Autonomous Navigating Robot for Detecting Falls and Risk of Falls in Nursing Home Residents with Alzheimer's Disease /ADRD
Autonomous Navigating Robot for Detecting Falls and Risk of Falls in Nursing Home Residents with Alzheimer's Disease /ADRD
批准号:
10698704
负责人:
Yuval Malinsky
金额:
$45.0万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2023
资助国家:
美国
项目状态:
已结题
起止时间:
2023-09-18 至 2024-08-31
关键词:
AcuteAdultAgreementAlgorithmic AnalysisAlzheimer&aposs DiseaseAlzheimer&aposs disease related dementiaArtificial IntelligenceBedsCaringCessation of lifeCommunicationComputer softwareDarknessDementiaDetectionElderlyEmployeeEyeFaceFall preventionFeasibility StudiesGoalsGrantHealth Insurance Portability and Accountability ActHip FracturesImpaired cognitionInjuryInsuranceInterventionLightMindMorbidity - disease rateNursing HomesOutcome MeasurePatientsPerformancePersonsPhasePopulationPopulations at RiskPosturePrivacyPsyche structureQuality of lifeReactionRecoveryRobotScheduleSecureSurveysSystemTechnologyTestingTimeTrainingVisitage groupdetection platformfall riskfallsfear of fallingfeasibility testingfirst responderimage processingimprovedmortalitypeerprimary outcomeprogramssatisfactionsecondary outcomeusabilityvirtual visit
中文摘要
摘要
每年有一半到四分之三的养老院(NH)居民倒下。约5%的
65岁及以上的成年人住在养老院,但养老院居民占20%,
这个年龄组的福尔斯死亡人数。50%的养老院居民有中度或重度
认知障碍患有阿尔茨海默病或ADRD的老年人更容易跌倒,
没有认知障碍的同龄人。福尔斯通常会造成严重的后果,尤其是在
年老体弱的居民每10名跌倒的居民中就有一人受到严重的相关伤害,
每年有65,000名患者遭受髋部骨折。没有受伤的居民通常会发展为
害怕跌倒,导致自我限制活动,导致能力下降
功能和生活质量下降。福尔斯对设施有重大影响,包括
对跌倒者的护理水平提高,工作人员的文书工作增加,调查结果不佳,
诉讼和更高的保险费。大多数福尔斯(66%-75%)发生在住院医生的房间里,
近一半(48%)的人最终受伤。在跌倒后立即治疗对患者的健康至关重要。
从身体和精神上的跌倒中恢复过来。现有的跌倒检测系统使用静态
住户房间内的摄像头,侵犯住户隐私,
隐藏在摄像机前或在黑暗中的福尔斯。工作人员严重短缺,
疗养院可能会造成跌倒在一段时间内未被发现的情况。
拟议的研究涉及一个负担得起的自主导航机器人,
已成功用于养老院痴呆症单位,红外摄像机,图像
处理软件和人工智能是一个额外的“一组眼睛”,以巡逻走廊和房间,
护理院的居民,并检测福尔斯和福尔斯的风险,并提醒工作人员,使他们能够
立即处理。
英文摘要
ABSTRACT
Between half and three-quarters of nursing home (NH) residents fall each year. About 5% of
adults 65 and older live in nursing homes, but nursing home residents account for 20% of
deaths from falls in this age group. 50% of nursing home residents have moderate or severe
cognitive impairment. Older adults with Alzheimer’s Disease or ADRD are more likely to fall than
their peers without cognitive impairment. Falls often have serious consequences, especially in
frail older residents. One in every 10 residents who fall has a serious related injury and about
65,000 patients suffer hip fracture each year. Residents who fall without injury often develop a
fear of falling that leads to self-imposed limitation of activity leading to a decrease in the ability
to function and a reduced quality of life. Falls have major consequences for facilities including
increased levels of care required for fallers, increased paperwork for staff, poor survey results,
lawsuits and higher insurance premiums. Most falls (66%-75%) occur in the resident’s room and
almost half (48%) end in an injury. Treating a person shortly after falling is critical to the
recovery from the fall both physically and mentally. Existing fall detection systems use static
cameras in the rooms of residents which violate resident privacy and may be unable to detect
falls that are hidden from the camera or that occur in the dark. The acute shortage of staff in
nursing homes may create situations where a fall is undetected for a while.
The proposed study involves integration of an affordable autonomous navigating robot which
has been used successfully in nursing home dementia units, with an infra-red camera, image
processing software and AI to be an additional “set of eyes” to patrol the corridors and rooms of
nursing homes residents and to detect falls and risk of falls and alert staff so they can be
handled immediately.
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会议论文
Autonomous Navigating Telepresence Robot for Alleviating Loneliness and Engaging Nursing Home Residents with and without Dementia
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批准号:10255485
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项目类别:
-
资助金额:$50.0万
-
财政年份:2021
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负责人:Yuval Malinsky
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依托单位:
Integrated Web-Based Customer Engagement, Physical Exercise, and Coaching Platform for Older Adults
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批准号:9408822
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项目类别:
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资助金额:$21.83万
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财政年份:2017
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负责人:Yuval Malinsky
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依托单位:
海外基金