8.2 KiB
8.2 KiB
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