An Hardware Recurrent Neural Network for Wearable Devices

Abstract

Automatic classification of time series signals acquired by wearable or portable devices covers a central role in many critical healthcare applications, such as heart rate monitoring [1], sleep apnea study [2], gait analysis [3] and fall detection [4]. In recent years, many approaches have been adopted, including a wide range of methods ranging from threshold-based algorithms to Deep Learning techniques. The threshold-based methods have the advantage of being simple and not heavy from a computational point of view, but at the cost of low accuracy. Deep Learning approaches ensure a higher precision, but the computational complexity is increased. This is a critical issue for wearable devices because a high computational complexity strongly affects the processing time and the battery life. In this paper, we propose a hardware architecture for time series analysis using Recurrent Neural Networks (RNNs) exploiting FPGA technology. The architecture is validated with three-axial accelerometer data acquired by a wearable device used for automatic fall detection. The experimental results show that the proposed architecture outperforms state of the art solutions both in terms of processing time and power consumption.