Abstract: A method for determining the risk of a person falling is provided. The method includes acquiring depth image data that comprises a plurality of frames that depict a person walking through a home, and extracting a foreground object from the depth image data. The method additionally includes generating a three-dimensional data object based on the foreground object, and identifying a walking sequence from the three-dimensional data object. The method further includes generating one or more gait parameters from the identified walking sequence, and comparing the one or more gait parameters against a standard clinical measure of the one or more gait parameters to determine a level of risk at which the person is of falling.

Abstract: A method for determining the risk of a person falling is provided. The method includes acquiring depth image data that comprises a plurality of frames that depict a person walking through a home, and extracting a foreground object from the depth image data. The method additionally includes generating a three-dimensional data object based on the foreground object, and identifying a walking sequence from the three-dimensional data object. The method further includes generating one or more gait parameters from the identified walking sequence, and comparing the one or more gait parameters against a standard clinical measure of the one or more gait parameters to determine a level of risk at which the person is of falling.

Abstract: A method for determining the risk of a person falling is provided. The method includes acquiring depth image data that comprises a plurality of frames that depict a person walking through a home, and extracting a foreground object from the depth image data. The method additionally includes generating a three-dimensional data object based on the foreground object, and identifying a walking sequence from the three-dimensional data object. The method further includes generating one or more gait parameters from the identified walking sequence, and comparing the one or more gait parameters against a standard clinical measure of the one or more gait parameters to determine a level of risk at which the person is of falling.

Abstract: A method for performing health risk assessments for a patient in a home or medical facility is provided. In various embodiments, the method comprises compiling depth image data from at least one depth camera associated with a particular patient, and generating at least one three-dimensional object based on the depth image data. The method additionally includes identifying a walking sequence from the at least one three-dimensional object, analyzing the walking sequence to generate one or more parameters, and performing at least one health risk assessment based on the one or more parameters to determine a health risk assessment score. The method further comprises sending an alert message to at least one caregiver when the at least one health risk assessment indicates the occurrence of an incident (i.e., the recorded depth image data) denoting that the patient has fallen or that there exists a high risk of the patient falling.

Abstract: Aspects of the present disclosure include methods and corresponding systems for performing health risk assessments for a patient in the home environment. In various aspects, depth image data for a person may be obtained and subsequently processed to generate one or more parameters, such as temporal and spatial gait parameters. Subsequently, the generated parameters may be processed with other medical information related to the patient, such as electronic health records, to perform various health risk assessments.

Abstract: Methods and systems for anonymized video analysis are described. In one embodiment, a first silhouette image of a person in a living unit may be accessed. The first silhouette image may be based on a first video signal recorded by a first video camera. A second silhouette image of the person in the living unit may be accessed. The second silhouette image may be of a different view of the person than the first silhouette image. The second silhouette image may be based on a second video signal recorded by a second video camera. A three-dimensional model of the person in voxel space may be generated based on the first silhouette image, the second silhouette image, and viewing conditions of the first video camera and the second video camera. In some embodiments, information on falls, gait parameters, and other movements of the person living unit are determined. Additional methods and systems are disclosed.

Abstract: A method for performing health risk assessments for a patient in a home or medical facility is provided. In various embodiments, the method comprises compiling depth image data from at least one depth camera associated with a particular patient, and generating at least one three-dimensional object based on the depth image data. The method additionally includes identifying a walking sequence from the at least one three-dimensional object, analyzing the walking sequence to generate one or more parameters, and performing at least one health risk assessment based on the one or more parameters to determine a health risk assessment score. The method further comprises sending an alert message to at least one caregiver when the at least one health risk assessment indicates the occurrence of an incident (i.e., the recorded depth image data) denoting that the patient has fallen or that there exists a high risk of the patient falling.

Abstract: Aspects of the present disclosure include methods and corresponding systems for performing health risk assessments for a patient in the home environment. In various aspects, depth image data for a person may be obtained and subsequently processed to generate one or more parameters, such as temporal and spatial gait parameters. Subsequently, the generated parameters may be processed with other medical information related to the patient, such as electronic health records, to perform various health risk assessments.

Abstract: Methods and systems for anonymized video analysis are described. In one embodiment, a first silhouette image of a person in a living unit may be accessed. The first silhouette image may be based on a first video signal recorded by a first video camera. A second silhouette image of the person in the living unit may be accessed. The second silhouette image may be of a different view of the person than the first silhouette image. The second silhouette image may be based on a second video signal recorded by a second video camera. A three-dimensional model of the person in voxel space may be generated based on the first silhouette image, the second silhouette image, and viewing conditions of the first video camera and the second video camera. In some embodiments, information on falls, gait parameters, and other movements of the person living unit are determined. Additional methods and systems are disclosed.