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首页> 外文会议>IEEE 10th International Conference on Ubiquitous Intelligence and Computing; IEEE 10th International Conference on Autonomic and Trusted Computing >Towards Context Consistency in a Rule-Based Activity Recognition Architecture
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Towards Context Consistency in a Rule-Based Activity Recognition Architecture

机译：基于规则的活动识别体系结构中的上下文一致性

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摘要

Accurate human activity recognition (AR) is crucial for intelligent pervasive environments, e.g., energy-saving buildings. In order to gain precise and fine-grained AR results, a system must overcome partial observability of the environment and noisy, imprecise, and corrupted sensor data. In this work, we propose a rule-based AR architecture that effectively handles multiple-user, multiple-area situations, recognizing real-time office activities. The proposed solution is based on an ontological approach, using low-cost, binary, wireless sensors. We employ context consistency diagrams (CCD) as a key component for fault correction. A CCD is a data structure that provides a mechanism for probabilistic reasoning about the current situation and determines the most probable current situation in the presence of inconsistencies, conflicts, and ambiguities in sensor readings. The implementation of the system and its evaluation in a living lab environment show that the CCD corrects up to 46.8% of sensor data faults, improving overall recognition accuracy by up to 11.1%, thus achieving reliable recognition results from unreliable sensor data.
机译：准确的人类活动识别（AR）对于智能无处不在的环境（例如节能建筑）至关重要。为了获得精确和细粒度的AR结果，系统必须克服环境的部分可观察性以及嘈杂，不精确和损坏的传感器数据。在这项工作中，我们提出了一种基于规则的AR体系结构，该体系结构可有效处理多用户，多区域情况，并识别实时办公活动。所提出的解决方案基于本体方法，使用了低成本的二进制无线传感器。我们采用上下文一致性图（CCD）作为错误纠正的关键组件。 CCD是一种数据结构，可提供一种对当前情况进行概率推理的机制，并在传感器读数存在不一致，冲突和歧义的情况下确定最可能的当前情况。该系统的实施及其在现场实验室环境中的评估表明，CCD可以纠正多达46.8％的传感器数据故障，从而将总体识别准确度提高了多达11.1％，从而从不可靠的传感器数据中获得了可靠的识别结果。

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