Autonomous Navigating Robot for Detecting Falls and Risk of Falls in Nursing Home Residents with Alzheimer's Disease /ADRD

摘要

ABSTRACT Between half and three-quarters of nursing home (NH) residents fall each year. About 5% of adults 65 and older live in nursing homes, but nursing home residents account for 20% of deaths from falls in this age group. 50% of nursing home residents have moderate or severe cognitive impairment. Older adults with Alzheimer’s Disease or ADRD are more likely to fall than their peers without cognitive impairment. Falls often have serious consequences, especially in frail older residents. One in every 10 residents who fall has a serious related injury and about 65,000 patients suffer hip fracture each year. Residents who fall without injury often develop a fear of falling that leads to self-imposed limitation of activity leading to a decrease in the ability to function and a reduced quality of life. Falls have major consequences for facilities including increased levels of care required for fallers, increased paperwork for staff, poor survey results, lawsuits and higher insurance premiums. Most falls (66%-75%) occur in the resident’s room and almost half (48%) end in an injury. Treating a person shortly after falling is critical to the recovery from the fall both physically and mentally. Existing fall detection systems use static cameras in the rooms of residents which violate resident privacy and may be unable to detect falls that are hidden from the camera or that occur in the dark. The acute shortage of staff in nursing homes may create situations where a fall is undetected for a while. The proposed study involves integration of an affordable autonomous navigating robot which has been used successfully in nursing home dementia units, with an infra-red camera, image processing software and AI to be an additional “set of eyes” to patrol the corridors and rooms of nursing homes residents and to detect falls and risk of falls and alert staff so they can be handled immediately.