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.
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.
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