Understanding and predicting falls of people living with dementia

福尔斯是一个严重的健康和社会保健问题，因为它们与缺乏社会互动（外出）、住院、髋部骨折、死亡、固定等有关。有已发表的证据表明，认知障碍，特别是痴呆症，是福尔斯的风险因素，因为双重任务和分享注意力的能力降低。最近的研究集中在开发基于AI的方法来检测福尔斯。然而，这项研究还需要在更广泛的社会和心理背景下进行。该项目旨在通过将计算机科学和心理学方法结合起来进行研究来解决这一问题，该研究旨在了解跌倒机制，以及人们发生福尔斯的更广泛背景。这一新的认识应有助于制定干预战略。关键的研究问题可能包括：能否开发基于人工智能的方法来检测移动性和稳定性的损失，并将其部署在真实的环境中？什么是与检测到的福尔斯相关的环境因素，以及有利于注意力转移的环境因素？是否可以设计干预措施来缓解这些确定的因素？成功的申请人将设计基于AI的方法来检测步态和姿势的不稳定性，这些不稳定性表明跌倒风险较高。这些方法将基于，并扩展，主要主管的成功的作品，在家庭环境中识别偏离学习的“正常”模型的运动。他们将被用来确定家庭中存在较高跌倒风险的区域。将招募65岁以上的早期和中期痴呆症患者，在家中或护理院进行研究。焦点小组和神经心理学测试将有助于将检测到的易跌倒区域与跌倒风险增加的可能原因联系起来，例如注意力转移的原因。在重点小组会议期间也可讨论减少或抑制这些因素的可能干预措施，迭接发展战略将有利于用户参与方法的设计和评价。这将确保开发的方法可用于真实的场景并实现影响。HCRW将帮助直接接触威尔士的政策制定者，以帮助影响政策，并在威尔士产生最大的影响。

Falls are a serious health and social care concern, as they are related to lack of social interaction (going out), hospitalisation, hip fractures, death, immobilisation, etc. There is published evidence that cognitive impairment, especially dementia, is an increased risk factor for falls due to reduced ability to dual-task and share attention. Recent research has focused on developing AI-based methods for detecting falls. However, this research is yet to be set in wider social and psychological contexts.The project aims at addressing this shortage by combining computer science and psychology methods in a study that seeks to understand the fall mechanisms, and the wider context within which people have falls. This new understanding should allow devising intervention strategies. Key research questions are likely to include:Can AI-based methods be developed to detect mobility and stability losses, and be deployed in real environments?What are the environmental factors associated with the detected falls, and that favour attention shift?Can interventions be designed to alleviate these identified factors?The successful applicant will design AI-based methods to detect instabilities in gait and posture that are indicative of higher fall risk. These methods will be based on, and extend, the main supervisor's successful works on identifying movements that deviate from a learnt 'normal' model in home environments. They will be used to identify areas in homes that present a higher risk of fall.Participants aged 65+ with early and middle dementia stages will be recruited, for studies either at home or in care-homes. Focus groups and neuropsychological tests will help linking the detected fall-prone areas with the likely causes of increased fall risk, such as causes of attention shifts. Possible interventions to reduce or suppress these elements may be discussed during focus groups as well.An iterative development strategy will favour user-involvement in the design and evaluation of methods. This will ensure that the developed methods are useable in real scenarios and realise impact. HCRW will help gain direct exposure to Welsh policymakers to help influencing policies and enable a maximum impact in Wales.