Knowledge-Based Architecture for Recognising Activities of Older People
Knowledge-Based Architecture for Recognising Activities of Older People
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DOI：
10.1016/j.procs.2019.09.214
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Mohamed Bennasar;B. Price;Avelie Stuart;D. Gooch;Ciaran Mccormick;Vikram Mehta;L. Clare;A. Bennaceur;Jessica Cohen;A. Bandara;M. Levine;B. Nuseibeh
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文献类型：
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作者：
Mohamed Bennasar;B. Price;Avelie Stuart;D. Gooch;Ciaran Mccormick;Vikram Mehta;L. Clare;A. Bennaceur;Jessica Cohen;A. Bandara;M. Levine;B. Nuseibeh
世界正面临人口老龄化现象，再加上健康和社会问题，影响到老年人独立生活的能力。这种情况对保健和社会服务的生存能力构成挑战。智能家居技术可以在减轻护理人员的压力以及降低卫生和社会服务的财务成本方面发挥重要作用。日常生活活动（ADL）识别是将传感器数据转化为高语义水平活动的重要步骤。监督机器学习（ML）算法是此应用程序中最常用的技术。然而，一个常见的问题是缺乏足够的带注释的数据来训练这些算法。收集带注释的数据既昂贵又耗时，还可能侵犯人们的隐私。执行复杂活动的个人内部和个人之间的差异是基于ml的活动识别方法面临的另一个挑战。本文提出了一种实时识别ADL的基于知识的多层体系结构。第一阶段，对传感器数据进行预处理；在第二阶段检测描述环境变化的事件，在第三阶段使用事件序列来识别语义上更复杂的活动。由于以前提出的本体要么设计用于处理特定的传感器数据，要么忽略了上下文环境信息，而上下文环境信息在识别复杂活动中很重要，因此提出了一种新的ADL本体来建模与传感器平台和目标活动相关的知识。
The world is facing an ageing population phenomenon, coupled with health and social problems, which affect older people’s ability to live independently. This situation challenges the viability of health and social services. Smart home technology can play a significant role in easing the pressure on caregivers, as well as reduce the financial costs of health and social services. Activity of Daily Living (ADL) recognition is an essential step to translate sensor data into activities at high semantic levels. Supervised Machine Learning (ML) algorithms are the most commonly used techniques for this application. However, a common problem is a lack of availability of enough annotated data to train these algorithms. Collecting annotated data is expensive, time consuming, and may violate people’s privacy. Intra- and inter-personal variation in performing complex activities is another challenge for an ML-based activity recognition approach. In this paper, a multi-layered knowledge-based architecture for recognising ADL in real-time is proposed. At the first stage, sensor data is pre-processed; events that describe changes in the environment are detected at the second stage, in which the sequence of events is used to recognise more semantically complex activities at the third stage. A new ADL ontology is proposed to model the knowledge related to the sensor platform and the targeted activities as the previously proposed ontologies were either designed to deal with specific sensor data, or they ignored the context environment information which is important in recognising complex activities.