Noise correcting patient fall risk state system and method for predicting patient falls
DCFirst Claim
1. A method for predicting a risk of a patient fall based on a plurality of fall risk states, the plurality of fall risk states defining a hierarchy of discrete fall risk states, at least one of the fall risk states being associated with an action, the method comprising:
- receiving a plurality of video frames from a surveillance video camera, said surveillance video camera captures a sequence of video frames of a surveillance area, wherein a patient is present in the surveillance area, each of the plurality of video frames comprising a plurality of predetermined areas;
detecting a change in at least one of the plurality of predetermined areas of the current video frame of the plurality of video frames;
evaluating the change detected in the at least one of the plurality of predetermined areas of the current video frame for noise; and
identifying a fall risk transition to a fall risk state selected from the hierarchy of discrete fall risk states, the hierarchy of discrete fall risk states including a wait state, a no-wait state, and at least one other state, and wherein each fall risk state includes event and timing information, wherein the identification of a fall risk transition is based upon the change detected in the at least one of the plurality of predetermined areas of the current video frame.
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Abstract
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.
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Citations
20 Claims
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1. A method for predicting a risk of a patient fall based on a plurality of fall risk states, the plurality of fall risk states defining a hierarchy of discrete fall risk states, at least one of the fall risk states being associated with an action, the method comprising:
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receiving a plurality of video frames from a surveillance video camera, said surveillance video camera captures a sequence of video frames of a surveillance area, wherein a patient is present in the surveillance area, each of the plurality of video frames comprising a plurality of predetermined areas; detecting a change in at least one of the plurality of predetermined areas of the current video frame of the plurality of video frames; evaluating the change detected in the at least one of the plurality of predetermined areas of the current video frame for noise; and identifying a fall risk transition to a fall risk state selected from the hierarchy of discrete fall risk states, the hierarchy of discrete fall risk states including a wait state, a no-wait state, and at least one other state, and wherein each fall risk state includes event and timing information, wherein the identification of a fall risk transition is based upon the change detected in the at least one of the plurality of predetermined areas of the current video frame. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A system for predicting a risk of a patient fall based on a plurality of fall risk states, the plurality of fall risk states defining a hierarchy of discrete fall risk states, at least one of the fall risk states being associated with an action, the system comprising:
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a surveillance video camera operative to capture a plurality of video frames of a surveillance area, wherein a patient is present in the surveillance area and each of the plurality of video frames comprises a plurality of predetermined areas; and a video processor operative to detect a change in at least one of the plurality of predetermined areas of the current video frame of the plurality of video frames, evaluate the change detected in the at least one of the plurality of predetermined areas of the current video frame for noise, and identify a fall risk transition to a fall risk state selected from the hierarchy of discrete fall risk states, the hierarchy of discrete fall risk states including a wait state, a no-wait state, and at least one other state, and wherein each fall risk state includes event and timing information, wherein the identification of a fall risk transition is based upon the change detected in the at least one of the plurality of predetermined areas of the current video frame. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19, 20)
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Specification