Context Detection is the core of Resonance AI and it’s based on the idea of determining what user is doing, by combining information coming from different data sources (e.g. Android / iOS devices, wearables, external systems) and processing them through advanced models developed for Google Tensorflow.
The Context API are organized in eight groups:
| API | Description |
|---|---|
| still | The user is still in a place. |
| in_vehicle | The user is moving by some type of vehicle (car, bus, train, subway, flight); |
| on_foot | The user is running or walking; |
| biking | The user is biking |
The devices used to estimate these informations are:
| API | Description |
|---|---|
| car | The user is driving a car; |
| bus | The user is moving by bus; |
| train | The user is traveling by train; |
| subway | The user is traveling by metro; |
| flight | The user is traveling by flight; |
| ferry | The user is traveling by ferry; |
The devices used to estimate these informations are:
This group gives information about the user places; the types implemented are:
The devices used to estimate these informations are:
This category collects context like:
The devices used to estimate these informations are:
This category collects context like:
The devices used to estimate these informations are:
This category collects context like:
The devices used to estimate these informations are:
This category collects context like:
The devices used to estimate these informations are:
This category collects context like:
The devices used to estimate these informations are: