Alta Scuola Politecnica (Politecnico di Milano and Politecnico di Torino), 20133 Milano, Italy.
Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milano, Italy.
Sensors (Basel). 2021 Mar 18;21(6):2147. doi: 10.3390/s21062147.
The most frequent form of dementia is Alzheimer's Disease (AD), a severe progressive neurological pathology in which the main cognitive functions of an individual are compromised. Recent studies have found that loneliness and living in isolation are likely to cause an acceleration in the cognitive decline associated with AD. Therefore, understanding social behaviours of AD patients is crucial to promote sociability, thus delaying cognitive decline, preserving independence, and providing a good quality of life. In this work, we analyze the localization data of AD patients living in assisted care homes to gather insights about the social dynamics among them. We use localization data collected by a system based on iBeacon technology comprising two components: a network of antennas scattered throughout the facility and a Bluetooth bracelet worn by the patients. We redefine the Relational Index to capture wandering and casual encounters, these being common phenomena among AD patients, and use the notions of Relational and Popularity Indexes to model, visualize and understand the social behaviour of AD patients. We leverage the data analyses to build predictive tools and applications to enhance social activities scheduling and sociability monitoring and promotion, with the ultimate aim of providing patients with a better quality of life. Predictions and visualizations act as a support for caregivers in activity planning to maximize treatment effects and, hence, slow down the progression of Alzheimer's disease. We present the Community Behaviour Prediction Table (CBPT), a tool to visualize the estimated values of sociability among patients and popularity of places within a facility. Finally, we show the potential of the system by analyzing the Coronavirus Disease 2019 (COVID-19) lockdown time-frame between February and June 2020 in a specific facility. Through the use of the indexes, we evaluate the effects of the pandemic on the behaviour of the residents, observing no particular impact on sociability even though social distancing was put in place.
最常见的痴呆症形式是阿尔茨海默病（AD），这是一种严重的进行性神经病理学疾病，个体的主要认知功能受损。最近的研究发现，孤独和独居可能导致与 AD 相关的认知能力下降加速。因此，了解 AD 患者的社交行为对于促进社交能力、延缓认知能力下降、保持独立性和提供良好的生活质量至关重要。在这项工作中，我们分析了居住在辅助生活养老院的 AD 患者的定位数据，以了解他们之间的社交动态。我们使用基于 iBeacon 技术的系统收集的定位数据，该系统由两个组件组成：分布在整个设施中的天线网络和患者佩戴的蓝牙手环。我们重新定义了关系指数来捕捉 AD 患者常见的游荡和偶然相遇现象，并使用关系和知名度指数的概念来建模、可视化和理解 AD 患者的社交行为。我们利用数据分析来构建预测工具和应用程序，以增强社交活动安排和社交性监测和促进，最终目标是为患者提供更好的生活质量。预测和可视化为护理人员在活动规划中提供支持，以最大限度地提高治疗效果，从而减缓阿尔茨海默病的进展。我们提出了社区行为预测表（CBPT），这是一种可视化工具，可以显示患者之间社交能力和设施内场所受欢迎程度的估计值。最后，我们通过分析特定设施在 2020 年 2 月至 6 月期间的 2019 年冠状病毒病（COVID-19）封锁时间框架，展示了该系统的潜力。通过使用这些指数，我们评估了大流行对居民行为的影响，尽管实施了社交距离，但社交能力没有受到特别影响。
