Utilizing Technology and AI Approaches to Facilitate Independence and Resilience in Older Adults

利用技术和人工智能方法促进老年人的独立性和适应能力

• 批准号: 10652011
• 负责人: Peter M. Abadir
• 金额: $ 27.81万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-30 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10652011
• 关键词: Addendum; Adverse effects; Affect; Agitation; Alzheimer' s disease related dementia; American; Anger; Antipsychotic Agents; Artificial Intelligence; Assisted Living Facilities; Awareness; Beds; Behavior; Boredom; Brain; Budgets; COVID-19; Calendar; Canada; Caregivers; Caring; Cessation of life; Classification; Client; Clinical; Code; Collaborations; Computer software; Country; Data; Data Analyses; Data Set; Databases; Dementia; Dementia caregivers; Depressed mood; Detection; Development; Development Plans; Direct Costs; Discipline of Nursing; Disinfection; Distress; Docking; Drug Prescriptions; Ear; Economics; Effectiveness; Elderly; Electronic Mail; Emerging Technologies; Emotional; Emotions; Equipment and supply inventories; Evaluation; Eye; Face; Facial Expression; Facial Muscles; Facilities and Administrative Costs; Family; Feeling; First Name; France; Frequencies; Fright; Fringe Benefit; Funding; Funding Agency; Future; Generations; Gifts; Goals; Grant; Happiness; Health; Health Technology; Healthcare; Home; Hour; Human; Human Resources; Impaired cognition; Industry; Institution; Institutional Review Boards; Instruction; Internet; Intervention; Israel; Japan; Knowledge; Language; Last Name; Lead; Life; LinkedIn; Loneliness; Long-Term Care; Machine Learning; Manuscripts; Maps; Measurement; Mediation; Memory; Mentors; Mind; Movement; Muscle; Music; National Institute on Aging; North America; Nurses; Nursing Homes; Participant; Patients; Pattern; Personal Satisfaction; Persons; Pharmaceutical Preparations; Pilot Projects; Play; Population; Positioning Attribute; Process; Provider; Psychologist; Public Health Schools; Quality of Care; Quality of life; Recording of previous events; Research; Research Assistant; Research Personnel; Resources; Robot; Role; Sampling; Schedule; Science; Sensory; Services; Side; Small Business Innovation Research Grant; Software Engineering; Standardization; Stress; Structure; Supervision; System; Systems Analysis; Techniques; Technology; Telephone; Testing; Thinking; Time; TimeLine; Training; United States; United States National Institutes of Health; Universities; Validation; Verbal Behavior; Visit; Visiting Nurse; Work; base; behavior measurement; college; community living; computer science; cost; cost effective; design; digital health; effectiveness trial; engineering design; experience; fall risk; feasibility testing; fitness; healthy aging; human subject; implementation barriers; improved; indexing; innovation; instrument; loved ones; member; movement analysis; neuropsychiatric symptom; neuropsychiatry; off-label use; pilot test; programs; recruit; research and development; resilience; software development; staff intervention; tool; translational impact; usability; web site

Detecting Agitation in Persons with AD/ADRD in a Nursing Home Using AI-based Facial Expression Analysis ABSTRACT 50% of nursing home residents have moderate or severe cognitive impairment. Persons with advanced AD/ADRD are often no longer able to communicate verbally. Agitation, which is found in 80% of residents with AD/ADRD, can pose a major problem for the resident, their family and professional caregivers. The inability to successfully manage AD/ADRD behaviors, often results in increased burden on caregivers and use of antipsychotic and other psychotropic medications which may have severe adverse effects. COVID-19 has also increased agitation incidents among residents with AD/ADRD and increased distress levels in both residents and caregivers. In this project we will integrate an AI-based facial expression analysis software with an autonomous navigating robot and validate that the integrated system can visit nursing home residents with advanced AD/ADRD and detect agitation.

使用基于AI的面部表情检测养老院中AD/ADRD患者的躁动 分析 摘要 50%的养老院居民有中度或重度认知障碍。具有高级 AD/ADRD通常不再能够口头沟通。焦虑，这是发现在80%的 患有AD/ADRD的居民可能会给居民、他们的家人和专业人员带来重大问题。 照顾者无法成功管理AD/ADRD行为，通常会导致负担增加 对照顾者和使用抗精神病药物和其他精神药物， 不良影响COVID-19还增加了AD/ADRD居民的躁动事件， 居民和照顾者的痛苦程度增加。 在这个项目中，我们将集成一个基于人工智能的面部表情分析软件与一个自主的 导航机器人，并验证集成系统可以访问养老院居民与先进的 AD/ADRD和检测搅拌。