Activity recognition: An evolutionary ensembles approach

Activity recognition is an emerging field that demands active research in ubiquitous computing for analyzing complex scenarios such as concurrent situation assessment and domination of major over the minor activities. In this paper, an evolutionary ensembles approach using Genetic Algorithm (GA) as a homogeneous learner has been proposed. This approach values both minor and major activities by processing each of them independently. It consists of two phases. The first phase is preprocessing of sensory data and extraction of feature vectors. Evolutionary ensembles are designed in second phase to learn different daily life activities. Finally, multiple ensembles output is pooled on central node as a complete rule profile for all performed activities. The proposed approach was evaluated on six different types of activities from Intelligent System Laboratory (ISL) dataset. It shows a higher accuracy as compared to single learner GA.