Fuzzy-Based Fine-Grained Human Activity Recognition within Smart Environments
Fuzzy-Based Fine-Grained Human Activity Recognition within Smart Environments
复制标题
智能环境中基于模糊的细粒度人类活动识别
DOI：
10.1109/smartworld-uic-atc-scalcom-iop-sci.2019.00059
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Feng Chen
中科院分区：
文献类型：
--
作者：
D. Triboan;Liming Chen;Feng Chen
随着人口老龄化的加剧，智能家居（SH）一直在积极研究，以实现环境辅助生活（AAL）并促进独立生活。人类活动识别（HAR）是AAL系统的支柱，用于检测日常生活活动（ADL）并提供及时的上下文感知帮助。现有的基于SH的AAL系统主要关注粗粒度的活动识别（AR），并假设使用二进制传感器成功使用日常对象。有限的注意力被给予细粒度AR通过验证预期的对象与来自多个异构传感器数据的证据的相互作用。本文提出了一种细粒度的AR方法，融合多模态数据从单个对象和处理非二进制传感器测量的不精确性。该方法利用模糊本体来建模细粒度的动作与不精确的成员关系状态的传感器对象和fuzzyDL推理工具来分类动作完成。此外，提出了一种微服务架构，该架构具有非侵入式异构环境和基于嵌入式对象的感知方法。传感方法集成了现成的和定制的设备，以收集细粒度的对象级交互。提供了一个案例研究来说明使用细粒度AR方法来识别基于厨房的活动。
With the increasing ageing population, Smart Home (SH) has been under vigorous investigation to enable Ambient Assisted Living (AAL) and foster independent living. Human Activity Recognition (HAR) is the backbone of AAL systems in order to detect Activities of Daily Living (ADL) and provide timely, context-aware assistance. Existing SH based AAL systems primarily focus on coarse-grained activity recognition (AR) and assume successful usage of everyday objects using binary sensors. Limited attention is given to fined-grained AR by verifying the intended object interactions with evidence from multiple heterogeneous sensor data. This paper proposes a fine-grained AR approach which fuses multimodal data from single objects and handles the imprecise nature of non-binary sensor measurements. This approach leverages the fuzzy ontology to model fine-grained actions with imprecise membership states of the sensors in relation to object and fuzzyDL reasoning tool to classify action completion. In addition, a microservice architecture is proposed with a non-intrusive heterogeneous ambient and embedded object based sensing method. The sensing method integrates both off-the-shelf and bespoke devices to collect fine-grained object level interactions. A case study is provided to illustrate the use of the fine-grained AR approach to recognize kitchen-based activities.