Online sequential ELM based transfer learning for transportation mode recognition

Transportation mode recognition plays an important role in discovering life patterns from people's physical behavior. Learning knowledge from mobile sensing data enables transportation mode recognition on mobile phone. However, existing transportation mode recognition methods are mostly based on fixed recognition models, which do not consider the diversities in different users and their transportation context. In this paper, an online sequential extreme learning machine based transfer learning method (TransELM) is proposed to recognize various transportation modes. TransELM is mainly comprised of three steps: firstly, an initial ELM classifier is trained on the labeled training data from the source domain; secondly, the mean and standard deviation are calculated as multi-class trustable intervals in source domain, and then the partially trustable samples are effectively extracted from the target domain; thirdly, the trustable samples are integrated, where an incremental OSELM method is employed to update the original ELM classifier. Experimental results show that TransELM obtains higher accuracy than the traditional ELM classifier in real world transportation mode recognition problems.