9. The method of claim 1 , wherein creating one or more models based on the likelihood of similarly expressed events happening at similar times with similar characteristics comprises analyzing activity sensed from a combination of sensors using a clustering process to find contiguous periods of a behavior and determine likelihood of the behavior occurring during bounds specified by the clustering process.
9. The method of claim 1 , wherein creating one or more models based on the likelihood of similarly expressed events happening at similar times with similar characteristics comprises analyzing activity sensed from a combination of sensors using a clustering process to find contiguous periods of a behavior and determine likelihood of the behavior occurring during bounds specified by the clustering process. 10. The method of claim 9 , wherein analyzing activity sensed from the combination of sensors using the clustering process to find contiguous periods of a behavior and determine likelihood of the behavior occurring during bounds specified by the clustering process comprises: defining a maximum cluster count; defining a minimum cluster weight; defining a starting standard deviation threshold; calculating times between adjacent behavior events; calculating mean and standard deviation of distances between adjacent behavior events based on the calculated times between adjacent behavior events; creating an ordered list of distances between adjacent behavior events as event tuples; selecting, from the ordered list, a smallest distance to create a first cluster; iterating through the ordered list of event tuples to create aggregate clusters based on a distance between a cluster's boundary event and a new event being less than the standard deviation threshold; pruning, from the aggregate clusters, clusters of insufficient cluster weight based on the minimum cluster weight; expanding the standard deviation threshold based on a number of clusters remaining after pruning exceeding the maximum cluster count; and based on the number of clusters remaining after pruning being less than the maximum cluster count, storing the clusters remaining after pruning, the clusters remaining after pruning representing Gaussian mixture model approximations of activity.