Using Structured Light Sensing with Machine Learning to Detect Unwitnessed In-Home Falls

使用结构光传感和机器学习来检测无人目击的家庭跌倒

• 批准号: 10818017
• 负责人: PAUL GIBSON
• 金额: $ 76.86万
• 依托单位: APPLIED UNIVERSAL DYNAMICS CORPORATION
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-15 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10818017
• 关键词: 3-Dimensional; Accident and Emergency department; Adult; Affect; Caregivers; Cessation of life; Classification; Communication Methods; Computer software; Craniocerebral Trauma; Darkness; Detection; Development; Devices; Elderly; Electronics; Emergency Situation; Emergency response; Endowment; Engineering; Evaluation; Floor; Goals; Healthcare; Hip Fractures; Home; Hospitalization; Hospitals; Image; Impaired cognition; Incidence; Injury; Internet; Lead; Learning; Life Expectancy; Light; Machine Learning; Measures; Medical Care Costs; Memory Loss; Minnesota; Monitor; Motion; Patients; Performance; Persons; Phase; Population; Principal Investigator; Privacy; Research; Risk; School Nursing; Signal Transduction; Stream; Structure; System; Technology; Testing; Time; Training; Traumatic Brain Injury; Universities; Validation; Vendor; Visible Radiation; Visit; Work; aged; cost; design; detection platform; experience; falls; forgetting; healthy aging; human study; innovation; machine learning algorithm; mid-career faculty; professor; prototype; research and development; sensor; software development; success; wireless; wireless communication

Project Summary/Abstract Older adults are disproportionately affected by falls. Older adults who have memory loss (mild to moderate cognitive impairment) can forget to wear wireless alert pendants or wristbands that are used in case they fall in their home. Falls among adults 65 and older caused over 34,000 deaths in 2019, making it the leading cause of injury death for that group. Older adult falls cost $50 billion in medical costs annually. Of those who fall, many suffer serious injuries, such as hip fractures and head traumas, which reduces their mobility, independence, and life expectancy. Studies have found an increased risk of complications associated with prolonged periods of lying on the floor following a fall. Older adults living alone or with memory loss are at the greatest risk of delayed assistance following a fall and cannot always be counted on to use their wearable emergency alert button. A low-cost, unobtrusive system capable of automatically detecting and alerting falls in the homes of older adults living alone or those with mild to moderate cognitive impairment, could help significantly reduce the incidence of delayed assistance after a fall. This phase II project, building on a successful phase I project, will develop an innovative new in-home fall monitoring system that solves many practical problems with existing systems. The technical approach uses structured light sensing (SLS) that creates 3D point clouds of a scene to allow detection of motion sequences using machine learning (ML) algorithms which will allow for the automatic detection of a person’s fall. There are multiple benefits of this approach for the target users: 1. The person is not required to carry or wear an electronic device that might be forgotten to be worn. 2. No action is required to be taken by the person after a fall. 3. The system does not use visible light video that would create privacy concerns for the person. 4. The system can work in darkness or very low light unlike visible light camera-based approaches. 5. The system is unobtrusive and works with existing Personal Emergency Response Systems (PERS), with minimal or no active user interaction. The SLS fall detection system is intended to work with multiple vendors of in-home alert systems. It will operate in lieu of or in parallel with, wearable buttons to signal an alert. The proposed system would be used if caregivers determine that a wearable button is not an adequate solution for the person being monitored. The proposed devices will be mounted high on the wall of each room and will wirelessly communicate to a central device in the home. The central device will send the alert to the in-home alert system upon detecting a fall. The proposed solution will not require any Internet connectivity. The out-of-home communication method is provided by the chosen vendor of the in-home alert system.

项目摘要/摘要 老年人受跌倒的影响不成比例。有记忆力损失的老年人（温和到现代的人 认知障碍）可能会忘记佩戴无线警报吊坠或腕带，以防其掉入 他们的家。 65岁及以上的成年人跌倒在2019年造成34,000多人死亡，这是主要原因 该组受伤死亡。老年人瀑布每年耗资500亿美元的医疗费用。那些跌倒的人 许多人受到严重伤害，例如髋部骨折和头部创伤，从而降低了它们的活动性， 独立和预期寿命。研究发现与并发症相关的并发症风险增加 秋天后，长时间躺在地板上。老年人独自生活或记忆力丧失 跌倒后延迟援助的最大风险，不能总是指望使用其可穿戴 紧急警报按钮。一种能够自动检测和警报的低成本，不引人注目的系统落在 老年人独自生活的房屋或患有轻度至中度认知障碍的房屋，可能会有所帮助 大幅度减少跌倒后延迟援助的事件。 该阶段第二阶段项目是在成功的I期项目上建立的，将开发创新的新家庭秋季 监视系统可以解决现有系统的许多实际问题。技术方法使用 结构化的光传感（SLS）创建场景的3D点云以允许检测运动序列 使用机器学习（ML）算法，该算法将允许自动检测一个人的跌倒。有 对于目标用户，这种方法的多重好处：1。不需要携带或佩戴该人 电子设备可能会忘记磨损。 2。 落下。 3。系统不使用可见的轻型视频，这会给人带来隐私问题。 4。系统不使用可见的轻型视频，这会给人带来隐私问题。 与可见光的基于摄像头的方法不同，系统可以在黑暗或非常低的光线下工作。 5。系统是 不受欢迎，并与现有的个人应急响应系统（PERS）一起工作，没有最小 主动用户互动。 SLS秋季检测系统旨在与多个家庭警报系统的供应商一起使用。它将运行 以可穿戴的按钮代替或并行，以发出警报。如果建议的系统将被使用 护理人员确定可穿戴按钮对于被监控的人来说不是足够的解决方案。这 建议的设备将高高安装在每个房间的墙壁上，并将无线通信与中央通信 家中的设备。中央设备将在检测到跌落后将警报发送到室内警报系统。 建议的解决方案将不需要任何互联网连接。户外交流方法是 由所选的家庭警报系统的供应商提供。