Abstract:
A system for monitoring a patient in a patient area having one or more detection zones, the system comprising one or more cameras, a user interface, and a computing system configured to receive a chronological series of frames from the one or more cameras, identify liquid candidates by comparing a current frame with a plurality of previous frames of the chronological series, determine locations of the liquid candidates, identify thermal signatures of the liquid candidates, determine types of liquids of the liquid candidates based on the locations and thermal signatures of the liquid candidates, and generate an alert with the user interface corresponding to the determined types of liquids.
Abstract:
A smart monitoring system comprising a plurality of sensor devices coupled to appliances and fixtures within a dwelling environment, where at least one of the plurality of sensor devices comprises sensor elements including an accelerometer. The system further comprising a computing device operative to receive event signals from the plurality of sensor devices, identify a possible fall event from one or more of the plurality of sensor devices based on the event signals, sample sensor data from one or more of the plurality of sensor devices wherein the sensor data includes measurements of movement. The computer device is further operative to determine a fall has occurred based on the sampled sensor data, sample additional sensor data from the one or more of the plurality of sensor devices for additional motion at a period of time subsequent to the possible fall event, and determine a recovery from the fall based on the additional sensor data.
Abstract:
A video monitoring system captures image frames of a patient in various positions. The captured image frames are analyzed by the system for changes in a patient's position or movement, frames in which the system detects one or both of patient movement and repositioning are retained. The system analyzes an area of interest within each image frame that corresponds to an area in the camera's view field with the patient. Sequential image frames are compared for motion, only frames without motion, where the patient is still, are analyzed.
Abstract:
A smart monitoring system comprising a plurality of sensor devices coupled to appliances and fixtures within a dwelling environment, at least one of the plurality of sensor devices comprising sensor elements including an accelerometer configured to detect a usage associated with the appliances and fixtures, and a computing device operative to receive event signals from the plurality of sensor devices, generate a collection of data with the event signals, analyze the collection of data, generate analytics and pattern data based on the analysis, and generate notifications based on abnormalities in the analytics and pattern data.
Abstract:
A method and system for detecting a fall risk condition, the system comprising a surveillance camera configured to generate a plurality of frames showing an area in which a patient at risk of falling is being monitored, and a computer system comprising memory and logic circuitry configured to store motion feature patterns that are extracted from video recordings, the motion feature patterns are representative of motion associated with real alarm cases and false-alarm cases of fall events, receive a fall alert from a classifier, determine motion features of one or more frames from the plurality of frames that correspond to the fall alert; compare the motion features of the one or more frames with the motion feature patterns, and determine whether to confirm the fall alert based on the comparison.
Abstract:
A method and system for detecting a fall risk condition, the system comprising a surveillance camera configured to generate a plurality of frames showing an area in which a patient at risk of falling is being monitored, and a computer system comprising memory and logic circuitry configured to store motion feature patterns that are extracted from video recordings, the motion feature patterns are representative of motion associated with real alarm cases and false-alarm cases of fall events, receive a fall alert from a classifier, determine motion features of one or more frames from the plurality of frames that correspond to the fall alert; compare the motion features of the one or more frames with the motion feature patterns, and determine whether to confirm the fall alert based on the comparison.
Abstract:
An electronic sitter management system coupled to patient surveillance network having a plurality of video cameras, each camera transmitting a stream of surveillance video of a respective patient room. The sitter management system includes at least one sitter management device and a plurality of sitter devices. Each device being assigned a plurality of patient rooms and capable of receiving a plurality of streams of surveillance video for the corresponding plurality of patient rooms and simultaneously displaying a plurality of video images of the corresponding plurality of patient rooms. Each device is also capable of transmitting sitter device availability information to the sitter management device. The sitter management device being capable of recognizing a sitter device being unavailable and reassigning the plurality of patient rooms previously assigned to the unavailable device to other of the plurality of sitter devices that are available.
Abstract:
A patient fall prediction system receives video image frames from a surveillance camera positioned in a patient's room and analyses the video image frames for movement that may be a precursor to a patient fall. In set up phase, the viewpoint of the camera is directed at a risk area associated with patient falls, beds, chairs, wheelchairs, etc. A risk area is defined graphically in the viewport. The patient fall prediction system generates a plurality of concurrent motion detection zones that are situated proximate to the graphic markings of the risk areas. These motion detection zones are monitored for changes between video image frames that indicate a movement. The pattern of detections is recorded and compared to a fall movement detection signature.