机器人辅助智能环境中用于异常检测的基于小波的人类活动时间模型
Wavelet-based temporal models of human activity for anomaly detection in smart robot-assisted environments
AI总结：
本文提出融合小波时间模型、二元传感器活动与专家规则的混合马尔可夫逻辑网络，用于机器人辅助环境长期活动异常检测，并在两个新公开数据集上验证有效。
摘要：
检测传感器数据模式中的异常在许多实际应用中都很重要，包括主动辅助生活（AAL）中的家庭活动监测。然而，如何表示和分析这些模式仍然是一项具有挑战性的任务，尤其是在数据相对稀缺且需要针对特定场景微调显式模型时。因此，本文提出了一种利用智能家居传感器对长期人类活动进行时间建模的新方法，用于检测机器人辅助环境中的异常情况。该模型基于小波变换，用于预测智能传感器数据，提供时间先验以检测人类环境中的意外事件。为此，本文开发了Hybrid Markov Logic Networks（混合马尔可夫逻辑网络）的一种新扩展，融合了不同的异常指标，包括由二元传感器检测到的活动、专家逻辑规则以及基于小波的时间模型。尤其是后者使推理系统能够发现偏离长期活动模式的情况，而这些情况无法被较简单的基于频率的模型检测到。为在办公和家庭场景中评估该方法，研究使用多个智能传感器收集了两个新的公开可用数据集。实验结果证明了所提解决方案的有效性及其在复杂人类环境中的成功部署，显示了其在未来智能家居和机器人集成服务中的潜力。
英文摘要：
Abstract. Detecting anomalies in patterns of sensor data is important in many practical applications, including domestic activity monitoring for Active Assisted Living (AAL). How to represent and analyse these patterns, however, remains a challenging task, especially when data is relatively scarce and an explicit model is required to be fine-tuned for specific scenarios. This paper, therefore, presents a new approach for temporal modelling of long-term human activities with smart-home sensors, which is used to detect anomalous situations in a robot-assisted environment. The model is based on wavelet transforms and used to forecast smart sensor data, providing a temporal prior to detect unexpected events in human environments. To this end, a new extension of Hybrid Markov Logic Networks has been developed that merges different anomaly indicators, including activities detected by binary sensors, expert logic rules, and wavelet-based temporal models. The latter in particular allows the inference system to discover deviations from long-term activity patterns, which cannot be detected by simpler frequency-based models. Two new publicly available datasets were collected using several smart-sensors to evaluate the approach in office and domestic scenarios. The experimental results demonstrate the effectiveness of the proposed solutions and their successful deployment in complex human environments, showing their potential for future smart-home and robot integrated services.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
