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.

抽象的 一半到四分之三的疗养院 (NH) 居民每年都会跌倒约 5%。 65岁及以上的成年人住在疗养院，但疗养院居民占20% 这个年龄段的疗养院居民中有 50% 因跌倒而死亡。 患有阿尔茨海默病或 ADRD 的老年人比患有认知障碍的老年人更容易跌倒。 没有认知障碍的同龄人跌倒通常会造成严重后果，尤其是在儿童中。 体弱的老年居民中，每 10 名跌倒的居民中就有 1 名患有严重的相关伤害。 每年有 65,000 名患者发生髋部骨折，未受伤的居民经常会发生髋部骨折。 害怕跌倒会导致自我限制活动，从而导致能力下降 跌倒对设施造成重大影响，包括： 跌倒者所需的护理水平增加，工作人员的文书工作增加，调查结果不佳， 诉讼和较高的保险费大多数跌倒（66%-75%）发生在住户的房间和 几乎一半 (48%) 的人在跌倒后立即接受治疗对于康复至关重要。 现有的跌倒检测系统使用静电来从跌倒中恢复。 住户房间内的摄像头侵犯住户隐私且可能无法检测到 镜头看不见的跌倒或在黑暗中发生的跌倒 工作人员严重短缺。 疗养院可能会造成跌倒一段时间内未被发现的情况。 拟议的研究涉及集成一个经济实惠的自主导航机器人， 已成功应用于疗养院痴呆症病房，配有红外摄像头，图像 处理软件和人工智能成为额外的“眼睛”来巡逻走廊和房间 疗养院居民，检测跌倒和跌倒风险，并提醒工作人员，以便他们能够 立即处理。