Phone Accelerometer¶
Sensor parameters description for [PHONE_ACCELEROMETER]:
| Key | Description |
|---|---|
[CONTAINER] |
Data stream container (database table, CSV file, etc.) where the accelerometer data is stored |
RAPIDS provider¶
Available time segments and platforms
- Available for all time segments
- Available for Android and iOS
File Sequence
- 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/s2 | The maximum magnitude of acceleration (\(\|acceleration\| = \sqrt{x^2 + y^2 + z^2}\)). |
| minmagnitude | m/s2 | The minimum magnitude of acceleration. |
| avgmagnitude | m/s2 | The average magnitude of acceleration. |
| medianmagnitude | m/s2 | The median magnitude of acceleration. |
| stdmagnitude | m/s2 | The standard deviation of acceleration. |
Assumptions/Observations
- 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.
Available time segments and platforms
- Available for all time segments
- Available for Android and iOS
File Sequence
- 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. |
Assumptions/Observations
- 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.
- See Panda et al for a definition of exertional and non-exertional activity episodes