Abstract: Systems and methods for tracking activities from a plurality of multimodal inputs are described. Activity tracking can include receiving a plurality of multimodal inputs, synchronizing the plurality of multimodal inputs, generating segments from the synchronized multimodal inputs, recognizing activities associated with each generated segment by performing a bagged formal concept analysis (BFCA), and recording the recognized activities in a storage. Tracking of activities can include the detection of moments (e.g., eating moments), during which an activity tracking application can prompt a user for information (e.g., a food journal).

Abstract: Method and apparatus for personal health navigation includes determining, by a processor, a personal health state space comprising a set of connected biological states for an individual; determining, for the individual, a region of interest within the personal health state space, in which the region of interest is defined by a goal state relative to a current state of the individual, the goal state associated with a personal health goal of the individual; determining a route comprising the current state, the goal state, and an intermediate state within the personal health state space, the intermediate state closer in distance to the goal state than the current state; and providing, to the individual, a health instruction indicative of a recommended action based on a connection between the current state and the intermediate state, in which the recommended action is selected to guide the individual to transition to the intermediate state.

Abstract: Systems and methods for tracking activities from a plurality of multimodal inputs are described. Activity tracking can include receiving a plurality of multimodal inputs, synchronizing the plurality of multimodal inputs, generating segments from the synchronized multimodal inputs, recognizing activities associated with each generated segment by performing a bagged formal concept analysis (BFCA), and recording the recognized activities in a storage. Tracking of activities can include the detection of moments (e.g., eating moments), during which an activity tracking application can prompt a user for information (e.g., a food journal).

Abstract: Activity tracking and classification for diabetes management is disclosed. In an example, an application operating on a user device or a server is configured to collect and synchronize lifelog data of a patient to atomic time intervals based on a time the lifelog data occurred or was recorded. The application or server is configured to segment the atomic intervals into daily activity intervals of first-level activities by determining if consecutive atomic intervals have a similar pattern of physical activity using at least a portion of the lifelog data. The application or server next selects a common daily activity model that corresponds to the patient and performs second-level activity recognition for each of the daily activity intervals using the selected common daily activity model. The application or server then generates, for display a personal chronical of the recognized second-level activities.