Added some code to visualize 'Force' data (could be tweaked to visualize other data)
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# 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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|----------------------------------------------------------------------------|
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| Data | MinVal | MaxVal | Description |
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| Millis | - | - | Timestamp from tablet |
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| Acc1 X | - | - | Front/Tip accelerometer (Direction: Left/Right) |
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| Acc1 Y | - | - | Front/Tip accelerometer (Direction: Up/Down) |
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| Acc1 Z | - | - | Front/Tip accelerometer (Direction: Back/Front) |
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| Acc2 X | - | - | Back accelerometer (Direction: Left/Right) |
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| Acc2 Y | - | - | Back accelerometer (Direction: Up/Down) |
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| Acc1 Z | - | - | Back accelerometer (Direction: Back/Front) |
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| Gyro X | - | - | Gyroscope sensor |
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| Gyro Y | - | - | Gyroscope sensor |
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| Gyro Z | - | - | Gyroscope sensor |
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| Mag X | - | - | Magnetometer |
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| Mag Y | - | - | Magnetometer |
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| Mag Z | - | - | Magnetometer |
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| Force | - | - | Force applied |
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| Time | - | - | Time from start of "recording" |
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|----------------------------------------------------------------------------|
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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}/{N}{A}.csv`
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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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### 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
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todo
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### Acc2
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todo
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### Gyro
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todo
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### 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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- Time is negative for some data, gotta find out why
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## Model selection
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todo
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