Home monitoring of patients with early and late stages of dementia

该项目属于EPSRC人工智能技术。近年来，现代社会的各种变化导致大量的人每天都呆在他们的家庭环境中。越来越多的人转向自我雇佣，而企业似乎正在探索最近与提高生产率有关的研究，允许员工有灵活的工作时间，其中10%在家里工作。与此同时，医学的进步和预期寿命的延长导致了人口老龄化的现象；尽管现在退休的老年人通常还有更多的寿命，但他们经常面临与年龄相关的疾病，如关节炎、帕金森氏症、痴呆症或老年性抑郁症，当他们变得更加严重时，这些疾病可能会让他们呆在家里。在DPhil的这个项目中，使用广泛可用的、低成本和轻型传感器来监测人类在家庭环境中的行为是一项任务。特别是，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方法在过去几年很流行，但在现有文献中，使用智能手表作为主要跟踪工具的情况并不常见；这也特别具有挑战性，因为智能手表的记录非常嘈杂，而且还与可能伴随从一个地方到另一个地方的运动一起执行的任务有关。一种通过智能手表执行PDR的算法；VICON系统将用于提供地面真实定位，并且必须仔细分析嘈杂的加速度数据以识别步骤。智能手表以前也被用于PDR方法，但只是作为一种传感器融合方法，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分析运动模式。虽然对运动模式的研究由来已久，但通常处理的是特定的运动，或者使用的传感器要么是复杂的网络，要么是侵入性的(例如，摄像头)，这两者都不适合于保护隐私的家庭环境。如果可以获得痴呆症患者的数据，上述项目的应用包括与痴呆症专家精神病学家的合作。

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.