We develop CACE (Constraints And Correlations mining Engine), a framework that significantly improves the recognition accuracy of complex daily activities in multi-inhabitant smarthomes. CACE views the implicit relationships between the activities of multiple people as an asset, and exploits such constraints and correlations in a hierarchical fashion, taking advantage of both person-specific sensor data (generated by wearable devices) and person-independent ambient sensor data (generated by ambient sensors). To effectively utilize such couplings, CACE first uses a multi-target particle filtering approach over ambient sensors captured movement data, to identify the number of distinct users and infer individual-specific movement trajectories. We then utilize a Hierarchical Dynamic Bayesian Network (HDBN)-based model for activity recognition. This model utilizes the inter-and-intra individual correlations and constraints, at both micro-activity and macro-activity levels, to recognize individual activities accurately. These constraints are learnt automatically using data-mining techniques, and help to dramatically reduce the computational complexity of HDBN-based inferencing. Empirical studies using a real-world testbed of five multi-inhabitant smarthomes shows that CACE is able to achieve an activity recognition accuracy of $\approx 95\%$≈95%, with a 16-fold reduction in computational overhead compared to traditional hybrid classification approaches.

中文翻译：

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多个居民的跟踪和行为增强活动识别

我们开发了 CACE（约束和相关挖掘引擎），这是一个框架，可显着提高多居民智能家居中复杂日常活动的识别准确性。CACE 将多人活动之间的隐含关系视为一种资产，并以分层方式利用此类约束和相关性，利用特定于个人的传感器数据（由可穿戴设备生成）和独立于个人的环境传感器数据（生成的）通过环境传感器）。为了有效地利用这种耦合，CACE 首先对环境传感器捕获的运动数据使用多目标粒子过滤方法，以识别不同用户的数量并推断个人特定的运动轨迹。然后，我们利用基于分层动态贝叶斯网络 (HDBN) 的模型进行活动识别。该模型利用微观活动和宏观活动层面的个体内部和内部的相关性和约束来准确识别个体活动。这些约束是使用数据挖掘技术自动学习的，有助于显着降低基于 HDBN 推理的计算复杂性。使用五个多居民智能家居的真实世界测试台的实证研究表明，CACE 能够实现活动识别准确度$\大约 95\%$≈95%，与传统的混合分类方法相比，计算开销减少了 16 倍。