A Multiresident Activity Recognition Approach based on Frequent Itemset Mining Features Activity recognition is one of the most important supporting technologies for smarthome applications, and most existing works conducted on this topic assume that there is only one resident in the smart home. However, there are often more than one resident at home in reality, which makes it ever more challenging to recognize their activities. In this work, a knowledgedriven approach for multiresident activity recognition based on the features constructed by frequent itemset is proposed. Unlike traditional singlelabel based methods, we address this problem as learning multiple activity labels from the sensordata sequence. Based on the binary ambient sensor data, our approach utilizes both features generated from single sensor and frequent itemset features of each class, i.e. activity. After that, five multilabel approaches are applied to recognize different activities in a short period of time. Finally, we conduct several experiments on a multiresident activity dataset, and the experimental result demonstrates that the proposed approach with frequent itemset features is better than those without frequent itemset features added.

RFARP: RFIDBased Activity Recognition and Prediction in Smart Home Smart Home is generally considered to be the final solution for human living problem, especially for health care of the elderly and disabled, power saving, etc. Human activity recognition in smart home is the key to achieve home automation, which enables smart services automatically run according to human mind. Recent researches have made several progresses in this field, however most of them can only recognize default activities which is probably not needed by smart home services. In addition, low scalability makes such researches infeasible out of laboratory. In this work, we unwrap this issue and propose a novel framework to not only recognize human activity, but also predict it. The framework contains three stages: recognition after the activity; recognition in progress and activity prediction in advance. With the help of RFID tags, the hardware cost of our framework is low enough to popularize. And the experiment result shows that our framework can realize good performance in activity recognition and prediction with high scalability.