Ambient assisted living and smart home technologies are a good way to take care of dependent people whose number will increase in the future. They allow the discovery and the recognition of human's activities of daily living (ADLs) in order to take care of people by keeping them in their home. In order to consider the human behavior nondeterminism, probabilistic approaches are used despite difficulties encountered in model generation and probabilistic indicators computing. In this article, a global method based on probabilistic finite-state automata and the definition of the normalized likelihood and perplexity is proposed to manage ADLs discovery and recognition. In order to reduce the computational complexity, some results about a simplified normalized likelihood computation are proved. A real case study showing the efficiency of the proposed method is discussed. Note to Practitioners-This article is motivated by the problem of the automatic recognition of activities that are daily performed by elderly or disabled people in a smart dwelling. The set of activities to be recognized is defined by a medical staff (e.g., to prepare meal, to do housework, to take leisure, etc.) and correspond to pathologies that have to be monitored by doctors (e.g., loss of memory, loss of mobility, etc.). The proposed method is based on a systematic procedure of offline construction of a model for each activity to be monitored (the activity discovering step). The online recognition of activities actually performed (the activity recognition step) is afterward based on these models of activities. Since the human behavior is nondeterministic, and may even be irrational, probabilistic activity models are built from a learning database. In the same way, probabilistic indicators are used for determining online the most probable activities actually performed. The efficiency of the proposed approach is illustrated through a case study performed in a smart living lab.

中文翻译：

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使用概率有限状态自动机发现和识别人类活动


环境辅助生活和智能家居技术是照顾受抚养人的好方法，这些受抚养人的数量在未来将会增加。它们允许发现和识别人类的日常生活活动 (ADL)，以便通过将人们留在家中来照顾他们。为了考虑人类行为的不确定性，尽管在模型生成和概率指标计算方面遇到困难，但仍使用概率方法。在本文中，提出了一种基于概率有限状态自动机以及归一化似然性和困惑度定义的全局方法来管理 ADL 发现和识别。为了降低计算复杂度，证明了简化归一化似然计算的一些结果。讨论了显示所提出方法的效率的真实案例研究。从业者注意事项 - 本文的动机是自动识别智能住宅中老年人或残疾人日常活动的问题。待识别的一组活动由医务人员定义（例如，准备膳食、做家务、休闲等），并对应于必须由医生监测的病理（例如，记忆丧失、丧失能力）。的流动性等）。所提出的方法基于为每个要监控的活动离线构建模型的系统过程（活动发现步骤）。随后，对实际执行的活动的在线识别（活动识别步骤）基于这些活动模型。由于人类行为是不确定的，甚至可能是非理性的，因此概率活动模型是根据学习数据库构建的。 以同样的方式，概率指标用于在线确定实际执行的最可能的活动。通过在智能生活实验室中进行的案例研究说明了所提出方法的效率。