In modern healthcare, sensing technologies such as IoT empower the quality of assisted living service by knowing what a resident is doing in real-time. Using extreme connectivity and cloud computing in a smart home, where a collection of sensors is installed, the sensors sample continuously from the movements of the resident as well as ambient data from the surrounding inside the house. Automatic human activity recognition of the resident's activities is one of the key components of assisted living in smart home. For monitoring in-home safety, the ability in recognizing abnormal activities such as accident, falling, acute disease attack (e.g. asthma, stroke, etc.), fainting, wobbling, is particularly important. The detection and machine learning process must be both accurate and fast, to cope with the real-time activity recognition. To this end, a novel streamlined sensor data processing method is proposed called Evolutionary Expand-and-Contract Instance-based Learning algorithm (EEAC-IBL). The multivariate data stream is first expanded into many subspaces, then the subspaces which are corresponding to the characteristics of the features are selected and condensed into a significant feature subset. The selection operates scholastically instead of deterministically by evolutionary optimization which approximates the best subgroup. Followed by data stream mining, the machine learning for activity recognition is done on the fly. This approach is unique and suitable for such extreme connectivity scenario where precise feature selection is not required, and the relative importance of each feature among the sensor data changes over time. This stochastic approximation method is fast and accurate, offering an alternative to traditional machine learning method for smart home activity recognition application. Our experimental results show computing advantages over other classical approaches.

在现代医疗保健中，诸如IoT之类的传感技术可通过了解居民的实时状况来提高辅助生活服务的质量。在安装了一系列传感器的智能家居中，通过使用极高的连接性和云计算，这些传感器会从居民的移动以及房屋内部周围的环境数据中连续采样。居民活动的自动人类活动识别是智能家居辅助生活的关键组成部分之一。为了监视室内安全，识别异常活动（例如事故，跌倒，急性疾病发作（例如哮喘，中风等），昏厥，摇摆）的能力尤其重要。检测和机器学习过程必须准确且快速，以应对实时活动识别。为此，提出了一种新颖的流线型传感器数据处理方法，称为基于进化扩展和收缩实例的学习算法（EEAC-IBL）。首先将多元数据流扩展为许多子空间，然后选择与特征的特征相对应的子空间并将其压缩为重要的特征子集。该选择以经院优化的方式进行，而不是通过近似最佳子组的进化优化来确定性地进行。在数据流挖掘之后，用于活动识别的机器学习是即时进行的。该方法是唯一的，适用于不需要精确特征选择且传感器数据中每个特征的相对重要性随时间变化的极端连接情况。这种随机逼近方法快速准确，为智能家居活动识别应用提供了传统机器学习方法的替代方法。我们的实验结果显示了与其他经典方法相比的计算优势。