Abstract: Detecting multiple people in a room includes detecting a point cloud corresponding to at least one user moving in the room, forming a bounding box corresponding to coordinates of points of the point cloud, and determining if the point cloud corresponds to multiple people based on a size of the point cloud, a presence of separate clusters of points in the point cloud, and/or detecting one or more people entering or leaving the room. Detecting a point cloud may include using a tracking device. The size of the point cloud may be compared with a size of a point cloud corresponding to a single user of the room. Detecting one or more people entering or leaving the room may include previously detecting entrances of the room by tracking movement of people in the room and determining a location where movement results in more or less people in the room.

Abstract: Determining sleep patterns of a user includes detecting a plurality of point clouds, each corresponding to a different position of the user at different times, forming a plurality of bounding boxes, each corresponding to coordinates of captured points of one of the point clouds, creating a wake/sleep classifier based on features of the point clouds, determining sleep positions of the user as a function of time based on the bounding boxes, and determining sleep patterns of the user based on the sleep positions of the user and on results of the sleep/wake classifier. Detecting a plurality of point clouds may include using a tracking device to capture movements of the user. The features of the point clouds may include intermediate data that is determined using scalar velocities of points in the point clouds, absolute velocities of points in the point clouds, and/or counts of points in the point clouds.

Abstract: Determining gait patterns and abnormalities of a user includes forming a plurality of point clouds corresponding to the user, each of the point clouds being three-dimensional coordinates of moving points, frame by frame, through a data capturing session, determining centroids of the point clouds, determining momentary walking velocities using estimates based on vectors connecting the centroids for adjacent frames captured during walking of the user, determining gait speed for the user based on the momentary walking velocities, determining at least one distribution of gait speeds for the user, and detecting gait abnormalities based on deviation of the gait speed from the at least one distribution of gait speeds. Detecting a plurality of point clouds may include using a tracking device to capture movements of the user. The tracking device may use radar and/or lidar. The system may determine a gait pattern of the user corresponding to routines of the user.

Abstract: Determining a physical state of a person includes detecting positions of different portions of the person, transforming detected positions into a point cloud having a density that varies according to movement of each of the portions, correlating movement and position data from the point cloud with known physical state positions and transitions between different states, choosing a particular physical state by matching the data from the point cloud with the particular physical state, and obtaining vital signs of the person during an optimal period of time for automatic capturing of vital signs by detecting when the person is in a particular state. The particular state may be a static state. The static state may be standing, sitting, or laying down. The vital signs may include measuring a breathing rate and measuring a heartbeat rate. Vital signs may be obtained by detecting pulsations in the point cloud representing breathing and heartbeats.

Abstract: Detecting multiple people in a room includes detecting a point cloud corresponding to at least one user moving in the room, forming a bounding box corresponding to coordinates of points of the point cloud, and determining if the point cloud corresponds to multiple people based on a size of the point cloud, a presence of separate clusters of points in the point cloud, and/or detecting one or more people entering or leaving the room. Detecting a point cloud may include using a tracking device. The size of the point cloud may be compared with a size of a point cloud corresponding to a single user of the room. Detecting one or more people entering or leaving the room may include previously detecting entrances of the room by tracking movement of people in the room and determining a location where movement results in more or less people in the room.

Abstract: Determining gait patterns and abnormalities of a user includes forming a plurality of point clouds corresponding to the user, each of the point clouds being three-dimensional coordinates of moving points, frame by frame, through a data capturing session, determining centroids of the point clouds, determining momentary walking velocities using estimates based on vectors connecting the centroids for adjacent frames captured during walking of the user, determining gait speed for the user based on the momentary walking velocities, determining at least one distribution of gait speeds for the user, and detecting gait abnormalities based on deviation of the gait speed from the at least one distribution of gait speeds. Detecting a plurality of point clouds may include using a tracking device to capture movements of the user. The tracking device may use radar and/or lidar. The system may determine a gait pattern of the user corresponding to routines of the user.

Abstract: Determining a physical state of a person includes detecting positions of different portions of the person, transforming detected positions of the person into a point cloud having a density that varies according to movement of each of the portions, correlating movement and position data from the point cloud with known physical state positions and transitions between different states, and choosing a particular physical state by matching the data from the point cloud with the particular physical state. Positions of different portions of the person may be detected using a tracking device. The tracking device may be a non-contact tracking device. The tracking device may include at least one wide band radar. The tracking devices may communicate wirelessly with at least one server in a cloud computing system. The states may include walking, standing, sitting, laying down, turning in bed, falling, and/or departed.

Abstract: Determining a physical state of a person includes detecting positions of different portions of the person, transforming detected positions of the person into a point cloud having a density that varies according to movement of each of the portions, correlating movement and position data from the point cloud with known physical state positions and transitions between different states, and choosing a particular physical state by matching the data from the point cloud with the particular physical state. Positions of different portions of the person may be detected using a tracking device. The tracking device may be a non-contact tracking device. The tracking device may include at least one wide band radar. The tracking devices may communicate wirelessly with at least one server in a cloud computing system. The states may include walking, standing, sitting, laying down, turning in bed, falling, and/or departed.

Abstract: Determining a physical state of a person includes detecting positions of different portions of the person, transforming detected positions into a point cloud having a density that varies according to movement of each of the portions, correlating movement and position data from the point cloud with known physical state positions and transitions between different states, choosing a particular physical state by matching the data from the point cloud with the particular physical state, and obtaining vital signs of the person during an optimal period of time for automatic capturing of vital signs by detecting when the person is in a particular state. The particular state may be a static state. The static state may be standing, sitting, or laying down. The vital signs may include measuring a breathing rate and measuring a heartbeat rate. Vital signs may be obtained by detecting pulsations in the point cloud representing breathing and heartbeats.