Careview Communications, Inc.

Other

Steve Gail Johnson

Stephen Ecker

Kyle Brook Johnson

Matthew Cameron Clark

Richard Craig Kuether

A patient fall prediction system from noise corrected surveillance video by identifying patient fall risk states. A hierarchy of discrete patient fall risk states, from no risk, to intermediate risk to critical risk, describe a patient fall risk. The system transitions from state to state based on changes detected in corresponding areas between a current video frame and a background frame. A set of fall risk state transition rules govern the entry into new fall risk states. A video frame is subdivided into multiple predetermined areas, at least two contain images of the patient. The number of false alarms are reduced by accurately defining fall risk state transition rules and by reducing the opportunity for noise to impact the state transition results. Frames that contain new changes are excluded from fall risk state processing, i.e., the first video frame that might cause an erroneous elevated fall risk state is culled.

Noise correcting patient fall risk state system and method for predicting patient falls

A patient fall prediction system receives video image frames from a surveillance camera positioned in a patient's room and analyzes 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. One fall movement detection signature is a sequential detection order from the motion detection zone closest to the risk area in the frames associated with patient falls, to the motion detection zone farthest away from the risk area. The patient fall prediction system continually monitors the motion detection zones for changes between image frames and compiles detections lists that are compared to known movement detection signatures, such as a fall movement detection signature. Once a match is identified, the patient fall prediction system issues a fall warning to a healthcare provider.

System and method for predicting patient falls

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. Analysis techniques include selecting a plurality of vertical positions in the area of interest, each vertical position corresponding to a part of the patient body indicative of the patient's position, horizontally scanning values along the horizontal pixel row at each vertical position in the area of interest, finding an average value for each vertical position and identifying a patient interval of the horizontal pixel row having pixel values different from the average and a representative horizontal position of the patient interval for each vertical interval. Then, comparing those median horizontal positions from a current image frame to the representative horizontal positions from a previous image frame to determine a change in the patient's position.

System and method for using a video monitoring system to prevent and manage decubitus ulcers in patients

The local surveillance sub-system recognizes that a patient medical procedure has or will soon commence by sensing the presence of a healthcare professional in or near the surveillance area, and in response, creates a separate patient medical procedure A/V file for the surveillance data that will be captured. A dedicated procedure remote may be provided for receiving manual interactions from HC professionals present for a procedure or, alternatively, the local surveillance sub-system may autonomously interact with a personal security token device possessed by the HC professional. A procedure data file is also created that holds all of the pertinent information concerning the procedure that is known by the local surveillance sub-system. The patient procedure surveillance A/V file is given a higher priority than ordinary surveillance data captured by the local surveillance sub-system and is then copied to a nonvolatile memory that is separate from the primary memory of the surveillance sub-system. The local surveillance sub-system captures surveillance A/V data that is copied to the nonvolatile memory until the system senses that the procedure has ended, when an end-of-procedure message is transmitted across the HCF distribution network. The corresponding patient medical procedure surveillance A/V file resides in a local nonvolatile memory until it can be downloaded to a central storage at the healthcare facility.

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Highland Village, TX, United States of America

Steve Gail Johnson