| countscans | devices | Number of scanned devices during a `time_segment`, a device can be detected multiple times over time and these appearances are counted separately |
| uniquedevices | devices | Number of unique devices during a `time_segment` as identified by their hardware (`bt_address`) address |
| countscansmostuniquedevice | scans | Number of scans of the most scanned device during a `time_segment` across the whole monitoring period |
|`[COMPUTE]`| Set to `True` to extract `PHONE_BLUETOOTH` features from the `DORYAB` provider|
|`[FEATURES]` | Features to be computed, see table below. These features are computed for three device categories: `all` devices, `own` devices and `other` devices.
Features description for `[PHONE_BLUETOOTH][PROVIDERS][DORYAB]`:
| countscans | scans | Number of scans (rows) from the devices sensed during a time segment instance. The more scans a bluetooth device has the longer it remained within range of the participant's phone |
| uniquedevices | devices | Number of unique bluetooth devices sensed during a time segment instance as identified by their hardware addresses (`bt_address`) |
| countscansmostuniquedevice | scans | Number of scans of the most sensed device within each time segment instance|
| countscansleastuniquedevice| scans| Number of scans of the least sensed device within each time segment instance |
| meanscans | scans| Mean of the scans of every sensed device within each time segment instance|
| stdscans | scans| Standard deviation of the scans of every sensed device within each time segment instance|
!!! note "Assumptions/Observations"
- This provider is adapted from the work by [Doryab et al](../../citation#doryab-bluetooth). Devices are clasified as belonging to the participant (`own`) or to other people (`others`) using k-means based on the number of times and the number of days each device was detected across each participant's dataset.
- If ownership cannot be computed because all devices were detected on only one day, they are all considered as `other`. Thus `all` and `other` features will be equal.
- These features are computed for devices detected within each time segment instance. For example, let's say that we logged the following devices on three different time segment instances (days) for `p01`:
```csv
local_date bt_address
2016-11-29 55C836F5-487E-405F-8E28-21DBD40FA4FF
2016-11-29 55C836F5-487E-405F-8E28-21DBD40FA4FF
2016-11-29 55C836F5-487E-405F-8E28-21DBD40FA4FF
2016-11-29 48872A52-68DE-420D-98DA-73339A1C4685
2016-11-29 48872A52-68DE-420D-98DA-73339A1C4685
2016-11-30 55C836F5-487E-405F-8E28-21DBD40FA4FF
2016-11-30 55C836F5-487E-405F-8E28-21DBD40FA4FF
2016-11-30 48872A52-68DE-420D-98DA-73339A1C4685
2017-05-07 5C5A9C41-2F68-4CEB-96D0-77DE3729B729
2017-05-07 25262DC7-780C-4AD5-AD3A-D9776AEF7FC1
2017-05-07 5B1E6981-2E50-4D9A-99D8-67AED430C5A8
2017-05-07 6C444841-FE64-4375-BC3F-FA410CDC0AC7
2017-05-07 5B1E6981-2E50-4D9A-99D8-67AED430C5A8
2017-05-07 4DC7A22D-9F1F-4DEF-8576-086910AABCB5
```
- For each device we compute `days_scanned` (the number of days on which each device was detected), `scans` (the number of times each device was detected), `scans_per_day` that's equal to `scans/days_scanned`, and whether a devices is labelled as `own` or `other` (note the last device is labelled as a `own` device because it was detected 6 times over two time segment instances):
- These are the metrics for each time instance (day) for `own` and `other` devices (we ignore `all` for brevity). The only `own` device (`55C836F5-487E-405F-8E28-21DBD40FA4FF`) was detected on the first two days, 3 and 2 times respectively, the `other` devices where detected on all three days. On the last day (`2017-05-07`) there were 6 scans from 5 unique devices, the most frequent device for that day was `5B1E6981-2E50-4D9A-99D8-67AED430C5A8` with 2 scans, and the mean number of scans among all devices was 1.2 (`[1 + 1 + 1 + 1 + 2] / 5`)
