Fall Detection and Prevention for Memory Care through Real-Time Artificial Intelligence Applied to Video
通过应用于视频的实时人工智能进行跌倒检测和预防以实现记忆护理
基本信息
- 批准号:10020322
- 负责人:
- 金额:$ 49.36万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2017
- 资助国家:美国
- 起止时间:2017-09-30 至 2021-04-30
- 项目状态:已结题
- 来源:
- 关键词:AccidentsAddressAdultAffectAgingAlgorithmsAlzheimer&aposs DiseaseAlzheimer&aposs disease related dementiaArtificial IntelligenceAssisted Living FacilitiesAutomationAutomobile DrivingAwarenessBedsCaregiversCaringCessation of lifeClinicalClinical TrialsCognitiveCollectionCommunitiesControl GroupsDataDementiaDetectionDevicesDiscipline of NursingDiseaseEmergency SituationEmergency department visitEmergency medical serviceEnsureEventFamilyFloorFractureFutureGoalsHealth Care CostsHealth care facilityHospital CostsHospitalizationHourHumanIndividualInterventionLeadLettersLifeMeasuresMedicalMemoryMonitorMorbidity - disease rateNotificationOccupational TherapistOutcomeParticipantPersonsPhasePhase I Clinical TrialsPopulationPopulation ControlPreventionPrivacyQuality of CareQuality of lifeRandomizedRecommendationResearchRiskRisk FactorsRoboticsSafetySample SizeSan FranciscoSeriesServicesSkilled Nursing FacilitiesSmall Business Innovation Research GrantSocial NetworkSpecificityStatistical Data InterpretationStreamSystemTechnologyTimeUnited States National Institutes of HealthVisitWaiting Listsbasecare costscohortcostdeep learningdementia caredesignexperiencefallsfrontierhuman-in-the-loopimprovedintelligent algorithmmemory caremortalitynovelphase 1 studypreventsensorstandard of caresymposiumtrendweb portal
项目摘要
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.
摘要
项目成果
期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Real-time video detection of falls in dementia care facility and reduced emergency care.
实时视频检测痴呆症护理机构中的跌倒情况并减少紧急护理。
- DOI:
- 发表时间:2019
- 期刊:
- 影响因子:0
- 作者:Xiong,GlenL;Bayen,Eleonore;Nickels,Shirley;Subramaniam,Raghav;Agrawal,Pulkit;Jacquemot,Julien;Bayen,AlexandreM;Miller,Bruce;Netscher,George
- 通讯作者:Netscher,George
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Glen Xiong其他文献
Glen Xiong的其他文献
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{{ truncateString('Glen Xiong', 18)}}的其他基金
Comparison of Asynchronous Telepsychiatry Alongside Synchronous Telepsychiatry in Skilled Nursing Facilities (CATALYST)
熟练护理机构中异步远程精神病学与同步远程精神病学的比较 (CATALYST)
- 批准号:
9920070 - 财政年份:2017
- 资助金额:
$ 49.36万 - 项目类别:
Comparison of Asynchronous Telepsychiatry Alongside Synchronous Telepsychiatry in Skilled Nursing Facilities (CATALYST)
熟练护理机构中异步远程精神病学与同步远程精神病学的比较 (CATALYST)
- 批准号:
9364336 - 财政年份:2017
- 资助金额:
$ 49.36万 - 项目类别:
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