AXIOM relies on smart home sensor networks to collect and synthesize activity data to make determinations about a person’s probable location within a home at a given time. It does this by generating an activity model through the monitoring of movement over time.
![Movement Chart](https://sites.temple.edu/axiom/files/2020/04/Screenshot-2020-04-17-at-10.49.10-PM.png)
AXIOM uses sensors placed throughout the home that are ordinarily used for safety and convenience to monitor room occupancy and transition. It learns how (where) a person spends their time and the context under which is happens.
From this activity model we are able to determine a person’s routine, and once established, detect any future deviations. Such deviations may be indicative of many things, both positive and negative, depending on use case.
![Activity Graph](https://sites.temple.edu/axiom/files/2020/04/graph.png)
knowledge extracted from a person’s normal movement passes through a collection of algorithms that perform various analysis to make the most accurate determinations.
Source code on GitHub