rapids/docs/features/phone-accelerometer.md

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# Phone Accelerometer
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Sensor parameters description for `[PHONE_ACCELEROMETER]`:
|Key                              | Description |
|----------------|-----------------------------------------------------------------------------------------------------------------------------------
|`[TABLE]`| Database table where the accelerometer data is stored
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## RAPIDS provider
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!!! info "Available time segments and platforms"
- Available for all time segments
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- Available for Android and iOS
!!! info "File Sequence"
```bash
- data/raw/{pid}/phone_accelerometer_raw.csv
- data/raw/{pid}/phone_accelerometer_with_datetime.csv
- data/interim/{pid}/phone_accelerometer_features/phone_accelerometer_{language}_{provider_key}.csv
- data/processed/features/{pid}/phone_accelerometer.csv
```
Parameters description for `[PHONE_ACCELEROMETER][PROVIDERS][RAPIDS]`:
|Key                              | Description |
|----------------|-----------------------------------------------------------------------------------------------------------------------------------
|`[COMPUTE]`| Set to `True` to extract `PHONE_ACCELEROMETER` features from the `RAPIDS` provider|
|`[FEATURES]` | Features to be computed, see table below
Features description for `[PHONE_ACCELEROMETER][PROVIDERS][RAPIDS]`:
|Feature |Units |Description|
|-------------------------- |---------- |---------------------------|
|maxmagnitude |m/s^2^ |The maximum magnitude of acceleration ($\|acceleration\| = \sqrt{x^2 + y^2 + z^2}$).
|minmagnitude |m/s^2^ |The minimum magnitude of acceleration.
|avgmagnitude |m/s^2^ |The average magnitude of acceleration.
|medianmagnitude |m/s^2^ |The median magnitude of acceleration.
|stdmagnitude |m/s^2^ |The standard deviation of acceleration.
!!! note "Assumptions/Observations"
1. Analyzing accelerometer data is a memory intensive task. If RAPIDS crashes is likely because the accelerometer dataset for a participant is to big to fit in memory. We are considering different alternatives to overcome this problem.
## PANDA provider
These features are based on the work by [Panda et al](../../citation#panda-accelerometer).
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2020-12-03 00:41:03 +01:00
!!! info "Available time segments and platforms"
- Available for all time segments
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- Available for Android and iOS
!!! info "File Sequence"
```bash
- data/raw/{pid}/phone_accelerometer_raw.csv
- data/raw/{pid}/phone_accelerometer_with_datetime.csv
- data/interim/{pid}/phone_accelerometer_features/phone_accelerometer_{language}_{provider_key}.csv
- data/processed/features/{pid}/phone_accelerometer.csv
```
Parameters description for `[PHONE_ACCELEROMETER][PROVIDERS][PANDA]`:
|Key                              | Description |
|----------------|-----------------------------------------------------------------------------------------------------------------------------------
|`[COMPUTE]`| Set to `True` to extract `PHONE_ACCELEROMETER` features from the `PANDA` provider|
|`[FEATURES]` | Features to be computed for exertional and non-exertional activity episodes, see table below
Features description for `[PHONE_ACCELEROMETER][PROVIDERS][PANDA]`:
|Feature |Units |Description|
|-------------------------- |---------- |---------------------------|
| sumduration | minutes | Total duration of all exertional or non-exertional activity episodes. |
| maxduration | minutes | Longest duration of any exertional or non-exertional activity episode. |
| minduration | minutes | Shortest duration of any exertional or non-exertional activity episode. |
| avgduration | minutes | Average duration of any exertional or non-exertional activity episode. |
| medianduration | minutes | Median duration of any exertional or non-exertional activity episode. |
| stdduration | minutes | Standard deviation of the duration of all exertional or non-exertional activity episodes. |
!!! note "Assumptions/Observations"
1. Analyzing accelerometer data is a memory intensive task. If RAPIDS crashes is likely because the accelerometer dataset for a participant is to big to fit in memory. We are considering different alternatives to overcome this problem.
2. See [Panda et al](../../citation#panda-accelerometer) for a definition of exertional and non-exertional activity episodes