Department of Electrical and Electronic Engineering, Centre for Vision, Speech and Signal Processing (CVSSP), University of Surrey, Surrey, United Kingdom.
Surrey and Borders Partnership NHS Foundation Trust, Leatherhead, Surrey, United Kingdom.
PLoS One. 2019 Jan 15;14(1):e0209909. doi: 10.1371/journal.pone.0209909. eCollection 2019.
Dementia is a neurological and cognitive condition that affects millions of people around the world. At any given time in the United Kingdom, 1 in 4 hospital beds are occupied by a person with dementia, while about 22% of these hospital admissions are due to preventable causes. In this paper we discuss using Internet of Things (IoT) technologies and in-home sensory devices in combination with machine learning techniques to monitor health and well-being of people with dementia. This will allow us to provide more effective and preventative care and reduce preventable hospital admissions. One of the unique aspects of this work is combining environmental data with physiological data collected via low cost in-home sensory devices to extract actionable information regarding the health and well-being of people with dementia in their own home environment. We have worked with clinicians to design our machine learning algorithms where we focused on developing solutions for real-world settings. In our solutions, we avoid generating too many alerts/alarms to prevent increasing the monitoring and support workload. We have designed an algorithm to detect Urinary Tract Infections (UTI) which is one of the top five reasons of hospital admissions for people with dementia (around 9% of hospital admissions for people with dementia in the UK). To develop the UTI detection algorithm, we have used a Non-negative Matrix Factorisation (NMF) technique to extract latent factors from raw observation and use them for clustering and identifying the possible UTI cases. In addition, we have designed an algorithm for detecting changes in activity patterns to identify early symptoms of cognitive decline or health decline in order to provide personalised and preventative care services. For this purpose, we have used an Isolation Forest (iForest) technique to create a holistic view of the daily activity patterns. This paper describes the algorithms and discusses the evaluation of the work using a large set of real-world data collected from a trial with people with dementia and their caregivers.
痴呆症是一种影响全球数百万人的神经和认知疾病。在英国，任何时候都有 1/4 的医院床位被患有痴呆症的人占用，而这些住院中有约 22%是由于可预防的原因。在本文中，我们讨论了使用物联网 (IoT) 技术和家庭内传感器设备结合机器学习技术来监测痴呆症患者的健康和幸福。这将使我们能够提供更有效和预防性的护理，并减少可预防的住院治疗。这项工作的一个独特方面是将环境数据与通过低成本家庭内传感器收集的生理数据相结合，以提取有关痴呆症患者在其家庭环境中的健康和幸福的可操作信息。我们与临床医生合作设计了我们的机器学习算法，重点是为现实世界的环境开发解决方案。在我们的解决方案中，我们避免生成太多警报/警报到预防增加监测和支持工作量。我们设计了一种算法来检测尿路感染 (UTI)，这是痴呆症患者住院的五个主要原因之一（在英国，痴呆症患者中有 9%的人因 UTI 住院）。为了开发 UTI 检测算法，我们使用非负矩阵分解 (NMF) 技术从原始观察中提取潜在因素，并使用它们进行聚类和识别可能的 UTI 病例。此外，我们还设计了一种算法来检测活动模式的变化，以识别认知能力下降或健康状况下降的早期症状，从而提供个性化和预防性护理服务。为此，我们使用了隔离森林 (iForest) 技术来创建日常活动模式的整体视图。本文描述了这些算法，并讨论了使用从痴呆症患者及其护理人员的试验中收集的大量真实世界数据评估工作的结果。
