A Lightweight and Accurate RNN in Wearable Embedded Systems for Human Activity Recognition

Human activity recognition (HAR) is an important technology for a wide range of applications including elderly people monitoring, ambient assisted living, sport and fitness activities. The aim of this paper is to address the HAR task directly on a wearable device, implementing a recurrent neural network (RNN) on a low cost, low power microcontroller, ensuring the required performance in terms of accuracy and low complexity. To reach this goal we first develop a lightweight RNN on the Human Activity Recognition Using Smartphones dataset in order to accurately detect human activity and then we port the RNN to the embedded device Cloud-JAM L4, based on an STM32 microcontroller. Experimental results show that this HAR RNN-based detector can be effectively implemented on the chosen constrained-resource system, achieving an accuracy of about 90.50% with a very low memory cost (40.883 KB) and inference time (67.131 ms), allowing the design of a wearable embedded system for human activity recognition.