Binarized-BLSTM-RNN based Human Activity Recognition

High computational complexity hinders the widespread usage of neural networks, especially in mobile devices, which are often the basis of fine-grained localization technology for ubiquitous health monitoring, context awareness, and indoor location tracking. In this paper, we present a binarized recurrent neural network whose weight parameters, input, and intermediate hidden layer output signals, are all binary-valued, and require only basic bit logic for the evaluation and training process. The proposed Binarized Long Short-Term Memory Network (B-BLSTM-RNN) is especially suitable for resource-constrained environments since it replaces either floating or fixed-point arithmetic with significantly more efficient bitwise operations. The model is based on a bidirectional Long Short-Term Memory Recurrent Neural Network (BLSTM-RNN). Designed to take contextual information into account, the network can process data gathered from different positions, resulting in a system, that's invariant to transformations and distortions of the input patterns. During the forward pass, the B-BLSTM drastically reduce memory size and accesses, and replace most arithmetic operations with bit-wise operations, which is expected to substantially improve power-efficiency. The binarized network is simple, accurate, efficient, and works on challenging gesture recognition tasks using raw MEM data. To validate the effectiveness of the network we conduct three sets of experiments. We achieved a classification accuracy with a the proposed network of about 90% which only 2% less than the full-precision network. We also compare our method with recent methods and outperform these methods by large margins on the conducted datasets.