The existing action recognition systems mainly focus on generalized methods to categorize human actions. However, the generalized systems cannot attain the same level of recognition performance for new users mainly due to the high variance in terms of human behavior and the way of performing actions, i.e. activity handling. The use of personalized models based on similarity was introduced to overcome the activity handling problem, but the improvement was found to be limited as the similarity was based on physiognomies rather than the behavior. Moreover, human interaction with contextual information has not been studied extensively in the domain of action recognition. Such interactions can provide an edge for both recognizing high-level activities and improving the personalization effect. In this paper, we propose the context-aware personalized human activity recognition (CAPHAR) framework which computes the class association rules between low-level actions/sensor activations and the contextual information to recognize high-level activities. The personalization in CAPHAR leverages the individual behavior process using a similarity metric to reduce the effect of the activity handling problem. The experimental results on the “daily lifelog” dataset show that CAPHAR can achieve at most 23.73% better accuracy for new users in comparison to the existing classification methods.

现有的动作识别系统主要集中于对人类动作进行分类的通用方法。然而，广义系统无法为新用户获得相同水平的识别性能，这主要是由于人类行为和执行动作（即活动处理）的方式存在很大差异。引入基于相似性的个性化模型来克服活动处理问题，但发现改进是有限的，因为相似性是基于相貌而不是行为。此外，人类与上下文信息的交互尚未在动作识别领域得到广泛研究。这种交互可以为识别高水平活动和提高个性化效果提供优势。在本文中，我们提出了上下文感知的个性化人类活动识别（CAPHAR）框架，该框架计算低级动作/传感器激活与上下文信息之间的类关联规则以识别高级活动。 CAPHAR 中的个性化利用相似性度量来利用个体行为过程来减少活动处理问题的影响。在“daily lifelog”数据集上的实验结果表明，与现有的分类方法相比，CAPHAR 对于新用户的准确率最多可以提高 23.73%。