Application designed to collect drivers data for collection of driving behaviors to train future machine learning/Deep Learning models.
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//Enviroment
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Light Sensor (Currently only sensor displayed to UI)
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LIGHT
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PRESSURE
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AMBIENT_TEMPERATURE
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RELATIVE_HUMIDITY
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//MOTION SENSORS
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ACCELERATION
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GRAVITY
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//Position Sensors
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MAGNETIC_FIELD
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PROXIMITY
Partially Implemented.
- Currently GPS data is collected and stored to local database.
- IS NOT currently included in event data.
Please Note: Permissions must currently be accepted for location prior to running the app.
Not yet implemented
- The idea is to connect to OBD-II addapter with a specialized hardwaret to collect vehicle related data.
Not Yet Implemented.
- Its possible to collect data from additional specialized sensors via bluetooth or other means. There are various possibilities.
Not Yet Implemented.
- Various "visual" data collection methods could be added in future. Although visual data requires additional bandwidth and processing needs, so this will need to be carefully thought out.
Not yet Implemented.
- Many data points can be added by integerating with google health or samsung fit. Currently Google is developing Health Connect which may be used in future to obtain additional data points such as Heart Rate, body temperature, etc.
Not yet implemented
- Collection of data from different sound sources (engine, interiror) to assist in making predictions.
- display Sensor Data from screen
- implement settings for app.
- data exportation for model training.
- connect to OBD-II
- Limit sensor data collection during driving activity only.
- Implement GPS location tracking as seperate data collection
- Permission Manager
Note: All available sensors are currently stored in the database. Only those selected in the settings menu are used to create events.
Note: This is example output of a single update... Updates occur frequently and local database will have many more entries
[
{
"accuracy": 3,
"id": 24597,
"measurementName": "LIGHT",
"sensorCategory": "ENVIRONMENT",
"sensorType": "android.sensor.light",
"timestamp": 1704845412929,
"unitOfMeasurement": "lx",
"values": [
17
]
},
{
"accuracy": 3,
"id": 24598,
"measurementName": "LIGHT",
"sensorCategory": "ENVIRONMENT",
"sensorType": "android.sensor.light",
"timestamp": 1704845412929,
"unitOfMeasurement": "lx",
"values": [
17
]
},
{
"accuracy": 3,
"id": 24599,
"measurementName": "GRAVITY",
"sensorCategory": "MOTION",
"sensorType": "android.sensor.gravity",
"timestamp": 1704845413025,
"unitOfMeasurement": "m/s^2",
"values": [
-2.9111452,
4.64582,
8.130926
]
},
{
"accuracy": 3,
"id": 24600,
"measurementName": "ACCELERATION",
"sensorCategory": "MOTION",
"sensorType": "android.sensor.linear_acceleration",
"timestamp": 1704845413025,
"unitOfMeasurement": "m/s^2",
"values": [
-0.49188042,
-1.2778986,
0.9810686
]
},
{
"accuracy": 3,
"id": 24601,
"measurementName": "PRESSURE",
"sensorCategory": "ENVIRONMENT",
"sensorType": "android.sensor.pressure",
"timestamp": 1704845413034,
"unitOfMeasurement": "hPa",
"values": [
969.3364
]
},
{
"accuracy": 3,
"id": 24602,
"measurementName": "GYROSCOPE",
"sensorCategory": "MOTION",
"sensorType": "android.sensor.gyroscope",
"timestamp": 1704845413044,
"unitOfMeasurement": "rad/s",
"values": [
0.5480827,
-0.20753536,
-0.039094225
]
},
{
"accuracy": 3,
"id": 24603,
"measurementName": "MAGNETIC_FIELD",
"sensorCategory": "POSITION",
"sensorType": "android.sensor.magnetic_field",
"timestamp": 1704845413085,
"unitOfMeasurement": "μT",
"values": [
36.43125,
-15.825001,
-30.993752
]
},
{
"accuracy": 3,
"id": 24604,
"measurementName": "LIGHT",
"sensorCategory": "ENVIRONMENT",
"sensorType": "android.sensor.light",
"timestamp": 1704845413163,
"unitOfMeasurement": "lx",
"values": [
14
]
},