185 lines
4.9 KiB
Markdown
185 lines
4.9 KiB
Markdown
# Notes on 1st project
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The given data at [iui-datalrelease1-sose2021-readonly/\*](/opt/iui-datarelease1-sose2021) represents sensor data from a pen.
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| Data | MinVal | MaxVal | Description |
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| ------ |:------:|:------:| ----------------------------------------------- |
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| Millis | - | - | Timestamp from tablet (Unix time) |
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| Acc1 X | - | 32768 | Front/Tip accelerometer (Direction: Left/Right) |
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| Acc1 Y | - | 32768 | Front/Tip accelerometer (Direction: Up/Down) |
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| Acc1 Z | - | 32768 | Front/Tip accelerometer (Direction: Back/Front) |
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| Acc2 X | - | 8192 | Back accelerometer (Direction: Left/Right) |
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| Acc2 Y | - | 8192 | Back accelerometer (Direction: Up/Down) |
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| Acc2 Z | - | 8192 | Back accelerometer (Direction: Back/Front) |
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| Gyro X | - | 32768 | Gyroscope sensor |
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| Gyro Y | - | 32768 | Gyroscope sensor |
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| Gyro Z | - | 32768 | Gyroscope sensor |
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| Mag X | - | 8192 | Magnetometer |
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| Mag Y | - | 8192 | Magnetometer |
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| Mag Z | - | 8192 | Magnetometer |
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| Force | - | 4096 | Force applied |
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| Time | - | - | Time from start of "recording" |
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There were 100 participants.
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The folder-structure is as follows:\
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`/opt/iui-datarelease1-sose2021/{P}/split_letters_csv/{N}{A}.csv`
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| Variable | Description |
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| -------- | ------------------------------------- |
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| P | The ID of the participant |
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| N | The N-th letter the participant wrote |
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| A | The letter that was written |
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Each participants folder contains a `calibration.txt`, which contains the calibration data of the pen for the participant.
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Sensor data was recorded at 100hz (100 recordings/s => 1 recording/ms).
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## Preprocessing
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### General
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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.
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### Millis
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- 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
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### Acc1/Acc2/Gyro/Mag
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todo
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### Force
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- Sometimes sensor data was recorded even when there is no action -> we need to determine the area of interest
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- maybe sliding window, where window avg has to be certain threshold
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- general threshold aproach (filter out data below threshold)
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- more ideas welcome
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- Data could be normalized by each users relative strength or data entry
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### Time
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## Neural Network
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This segment are notes dedicated to the neural network itself.
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### Ideas
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- Don't use batch normalization but normalize by maxval of sensor
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### Results
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#### Test1 (72.99%)
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```python
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thresh = 100
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leeway = 5
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model = Sequential()
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model.add(Flatten(input_shape=X_filter[0].shape))
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model.add(BatchNormalization())
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model.add(Dense(1560, activation='relu'))
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model.add(Dense(750, activation='relu'))
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model.add(Dense(300, activation='relu'))
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model.add(Dense(156, activation='relu'))
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model.add(Dense(26, activation='softmax'))
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```
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```
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Evaluate on test data
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82/82 [==============================] - 0s 3ms/step - loss: 1.4249 - acc: 0.7299
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```
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#### Test2 (75.21%)
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```python
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thresh = 75
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leeway = 3
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model = Sequential()
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model.add(Flatten(input_shape=X_filter[0].shape))
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model.add(BatchNormalization())
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model.add(Dense(1560, activation='relu'))
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model.add(Dense(750, activation='relu'))
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model.add(Dense(300, activation='relu'))
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model.add(Dense(156, activation='relu'))
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model.add(Dense(26, activation='softmax'))
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```
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```
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Evaluate on test data
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82/82 [==============================] - 0s 2ms/step - loss: 1.5145 - acc: 0.7521
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```
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#### Test3 (36.96%)
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```python
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thresh = 10
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leeway = 3
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model = Sequential()
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model.add(Flatten(input_shape=X_filter[0].shape))
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model.add(BatchNormalization())
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model.add(Dense(1560, activation='relu'))
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model.add(Dense(750, activation='relu'))
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model.add(Dense(300, activation='relu'))
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model.add(Dense(156, activation='relu'))
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model.add(Dense(26, activation='softmax'))
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```
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```
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Evaluate on test data
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82/82 [==============================] - 0s 2ms/step - loss: 2.5231 - acc: 0.3696
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```
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#### Test4 (71.51%)
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```python
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thresh = 50
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leeway = 3
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model = Sequential()
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model.add(Flatten(input_shape=X_filter[0].shape))
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model.add(BatchNormalization())
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model.add(Dense(1560, activation='relu'))
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model.add(Dense(750, activation='relu'))
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model.add(Dense(300, activation='relu'))
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model.add(Dense(156, activation='relu'))
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model.add(Dense(26, activation='softmax'))
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```
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```
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Evaluate on test data
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82/82 [==============================] - 0s 2ms/step - loss: 1.5005 - acc: 0.7151
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```
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#### Test5 ()
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