With the acceleration of aging process of population structure, the single resident lifestyle is increasing on account of the high cost of care services and the privacy invasion concern. It is essential to monitor the activities of solitary elderly to find the emergency and lifestyle deviation, as independent life cannot be maintained due to physical or mental problems. The unobtrusive systems are the most preferred choice for the real-life long-term monitoring, while the camera and wearable devices based systems are not suitable due to the privacy and uncomfortableness, respectively. We propose a novel sensor-based activity recognition model based on the two-layer multi-granularity framework and the emergent paradigm with marker-based stigmergy. The stigmergy based marking subsystem builds features by aggregating the context-aware information and generating the two-dimensional activity pheromone trail. The two-layer framework consists of coarse-grained and fine-grained classification subsystems. The coarse-grained subsystem identifies whether the input completed activity segmented by the traditional method is easily-confused, and utilizes our generalized segmentation method to increase the inter-cluster distance. The fine-grained subsystem employs machine learning or deep learning classifiers to realize the activity recognition task. The proposed model is a data-driven model based on the information self-organization. It does not need sophisticated domain knowledge, and can fully mine the hidden feature structure containing semantically related information and spatio-temporal characteristics. The experimental results demonstrate the effectiveness of the proposed method.

随着人口结构老龄化进程的加快，由于护理服务的高成本和对隐私权的侵犯，单身居民的生活方式正在增加。监视孤独老人的活动以发现紧急情况和生活方式偏离是至关重要的，因为由于身体或精神问题无法维持独立生活。对于现实生活中的长期监控而言，非干扰性系统是最优选的选择，而基于摄像头和可穿戴设备的系统分别由于隐私和不舒适性而不合适。我们提出了一个新颖的基于传感器的活动识别模型，该模型基于两层多粒度框架以及基于标记的Stigmergy的新兴范例。基于Stigmergy的标记子系统通过聚合上下文感知信息并生成二维活动信息素踪迹来构建特征。两层框架由粗粒度和细粒度分类子系统组成。粗粒度子系统识别通过传统方法分割的输入完成活动是否容易混淆，并利用我们的广义分割方法来增加群集间距离。细粒度子系统采用机器学习或深度学习分类器来实现活动识别任务。该模型是基于信息自组织的数据驱动模型。它不需要复杂的领域知识，并可以充分挖掘包含语义相关信息和时空特征的隐藏特征结构。实验结果证明了该方法的有效性。