84 lines
4.9 KiB
Markdown
84 lines
4.9 KiB
Markdown
# Phone Accelerometer
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Sensor parameters description for `[PHONE_ACCELEROMETER]`:
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|Key | Description |
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|----------------|-----------------------------------------------------------------------------------------------------------------------------------
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|`[CONTAINER]`| Data stream [container](../../datastreams/data-streams-introduction/) (database table, CSV file, etc.) where the accelerometer data is stored
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## RAPIDS provider
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!!! info "Available time segments and platforms"
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- Available for all time segments
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- Available for Android and iOS
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!!! info "File Sequence"
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```bash
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- data/raw/{pid}/phone_accelerometer_raw.csv
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- data/raw/{pid}/phone_accelerometer_with_datetime.csv
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- data/interim/{pid}/phone_accelerometer_features/phone_accelerometer_{language}_{provider_key}.csv
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- data/processed/features/{pid}/phone_accelerometer.csv
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```
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Parameters description for `[PHONE_ACCELEROMETER][PROVIDERS][RAPIDS]`:
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|Key | Description |
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|----------------|-----------------------------------------------------------------------------------------------------------------------------------
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|`[COMPUTE]`| Set to `True` to extract `PHONE_ACCELEROMETER` features from the `RAPIDS` provider|
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|`[FEATURES]` | Features to be computed, see table below
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Features description for `[PHONE_ACCELEROMETER][PROVIDERS][RAPIDS]`:
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|Feature |Units |Description|
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|-------------------------- |---------- |---------------------------|
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|maxmagnitude |m/s^2^ |The maximum magnitude of acceleration ($\|acceleration\| = \sqrt{x^2 + y^2 + z^2}$).
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|minmagnitude |m/s^2^ |The minimum magnitude of acceleration.
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|avgmagnitude |m/s^2^ |The average magnitude of acceleration.
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|medianmagnitude |m/s^2^ |The median magnitude of acceleration.
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|stdmagnitude |m/s^2^ |The standard deviation of acceleration.
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!!! note "Assumptions/Observations"
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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.
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## PANDA provider
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These features are based on the work by [Panda et al](../../citation#panda-accelerometer).
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!!! info "Available time segments and platforms"
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- Available for all time segments
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- Available for Android and iOS
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!!! info "File Sequence"
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```bash
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- data/raw/{pid}/phone_accelerometer_raw.csv
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- data/raw/{pid}/phone_accelerometer_with_datetime.csv
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- data/interim/{pid}/phone_accelerometer_features/phone_accelerometer_{language}_{provider_key}.csv
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- data/processed/features/{pid}/phone_accelerometer.csv
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```
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Parameters description for `[PHONE_ACCELEROMETER][PROVIDERS][PANDA]`:
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|Key | Description |
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|----------------|-----------------------------------------------------------------------------------------------------------------------------------
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|`[COMPUTE]`| Set to `True` to extract `PHONE_ACCELEROMETER` features from the `PANDA` provider|
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|`[FEATURES]` | Features to be computed for exertional and non-exertional activity episodes, see table below
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Features description for `[PHONE_ACCELEROMETER][PROVIDERS][PANDA]`:
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|Feature |Units |Description|
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|-------------------------- |---------- |---------------------------|
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| sumduration | minutes | Total duration of all exertional or non-exertional activity episodes. |
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| maxduration | minutes | Longest duration of any exertional or non-exertional activity episode. |
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| minduration | minutes | Shortest duration of any exertional or non-exertional activity episode. |
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| avgduration | minutes | Average duration of any exertional or non-exertional activity episode. |
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| medianduration | minutes | Median duration of any exertional or non-exertional activity episode. |
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| stdduration | minutes | Standard deviation of the duration of all exertional or non-exertional activity episodes. |
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!!! note "Assumptions/Observations"
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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.
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2. See [Panda et al](../../citation#panda-accelerometer) for a definition of exertional and non-exertional activity episodes
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