Abstract:
A method includes that for each model from multiple models, evaluating a model prediction accuracy based on a dataset of a user over a first time duration. The dataset includes a sequence of actions with corresponding contexts based on electronic device interactions. Each model is trained to predict a next action at a time point within the first time duration, based on a first behavior sequence over a first time period from the dataset before the time point, a second behavior sequence over a second time period from the dataset before the time point, and context at the time point. A model is selected from the multiple models based on its model prediction accuracy for the user based on a domain. An action to be initiated at a later time using an electronic device of the user is recommended using the selected model during a second time duration.
Abstract:
A method and system for monitoring resource information and user activity. The method includes acquiring one or more data streams from one or more resource meters and one or more electronic device sensors. Discrete events are computed from each data stream. A sequence of discrete sensor-meter event itemsets are extracted based on the events. Frequent sensor-meter event itemsets are discovered from the sequence of discrete event itemsets that occur together, and a frequency of occurrence of each frequent co-occurrence itemset is discovered. Rising sensor-meter event itemsets and falling sensor-meter event itemsets are matched based on appliance state models and the frequency of occurrence of each sensor-meter event itemset. Each individual fixture is identified. Each fixture cluster is classified to a fixture category. Based on the matched fixture events, fixture clusters, and categories, resource usage information and user activities are determined for each fixture usage event identified.
Abstract:
An activity recognition system for an electronic device comprising at least one sensor and a two-phase activity recognition module. The sensors capture data relating to user activity. The activity recognition application module identifies a user activity based on data captured by the sensors, and dynamically controls power consumption of the activity recognition module based on the user activity identified.