History

Tuan-Dat Tran f06f61cd04 started implementing sliding window		2021-07-14 10:15:52 +02:00
..
best_model7904	…
DataVisualization.ipynb	…
Hyperparameter.ipynb	Added really bad normalization	2021-06-10 17:18:36 +00:00
NNwAll.ipynb	edited nn for project 2, data visualization for project 3	2021-07-05 15:01:40 +02:00
NeuralNetwork.ipynb	started implementing sliding window	2021-07-14 10:15:52 +02:00
README.md	…
T_DataNormaization.ipynb	Added presentation/final version of project 1	2021-06-23 08:12:11 +00:00
all_forces.png	…
results.csv	…
t_Data_Norm_wth_SW.ipynb	…

README.md

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