Genetic programming based activity recognition on a smartphone sensory data benchmark

Abstract

Activity recognition from smartphone sensor inputs is of great importance to enhance user experience. Our study aims to investigate the applicability of Genetic Programming (GP) approach on this complex real world problem. Traditional methods often require substantial human efforts to define good features. Moreover the optimal features for one type of activity may not be suitable for another. In comparison, our GP approach does not require such feature extraction process, hence, more suitable for complex activities where good features are difficult to be pre-defined. To facilitate this study we therefore propose a benchmark of activity data collected from various smartphone sensors, as currently there is no existing publicly available database for activity recognition. In this study, a GP-based approach is applied to nine types of activity recognition tasks by directly taking raw data instead of features. The effectiveness of this approach can be seen by the promising results. In addition our benchmark data provides a platform for other machine learning algorithms to evaluate their performance on activity recognition.