Abstract: A method and system for privacy-aware movement tracking includes receiving a series of images of a field of view, such as captured by a camera. The images containing movement of an unidentified person within the field of view. A body region corresponding to the person is detected within the images. A movement dataset for the unidentified person is generated based on tracking movement of the body region over the fired of view within the images is generated. A characterizing feature set is determined for the unidentified person. The set is associated within the movement dataset to form a first track entry. Anonymizing of the body region can be applied to remove identifying features while or prior to determining the characterizing feature set. A second track entry can be generated from a second series of images and match between the track entries can be determined. A method and system for privacy-aware operation and learning of a computer-implemented classification module is also contemplated.

Abstract: A method for performing thermal screening is described. The method includes capturing at least one non-thermal image of a scene having a person; capturing at least one thermal image of the scene having the person; determining a region of interest of the non-thermal image; determining, based on a body sub-region of the non-thermal image corresponding to the body of the person within the scene, a distance value representative of a physical distance of the person from the thermal camera; determining a region of interest of the thermal image corresponding to the region of interest of the non-thermal image; determining a raw temperature value for the person based on the region of interest of the thermal image; and normalizing the raw temperature value based on at least the distance value to determine a normalized temperature value. A corresponding system and non-transitory computer-readable medium are also provided.

Abstract: A method and system for privacy-aware movement tracking includes receiving a series of images of a field of view, such as captured by a camera. The images containing movement of an unidentified person within the field of view. A body region corresponding to the person is detected within the images. A movement dataset for the unidentified person is generated based on tracking movement of the body region over the fired of view within the images is generated. A characterizing feature set is determined for the unidentified person. The set is associated within the movement dataset to form a first track entry. Anonymizing of the body region can be applied to remove identifying features while or prior to determining the characterizing feature set. A second track entry can be generated from a second series of images and match between the track entries can be determined. A method and system for privacy-aware operation and learning of a computer-implemented classification module is also contemplated.

Abstract: Disclosed herein are embodiments of systems, methods, and products comprises an analytic server, which provides stateless decision support for clinical evaluation. The analytic server receives clinical evaluation process model from a domain expert and converts the process model into logical expressions of a decision tree. The analytic server dynamically identifies the data referenced in the process and retrieves the referenced data from external databases. The analytic server generates a stateless decision tree comprising the local expressions and the referenced data. The analytic server receives a request comprising patient data and patient needs and preferences. The analytic server determines a decision tree based on the contents of the request. The analytic server evaluates the patient against the decision tree and determines the evaluation results.