Date of Graduation
Master of Science in Computer Engineering (MSCmpE)
Computer Science & Computer Engineering
Second Committee Member
As technology advances in the field of Computer Vision, new applications will emerge. One device that has emerged is the smart-camera, a camera attached to an embedded system that can perform routines a regular camera could not, such as object or event detection. In this thesis we describe a smart-camera system we designed, implemented, and evaluated for fall prevention monitoring of at-risk people while in bed, whether it be for a hospital patient, nursing home resident, or at home elderly resident. The camera will give a nurse or caregiver environmental awareness of the at-risk person and notify them when that person performs an action that could lead to a hazardous event. This camera uses Haar Cascade facial detection techniques, Histogram of Oriented Gradients(HOG) for person detection, and Mixture of Gaussians (MOG) background subtraction while operating. Regions are created by a person from a graphical user interface (GUI). The camera looks within these regions to find a face, a standing person, or just a change in the image. A notification is sent to the smartphone of the nurse or caregiver of the corresponding at-risk person when the camera finds one of these three detections in the drawn region. The Cloud is utilized to send the notification to the nurse or caregiver’s smartphone. Given a properly placed camera and properly drawn regions, notifications can be sent when the at-risk person is doing an action that demands the attention of the nurse or caregiver, such as getting out of bed. The smart-camera does contain drawbacks. It is likely to give alerts when visitors are in the room, and it does not know how to pause notifications when a nurse, doctor, or caregiver comes into the room.
Kutchka, Jeffrey, "Automatic Assessment of Environmental Hazards for Fall Prevention Using Smart-Cameras" (2016). Theses and Dissertations. 1766.