(Dense(750, activation='relu'))

model.add(Dense(300, activation='relu'))

model.add(Dense(156, activation='relu'))

model.add(Dense(26, activation='softmax'))

Evaluate on test data
82/82 [==============================] - 0s 2ms/step - loss: 1.5863 - acc: 0.7607

Test8 (75.49%)

THRESH = 70
LEEWAY = 3
EPOCH = 30

DENSE_COUNT = 5
DENSE_NEURONS = 1800

Evaluate on test data
21/21 [==============================] - 0s 2ms/step - loss: 1.5598 - acc: 0.7684

Test9 (78.15%)

THRESH = 70
LEEWAY = 1
EPOCH = 30

DENSE_COUNT = 5
DENSE_NEURONS = 1800

Evaluate on test data
21/21 [==============================] - 0s 2ms/step - loss: 1.4677 - acc: 0.7815

Test10 (77.64%)

THRESH = 70
LEEWAY = 0
EPOCH = 30

DENSE_COUNT = 5
DENSE_NEURONS = 1900

Test11 (77.41%)

THRESH = 70
LEEWAY = 0
EPOCH = 30

DENSE_COUNT = 5
DENSE_NEURONS = 1800

Test12 (73.90%)

THRESH = 70
LEEWAY = 0
EPOCH = 30

DENSE_COUNT = 5
DENSE_NEURONS = 1800

DENSE_COUNT = 5
DENSE_NEURONS = 900

Test13 (78.89%)

NORM->FLAT->DENSE

THRESH = 70
LEEWAY = 0
EPOCH = 30

DENSE_COUNT = 5
DENSE_NEURONS = 1900

Test14 (79.04%)

NORM->FLAT->DENSE

THRESH = 70
LEEWAY = 0
EPOCH = 30

DENSE_COUNT = 5
DENSE_NEURONS = 1800

Test15 (79.00%)

NORM->FLAT->DENSE

THRESH = 70
LEEWAY = 0
EPOCH = 30

DENSE_COUNT = 5
DENSE_NEURONS = 1700

Test16 (78.69%)

NORM->FLAT->DENSE

THRESH = 70
LEEWAY = 0
EPOCH = 30

DENSE_COUNT = 5
DENSE_NEURONS = 1600

Test17 (78.57%)

NORM->FLAT->DENSE

THRESH = 70
LEEWAY = 0
EPOCH = 30

DENSE_COUNT = 4
DENSE_NEURONS = 1800

Test18 (78.12%)

NORM->FLAT->DENSE

THRESH = 70
LEEWAY = 0
EPOCH = 30

DENSE_COUNT = 6
DENSE_NEURONS = 1800

Test19 (79.13%)

NORM->FLAT->DENSE

THRESH = 70
LEEWAY = 0
EPOCH = 30

DENSE_COUNT = 3
DENSE_NEURONS = 1800

DENSE2_COUNT = 2
DENSE2_NEURONS = 1200