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

摘要 在美国，阿尔茨海默病(AD)是唯一一种最昂贵的疾病，也是世界上唯一一种 死亡人数不断增加的前六名。最大的费用是住院费用， 其中跌倒是最大的罪魁祸首，经常需要日常生活活动的帮助。摔了一跤 安全系统显示了降低成本和提高护理质量的潜力 发生紧急事件的可能性(例如，在骨折发生前检测到跌倒，减少 重复下跌的次数)。不幸的是，还没有发现和预防跌倒的技术 专为痴呆症护理的需要而开发，在这些需求中，个人(1)经常摔倒 以及(2)往往无法告诉医护人员他们是如何跌倒的，从而导致更多人使用急救医疗 在无人目击坠落时提供服务(EMS)，以确保受影响人员的安全。 我们的目标是进行SafelyYou Guardian的随机等待名单对照临床试验(n=460)， 一种带有壁挂式摄像头的在线跌倒检测系统，可以自动检测跌倒 阿尔茨海默病及相关痴呆患者(ADRD)。自动化是基于以下算法的 以人在人的方式推动深度学习的前沿，这是人工智能(AI)的一个子领域 循环(HIL)。SafelyYou Guardian的设计主要是在记忆护理设施中运行 (此处定义为提供ADRD护理的辅助生活和熟练护理设施)。深沉 学习已经给几个领域带来了革命性的变化：机器人、自动驾驶汽车、社交网络 很特别。我们的方法以伯克利人工智能研究中心开发的新算法为基础 Lab(Bair)，由SafelyYou扩展，用于实时检测视频中的罕见事件。HIL 从呼叫中心操作，确认了由我们的人工提供的跌倒检测警报 智能算法，并向社区发出呼叫，因此可能会发生干预 在坠落检测到的几分钟内。随后，一名工作于 我们在旧金山的办公室通过视频会议与一线员工一起回顾秋季视频 并使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)以防止未来的下跌。我们利用我们的HIL范式，在这个范式中，我们的深度学习 该方法以高灵敏度识别和预过滤坠落，随后由确认 跌倒具有很高的特异性，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，以及我们从三个合作伙伴那里为480名居民提供付费承诺的经验 网络。导致NIH第二阶段提案的过去的试点包括： ·试点1：对健康受试者进行概念技术验证(200次动作跌倒)。 ·试点2：我们展示了居民、家庭和 工作人员，通过收集3个月的视频数据在风铃马林，我们的第一个 合作伙伴设施；我们确定了总计4小时的秋季数据。这带来了临床上的好处 包括通过OT的干预减少80%的跌倒。 ·试点3：通过在11个月部署该系统，我们展示了可扩展性和接受度 社区，由我们的系统监控的87名居民(离线，无HIL干预)。 ·试点4：小规模NIH I期临床试验。我们展示了表演的能力 实时跌倒检测，通过我们的合作伙伴对HIL进行实时干预 麦哲伦解决方案公司，为设施提供全天候监测服务。 我们证明，89次坠落中有93%被探测到，地面上的时间是 减少了42%，使用EMS的可能性降低了50%，并且 包括参与者和非参与者在内的设施总降幅下降了38%。 为NIH SBIR第二阶段提出的试验将提供临床证据，表明 实验(试点2)和小规模(试点4)观察到的趋势是真实的现象。会的 使用等待名单控制人口(230名居民)与监测人口进行比较 与SafelyYou Guardian(230名居民)合作。在跨界后，轮候名单上的人口也将 从技术中获益，并在跨界之前与自己进行比较。

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.