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STTR Phase I: Development of a Safety System for Individuals with Alzheimer's Disease and Related Dementias
Phone: (510) 642-2468
Phone: (510) 642-2468
Contact: George Netscher
Type: Nonprofit College or University
The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project is a safety system for improving the quality and reducing the cost of dementia care. Alzheimer's disease affects 5.4M in the US, including 1 in 9 over 65 and 1 in 3 over 85, and represents two thirds of all those affected by dementia. Despite that Alzheimer's disease is the single most expensive disease in the US and falls are the leading cause of hospitalization in Alzheimer's care, current tools offer little support. Although 3/4 of elderly fallers will experience a repeat fall, solutions like bed alarms and wearable fall detection systems offer no way to see how falls occur. Care staff have no way of learning from the first fall to reduce the likelihood of the second and must implement painful and expensive policies such as sending every unwitnessed fall to the emergency room in case a hit to the head occurred. The proposed project addresses this critical gap in Alzheimer's care by detecting falls based on camera video where falls can be reviewed by a human assistant in real-time and after the fact. Real-time review allows for instant notification if a hit to the head occurred, and review after the fact allows for determining the cause of the fall to see if changes in room layout and/or policy could be made. The primary aim of this project is to collect video data of real falls 1) to apply and extend state-of-the-art deep learning methods to perform high accuracy detection and 2) to validate that affected individuals, family, and care staff are accepting of a camera-based solution. Fall detection will be performed by extending the Region-Based Convolutional Neural Network (RCNN) algorithm using domain adaptation techniques developed to robustly handle night-vision camera operation, occlusion, and non-standard human pose. Technical success will be measured by <1% missed detection and <50% false positive rate from this feasibility study. This first accuracy threshold will define a lower bound where, as has been demonstrated repeatedly in the deep-learning paradigm, accuracy will continue to improve as more data is collected.
* Information listed above is at the time of submission. *