Department of Computer Science, University College London, London WC1E 6BT, UK.
School of Electronic and Information, Zhongyuan University of Technology, Zhengzhou 450007, China.
Sensors (Basel). 2021 Jan 2;21(1):260. doi: 10.3390/s21010260.
Age-related health issues have been increasing with the rise of life expectancy all over the world. One of these problems is cognitive impairment, which causes elderly people to have problems performing their daily activities. Detection of cognitive impairment at an early stage would enable medical doctors to deepen diagnosis and follow-up on patient status. Recent studies show that daily activities can be used to assess the cognitive status of elderly people. Additionally, the intrinsic structure of activities and the relationships between their sub-activities are important clues for capturing the cognitive abilities of seniors. Existing methods perceive each activity as a stand-alone unit while ignoring their inner structural relationships. This study investigates such relationships by modelling activities hierarchically from their sub-activities, with the overall goal of detecting abnormal activities linked to cognitive impairment. For this purpose, recursive auto-encoders (RAE) and their linear vs. greedy and supervised vs. semi-supervised variants are adopted to model the activities. Then, abnormal activities are systematically detected using RAE's reconstruction error. Moreover, to apply RAEs for this problem, we introduce a new sensor representation called raw sensor measurement (RSM) that captures the intrinsic structure of activities, such as the frequency and the order of sensor activations. As real-world data are not accessible, we generated data by simulating abnormal behaviour, which reflects on cognitive impairment. Extensive experiments show that RAEs can be used as a decision-supporting tool, especially when the training set is not labelled to detect early indicators of dementia.
随着全球预期寿命的提高，与年龄相关的健康问题也越来越多。其中一个问题是认知障碍，它导致老年人在进行日常活动时出现问题。早期发现认知障碍可以使医生更深入地诊断和跟踪患者的状况。最近的研究表明，日常活动可以用来评估老年人的认知状态。此外，活动的内在结构及其子活动之间的关系是捕捉老年人认知能力的重要线索。现有的方法将每项活动视为独立的单元，而忽略了它们内在的结构关系。本研究通过从子活动对活动进行层次建模来研究这些关系，其总体目标是检测与认知障碍相关的异常活动。为此，采用递归自动编码器（RAE）及其线性与贪婪、监督与半监督变体对活动进行建模。然后，使用 RAE 的重构误差系统地检测异常活动。此外，为了将 RAEs 应用于该问题，我们引入了一种新的传感器表示方法，称为原始传感器测量（RSM），它可以捕捉活动的内在结构，例如传感器激活的频率和顺序。由于无法获得真实世界的数据，我们通过模拟异常行为来生成数据，这反映了认知障碍。大量实验表明，RAE 可以用作决策支持工具，尤其是在训练集未标记的情况下，用于检测痴呆症的早期指标。
