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OwnerSteve Gail Johnson, Kyle Brook Johnson +1inventors
InventorsSteve Gail Johnson, Kyle Brook Johnson, Stephen Ecker
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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.
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:
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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