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Activity recognition by smartphone based multi-channel sensors with genetic programming

Recognition of activities such as sitting, standing, walking and running can significantly improve the interaction between human and machine, especially on mobile devices. In this study we present a GP based method which can automatically evolve recognition programs for various activities using multisensor data. This investigation shows that GP is capable of achieving good recognition on binary problems as well as on multi-class problems. With this method domain knowledge about an activity is not required. Furthermore, extraction of time series features is not necessary. The investigation also shows that these evolved GP solutions are small in size and fast in execution. They are suitable for real-world applications which may require real-time performance.