Fall Detection and Prevention for Memory Care through Real-Time Artificial Intelligence Applied to Video

通过应用于视频的实时人工智能进行跌倒检测和预防以实现记忆护理

• 批准号: 10020322
• 负责人: Glen Xiong
• 金额: $ 49.36万
• 依托单位: SAFELYYOU, INC.
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-30 至 2021-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10020322
• 关键词: Accidents; Address; Adult; Affect; Aging; Algorithms; Alzheimer' s Disease; Alzheimer' s disease related dementia; Artificial Intelligence; Assisted Living Facilities; Automation; Automobile Driving; Awareness; Beds; Caregivers; Caring; Cessation of life; Clinical; Clinical Trials; Cognitive; Collection; Communities; Control Groups; Data; Dementia; Detection; Devices; Discipline of Nursing; Disease; Emergency Situation; Emergency department visit; Emergency medical service; Ensure; Event; Family; Floor; Fracture; Future; Goals; Health Care Costs; Health care facility; Hospital Costs; Hospitalization; Hour; Human; Individual; Intervention; Lead; Letters; Life; Measures; Medical; Memory; Monitor; Morbidity - disease rate; Notification; Occupational Therapist; Outcome; Participant; Persons; Phase; Phase I Clinical Trials; Population; Population Control; Prevention; Privacy; Quality of Care; Quality of life; Randomized; Recommendation; Research; Risk; Risk Factors; Robotics; Safety; Sample Size; San Francisco; Series; Services; Skilled Nursing Facilities; Small Business Innovation Research Grant; Social Network; Specificity; Statistical Data Interpretation; Stream; System; Technology; Time; United States National Institutes of Health; Visit; Waiting Lists; base; care costs; cohort; cost; deep learning; dementia care; design; experience; falls; frontier; human-in-the-loop; improved; intelligent algorithm; memory care; mortality; novel; phase 1 study; prevent; sensor; standard of care; symposium; trend; web portal

Abstract In the US, Alzheimer’s disease (AD) is the single most expensive disease, the only one in the top six for which the number of deaths is increasing. The greatest costs are hospitalizations, where falls are the largest culprit, and frequent need for assistance with daily life activities. A fall safety system shows the potential to reduce costs and increase quality of care by reducing the likelihood of emergency events (e.g., detecting falls before a fracture occurs, reducing the number of repeat falls). Unfortunately, no fall detection and prevention technology has been developed specifically for the needs of dementia care where individuals (1) fall more frequently and (2) often cannot tell care staff how they fell, leading to increased use of Emergency Medical Services (EMS) when falls are unwitnessed to ensure affected individuals are safe. Our goal is to perform a randomized wait-list control clinical trial (n=460) of SafelyYou Guardian, an online fall detection system with wall-mounted cameras to automatically detect falls for residents with AD and related dementias (ADRD). The automation is based on algorithms that push the frontier of deep learning, a subfield of Artificial Intelligence (AI), with a human-in-the- loop (HIL). SafelyYou Guardian is designed to primarily operate in memory care facilities (defined herein as assisted living and skilled nursing facilities providing ADRD care). Deep learning has already revolutionized several fields: robotics, self-driving cars, social networks in particular. Our approach is anchored in novel algorithms developed at the Berkeley AI Research Lab (BAIR) and extended by SafelyYou for real-time detection of rare events in video. The HIL is operating from a call center, confirms the fall detection alerts provided by our artificial intelligence algorithms, and places a call to the communities, so an intervention can happen within minutes of the fall detection. Subsequently, an Occupational Therapist (OT) working from our office in San Francisco reviews the fall videos with the front-line staff over video conference and using our web portal to make recommendations on how to re-organize the resident space (intervention) to prevent future falls. We leverage our HIL paradigm, in which our deep learning approach identifies and pre-filters falls with high sensitivity followed by a human who confirms the fall with high specificity and calls the communities in case of detected fall. This project leverages past small scale clinical and technical pilots including 87 residents from 11 partner communities, and our experience with paid commitments for 480 residents from three partner networks. Past pilots leading to this NIH Phase II proposal include: · Pilot 1: Technical proof of concept with healthy subjects (200 acted falls). · Pilot 2: We demonstrated acceptance of privacy/safety tradeoffs by residents, family and staff, through the collection of 3 months of video data at WindChime of Marin, our first partner facility; we identified 4 total hours of fall data. This led to clinical benefits including an 80% fall reduction through the intervention of OT. · Pilot 3: We demonstrated scalability and acceptance by deploying the system in 11 communities, for 87 residents monitored by our system (offline, no HIL intervention). · Pilot 4: Small scale NIH Phase I clinical trial. We demonstrated the ability to perform real-time fall detection, with real-time intervention of the HIL through our partner company Magellan-Solutions which provides the 24/7 monitoring service for the facilities. We demonstrated that 93% of 89 falls were detected, that time on the ground was reduced by 42%, that the likelihood of EMS use was 50% lower with video available, and the that total facility falls including participants and non-participants decreased by 38%. The trial proposed for this NIH SBIR Phase II will provide clinical evidence that the preliminary trends observed experimentally (pilot 2) and at small scale (pilot 4) are true phenomena. It will use a wait-list control population (230 residents) to be compared to the population monitored with SafelyYou Guardian (230 residents). After crossover, the wait-list population will also benefit from the technology and be compared to itself before crossover.

摘要 在美国，阿尔茨海默病（AD）是最昂贵的疾病，也是美国唯一一种 死亡人数上升的前六名。最大的费用是住院， 其中福尔斯是最大的罪魁祸首，并且日常生活活动经常需要帮助。下降 安全系统显示出通过减少 紧急事件的可能性（例如，在骨折发生之前检测福尔斯， 重复福尔斯数）。不幸的是，还没有跌倒检测和预防技术 专为痴呆症护理的需要而开发，其中个人（1）更频繁地跌倒 （2）经常不能告诉护理人员他们是如何摔倒的，导致急诊医疗的使用增加 当福尔斯下落无人目击时，提供紧急医疗服务（EMS），以确保受影响人员的安全。 我们的目标是对SafelyYou Guardian进行随机等待列表对照临床试验（n=460）， 一个在线跌倒检测系统，带有壁挂式摄像头，可自动检测福尔斯， AD和相关痴呆（ADRD）患者。自动化是基于算法， 推动深度学习的前沿，人工智能（AI）的一个子领域，与人类在- 循环（HIL）。SafelyYou Guardian主要用于记忆保健设施 （此处定义为提供ADRD护理的辅助生活和熟练护理设施）。深 学习已经彻底改变了几个领域：机器人，自动驾驶汽车，社交网络， 特别的。我们的方法基于伯克利人工智能研究所开发的新算法 实验室（BAIR）和SafelyYou扩展，用于实时检测视频中的罕见事件。所述HIL 是从呼叫中心操作，确认我们的人工提供的跌倒检测警报， 智能算法，并致电社区，因此可以进行干预 几分钟内发现的随后，一名职业治疗师（OT）从 我们在旧金山弗朗西斯科的办公室通过视频会议与一线工作人员一起审查秋季视频 并利用我们的门户网站就如何重新组织居住空间提出建议 （干预）以防止将来的福尔斯。我们利用我们的HIL范式，其中我们的深度学习 该方法以高灵敏度识别和预过滤福尔斯，然后由人确认 具有高度特异性的跌倒，并在检测到跌倒的情况下呼叫社区。这个项目 利用过去的小规模临床和技术试点，包括来自11个合作伙伴的87名住院医生 社区，以及我们为来自三个合作伙伴的480名居民提供有偿承诺的经验 网络.导致NIH第二阶段提案的过去试点包括： ·试点1：健康受试者的概念技术验证（200次动作福尔斯）。 ·试点2：我们证明了居民、家庭和社区对隐私/安全权衡的接受程度。 工作人员，通过收集3个月的视频数据在风铃的马林，我们的第一个 合作机构;我们确定了总计4小时的跌倒数据。这导致了临床效益 包括通过OT的干预减少了80%的跌倒。 ·试点3：我们通过在11年内部署系统，展示了可扩展性和可接受性。 社区，由我们的系统监测的87名居民（离线，无HIL干预）。 ·试点4：小规模NIH I期临床试验。我们证明了我们的能力 实时跌倒检测，通过我们的合作伙伴实时干预HIL Magellan-Solutions公司为设施提供24/7监控服务。 我们证明了89次福尔斯中有93%被检测到，在地面上的时间是 减少了42%，使用EMS的可能性降低了50%， 包括参与者和非参与者在内的总贷款额福尔斯下降了38%。 这项NIH SBIR II期拟议的试验将提供临床证据，证明初步的 在实验（试验2）和小规模（试验4）中观察到的趋势是真实的现象。它将 使用等待列表控制人口（230名居民）与监测人口进行比较 与SafelyYou Guardian（230名居民）合作。交叉后，等待名单上的人口也将 从技术中受益，并在交叉之前与自身进行比较。

项目成果

Real-time video detection of falls in dementia care facility and reduced emergency care.

实时视频检测痴呆症护理机构中的跌倒情况并减少紧急护理。

• 发表时间: 2019
• 期刊: The American journal of managed care
• 作者: Xiong,GlenL;Bayen,Eleonore;Nickels,Shirley;Subramaniam,Raghav;Agrawal,Pulkit;Jacquemot,Julien;Bayen,AlexandreM;Miller,Bruce;Netscher,George
• 通讯作者: Netscher,George