Human activity recognition based on the smartphone sensors has the potential to impact a wide range of applications such as healthcare, smart home, and remote monitoring. For simple activities like “Sit” and “Walk”, it can be distinguished relatively easily. However, for similar activities with respect to transportations, such as “Train”, “Bus” and “Subway”, it still remains an open problem. To tackle this problem, this paper uses the recently proposed Independently Recurrent Neural Network (IndRNN) to process data of different lengths in order to capture the temporal patterns at different granularities. The unique property of IndRNN in terms of the gradient propagation allows it to construct deep RNN networks and extract good features. The proposed method has been applied and submitted to SHL recognition challenge as “UOW AMRL”.