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# Notes on 1st project
The given data at [iui-datalrelease1-sose2021-readonly/\* ](/opt/iui-datarelease1-sose2021 ) represents sensor data from a pen.
| Data | MinVal | MaxVal | Description |
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| ------ |:------:|:------:| ----------------------------------------------- |
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| 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 |
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| Time | - | - | Time from start of "recording" |
There were 100 participants.
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The folder-structure is as follows:\
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`/opt/iui-datarelease1-sose2021/{P}/{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 |
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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
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### 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.
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### 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
todo
### Acc2
todo
### Gyro
todo
### 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
- Time is negative for some data, gotta find out why
## Model selection
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todo