Human activity recognition has been a popular research area concerned with identifying the specific movement or action of a person based on variety of sensor data. Conventional human activity recognition approaches are mainly data driven, which are not working well for composite activity recognition due to the complexity and uncertainty of real scenarios. We propose in this paper a hierarchical structure-based framework and methodology for human activity recognition by an integration of data-driven approach and knowledge-based approach, which provides an interesting framework capable of bridging lower-level pattern recognition and higher-level knowledge for reasoning and explanation. More specifically, this approach constructs a hierarchical structure for representing the composite activity by a composition of lower-level actions and gestures according to its semantic meaning. This hierarchical structure is then transformed into formal syntactical logical formulas and rules, based on which the resolution based automated reasoning is applied to recognize the composite activity given the recognized lower-level actions by using data driven machine learning methods. The work is the validated using some open-source data about video based human activity recognition. The present work provides a promising framework and application illustration of integration of machine learning and symbolic reasoning.

人类活动识别已成为涉及基于多种传感器数据识别人的特定运动或动作的热门研究领域。常规的人类活动识别方法主要是数据驱动的，由于实际场景的复杂性和不确定性，因此不适用于复合活动识别。我们在本文中提出了一种基于层次结构的框架，通过整合数据驱动方法和基于知识的方法来进行人类活动识别，该方法和方法提供了一个有趣的框架，能够将低级模式识别和高级知识相结合。推理和解释。进一步来说，这种方法构造了一个层次结构，用于根据其语义含义通过组合低级动作和手势来表示复合活动。然后，将这种层次结构转换为正式的句法逻辑公式和规则，在此基础上，通过使用数据驱动的机器学习方法，基于分辨率的自动推理可应用于识别给定的低级动作的复合活动。使用有关基于视频的人类活动识别的一些开源数据对这项工作进行了验证。本工作为机器学习和符号推理的集成提供了有希望的框架和应用说明。在此基础上，基于分辨率的自动推理可通过使用数据驱动的机器学习方法在给定已识别的较低级动作的情况下识别复合活动。使用有关基于视频的人类活动识别的一些开源数据对这项工作进行了验证。本工作为机器学习和符号推理的集成提供了有希望的框架和应用说明。在此基础上，基于分辨率的自动推理可通过使用数据驱动的机器学习方法在给定已识别的较低级动作的情况下识别复合活动。使用有关基于视频的人类活动识别的一些开源数据对这项工作进行了验证。本工作为机器学习和符号推理的集成提供了有希望的框架和应用说明。