Efficiency and scalability are obstacles that have not yet received a viable response from the human activity recognition research community. This paper proposes an activity recognition method. The knowledge model is in the form of ontology, the state-of-the-art in knowledge representation and reasoning. The ontology starts with probabilistic information about subjects' low-level activities and location and then is populated with the assertion axioms learned from data or defined by the user. Unlike methods that choose only the most probable candidate from sensor readings, the proposed method keeps multiple candidates with the known degree of confidence for each one and involves them in decision making. Using this method, the system is more flexible to deal with unreliable readings from sensors, and the final recognition rate is improved. Besides, to resolve the scalability problem, a system is designed and implemented to do reasoning and storing in a relational database management system. Numerical evaluation and conceptual benchmarking prove the proposed system feasibility.

中文翻译：

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一种基于本体的、完全概率的、可扩展的人类活动识别方法

效率和可扩展性是人类活动识别研究界尚未收到可行回应的障碍。本文提出了一种活动识别方法。知识模型采用本体的形式，是知识表示和推理的最新技术。本体以关于主体低级活动和位置的概率信息开始，然后填充从数据中学习或由用户定义的断言公理。与仅从传感器读数中选择最可能的候选者的方法不同，所提出的方法使多个候选者保持每个候选者的已知置信度，并让他们参与决策。使用这种方法，系统可以更灵活地处理来自传感器的不可靠读数，并提高最终识别率。此外，为了解决可扩展性问题，设计并实现了一个系统来在关系数据库管理系统中进行推理和存储。数值评估和概念基准测试证明了所提出的系统的可行性。