Home monitoring of patients with early and late stages of dementia

早期和晚期痴呆症患者的家庭监测

• 批准号: 1904043
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1904043
• 关键词: Home; monitoring; patients; early; late

This project falls within the EPSRC Artificial Intelligence TechnologiesIn recent years, various changes in modern societies have resulted in a sig-nificant number of people spending a considerable amount of their day in their home environments. More and more people turn to self-employment, while businesses seem to be exploring recent studies related to increasing productivity, by allowing their employees to have flexible working times, of-ten working from home. At the same time, the advances in medicine and the increase in life expectancy have resulted in the phenomenon of the ageing population; even though nowadays older retired adults normally have many more years to live, they are often faced with age-related diseases, such as arthritis, Parkinson's, dementia or geriatric depression, that might keep them at home as they become more severe.In this DPhil project, the task of monitoring human behaviour in their home environment employing widely available, low-cost and light-weight sensors is tackled. In particular, we will explore the following research directions: 1. An algorithm for room identification, based only on BLE beacons and IMU data from smartwatches. Even though RSSI methods based on the use of smartwatches have been popular these last few years, the use of smartwatches as the main tool for tracking is not met frequently in existing literature; it is also particularly challenging, as smartwatch recordings are very noisy and also related to tasks that might be per-formed alongside movement intended to travel from one place to an-other.2. An algorithm to perform PDR from smartwatches; the VICON system will be used to provide ground truth positioning, and the noisy acceleration data will have to be analysed carefully to identify steps. Smartwatches have been used in PDR methods before, but only as a sensor fusion method, with smartphones or smartglasses being the primary sensing device.3. Motion pattern analysis at home using location and gait information. Though motion patterns have long been studied, it is either specific movements that are usually tackled, or the sensors used are either com-plicated networks, or intrusive (e.g., cameras), both of which are not appropriate for the privacy-preserving home environment.4. Should data from dementia patients become available, application of the aforementionedThe project includes collaboration with expert psychiatrists on dementia.

该项目属于EPSRC人工智能技术范围内的福尔斯。近年来，现代社会的各种变化导致大量的人在他们的家庭环境中度过了相当长的一天。越来越多的人转向自营职业，而企业似乎正在探索最近的研究，通过允许员工有灵活的工作时间，经常在家工作，以提高生产力。与此同时，医学的进步和预期寿命的增加导致了人口老龄化现象;尽管现在退休的老年人通常有更多的寿命，但他们经常面临与年龄有关的疾病，如关节炎，帕金森氏症，痴呆症或老年抑郁症，当他们变得更严重时，这些疾病可能会让他们呆在家里。在这个DPhil项目中，解决了采用广泛可用的、低成本和重量轻的传感器来监测人类在其家庭环境中的行为的任务。具体而言，我们将探索以下研究方向：1。一种仅基于BLE信标和智能手表IMU数据的房间识别算法。尽管基于使用智能手表的RSSI方法在过去几年中已经很流行，但在现有文献中，使用智能手表作为跟踪的主要工具并不常见;这也是特别具有挑战性的，因为智能手表记录非常嘈杂，并且还与可能伴随意图从一个地方旅行到另一个地方的运动而执行的任务有关。从智能手表执行PDR的算法; VICON系统将用于提供地面实况定位，并且必须仔细分析嘈杂的加速度数据以识别步骤。智能手表以前曾用于PDR方法中，但仅作为传感器融合方法，智能手机或智能眼镜是主要的传感设备。使用位置和步态信息在家中进行运动模式分析。虽然运动模式已经研究了很长时间，但通常处理的是特定的运动，或者使用的传感器是复杂的网络，或者是侵入性的（例如，摄像头），这两者都不适合保护隐私的家庭环境。如果痴呆症患者的数据可用，则应用上述项目包括与痴呆症方面的精神病专家合作。