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.

项目总结/摘要 老年人受福尔斯的影响更大。有记忆力丧失的老年人（轻度至中度 认知障碍）可能会忘记佩戴无线警报吊坠或腕带，以防他们落入水中 他们的家2019年，65岁及以上成年人的福尔斯摔倒造成34，000多人死亡，成为主要原因 这一组的伤亡人数。老年人福尔斯跌倒每年花费500亿美元的医疗费用。在那些倒下的人中， 许多人遭受严重伤害，如髋部骨折和头部创伤，这降低了他们的活动能力， 独立性和预期寿命。研究发现， 摔倒后长时间躺在地板上。独居或记忆丧失的老年人在 跌倒后延迟援助的最大风险，并且不能总是指望使用他们的可穿戴设备 紧急警报按钮一种低成本、不显眼的系统，能够自动检测和报警福尔斯跌倒， 独居的老年人或有轻度至中度认知障碍的人的家中， 显著降低跌倒后延迟援助的发生率。 这个第二阶段的项目，在一个成功的第一阶段项目的基础上，将开发一个创新的新的家庭秋季 监控系统解决了现有系统的许多实际问题。技术方法使用 结构光传感（SLS），其创建场景的3D点云以允许检测运动序列 使用机器学习（ML）算法，可以自动检测人的跌倒情况。有 这种方法对目标用户的多重好处：1.该人无须携带或佩戴 电子设备，可能会被忘记佩戴。2.不需要采取任何行动后，该人 秋天3.该系统不使用可见光视频，这会给人带来隐私问题。4.的 与基于可见光照相机方法不同，系统可以在黑暗或非常低的光下工作。5.该系统 不显眼，并与现有的个人应急响应系统（PERS），最小或没有 活跃的用户交互。 SLS跌倒检测系统旨在与家庭警报系统的多个供应商合作。热线电话的服务时间 代替或与可佩戴按钮并行地发出警报信号。拟议的系统将用于以下情况： 护理人员确定可佩戴按钮对于被监视的人来说不是适当的解决方案。的 拟议中的设备将安装在每个房间的墙上，并将无线通信到中央 设备在家里。一旦检测到跌倒，中央设备将向家庭警报系统发送警报。的 建议的解决方案将不需要任何互联网连接。所述家庭外通信方法， 由家用警报系统的选定供应商提供。