Division of Neuromuscular Medicine, Department of Neurology, Los Angeles Medical Center, University of California, 300 Medical Plaza B200, Los Angeles, CA, 90095, USA.
SpeedyAI, Inc, 19940 Ridge Estate Ct, Walnut, CA, 91789, USA.
Sci Rep. 2021 Jan 28;11(1):2471. doi: 10.1038/s41598-021-81115-9.
Falls are a leading cause of unintentional injuries and can result in devastating disabilities and fatalities when left undetected and not treated in time. Current detection methods have one or more of the following problems: frequent battery replacements, wearer discomfort, high costs, complicated setup, furniture occlusion, and intensive computation. In fact, all non-wearable methods fail to detect falls beyond ten meters. Here, we design a house-wide fall detection system capable of detecting stumbling, slipping, fainting, and various other types of falls at 60 m and beyond, including through transparent glasses, screens, and rain. By analyzing the fall pattern using machine learning and crafted rules via a local, low-cost single-board computer, true falls can be differentiated from daily activities and monitored through conventionally available surveillance systems. Either a multi-camera setup in one room or single cameras installed at high altitudes can avoid occlusion. This system's flexibility enables a wide-coverage set-up, ensuring safety in senior homes, rehab centers, and nursing facilities. It can also be configured into high-precision and high-recall application to capture every single fall in high-risk zones.
跌倒（Falls）是意外伤害的主要原因，如果未能及时发现和治疗，可能导致严重残疾甚至致命后果。当前的检测方法存在以下一个或多个问题：频繁更换电池、佩戴者不适、成本高、设置复杂、家具遮挡以及需要大量计算。事实上，所有非穿戴式方法都无法检测到超过 10 米外的跌倒。在这里，我们设计了一个全屋范围的跌倒检测系统，能够在 60 米及以上的距离检测到绊倒、滑倒、昏厥和各种其他类型的跌倒，包括通过透明玻璃、屏幕和雨水进行检测。通过使用机器学习分析跌倒模式，并通过本地低成本单板计算机上的定制规则进行分析，该系统能够区分真正的跌倒和日常活动，并通过常规监控系统进行监测。通过在一个房间内设置多个摄像头或在高处安装单个摄像头，可以避免遮挡。该系统具有灵活性，能够实现广泛的覆盖范围，确保老年人家庭、康复中心和护理设施的安全。它还可以配置为高精度和高召回率的应用，以捕捉高风险区域的每一次跌倒。
