| Summary: | Activity Recognition is an active research topic focused on detecting human actions and behaviours in smart environments. In most cases, the use of data-driven models aim to relate data from sensors to an activity through a model developed by a supervised approach. In this work, we focus on the goal of domain adaptation between smart environments, which has required a novel approach to relate the concepts of domain adaptation using binary sensor and learning from daily imbalanced data. In this work, the sensor activation from a given context is translated to a different one, based on the temporal alignment from human activities. The domain adaptation of binary sensor is accomplished through a three step procedure: i) clustering of sensor activation, ii) activity based alignment of sensor data between the two environments, iii) an ensemble of classifiers used to mine a mapping function, translating sensor data between the two environments. The proposed method was evaluated over a publicly available dataset, and obtained preliminary results which were encouraging with an F1-Score of 87%.
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