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

用于检测患有阿尔茨海默病/ADRD 的疗养院居民跌倒和跌倒风险的自主导航机器人

• 批准号: 10698704
• 负责人: Yuval Malinsky
• 金额: $ 45万
• 依托单位: VIGOROUS MIND, INC.
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-18 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10698704
• 关键词: Acute; Adult; Agreement; Algorithmic Analysis; Alzheimer' s Disease; Alzheimer' s disease related dementia; Artificial Intelligence; Beds; Caring; Cessation of life; Communication; Computer software; Darkness; Dementia; Detection; Elderly; Employee; Eye; Face; Fall prevention; Feasibility Studies; Goals; Grant; Health Insurance Portability and Accountability Act; Hip Fractures; Impaired cognition; Injury; Insurance; Intervention; Light; Mind; Morbidity - disease rate; Nursing Homes; Outcome Measure; Patients; Performance; Persons; Phase; Population; Populations at Risk; Posture; Privacy; Psyche structure; Quality of life; Reaction; Recovery; Robot; Schedule; Secure; Surveys; System; Technology; Testing; Time; Training; Visit; age group; detection platform; fall risk; falls; fear of falling; feasibility testing; first responder; image processing; improved; mortality; peer; primary outcome; programs; satisfaction; secondary outcome; usability; virtual visit

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.

摘要 每年有一半到四分之三的养老院（NH）居民倒下。约5%的 65岁及以上的成年人住在养老院，但养老院居民占20%， 这个年龄组的福尔斯死亡人数。50%的养老院居民有中度或重度 认知障碍患有阿尔茨海默病或ADRD的老年人更容易跌倒， 没有认知障碍的同龄人。福尔斯通常会造成严重的后果，尤其是在 年老体弱的居民每10名跌倒的居民中就有一人受到严重的相关伤害， 每年有65，000名患者遭受髋部骨折。没有受伤的居民通常会发展为 害怕跌倒，导致自我限制活动，导致能力下降 功能和生活质量下降。福尔斯对设施有重大影响，包括 对跌倒者的护理水平提高，工作人员的文书工作增加，调查结果不佳， 诉讼和更高的保险费。大多数福尔斯（66%-75%）发生在住院医生的房间里， 近一半（48%）的人最终受伤。跌倒后不久进行治疗对于患者来说至关重要 从身体和精神上的跌倒中恢复过来。现有的跌倒检测系统使用静态 住户房间内的摄像头，侵犯住户隐私， 隐藏在摄像机前或在黑暗中的福尔斯。工作人员严重短缺， 疗养院可能会造成跌倒在一段时间内未被发现的情况。 拟议的研究涉及一个负担得起的自主导航机器人， 已成功用于养老院痴呆症单位，红外摄像机，图像 处理软件和人工智能是一个额外的“一组眼睛”，以巡逻走廊和房间， 护理院的居民，并检测福尔斯和福尔斯的风险，并提醒工作人员，使他们能够 立即处理。