Architecture Commons^™

Simulation For A Mems-Based Ctrnn Ultra-Low Power Implementation Of Human Activity Recognition, Muhammad Emad-Ud-Din, Mohammad H. Hasan, Roozbeh Jafari, Siavash Pourkamali, Fadi M. Alsaleem

Durham School of Architectural Engineering and Construction: Faculty Publications

This paper presents an energy-efficient classification framework that performs human activity recognition (HAR). Typically, HAR classification tasks require a computational platform that includes a processor and memory along with sensors and their interfaces, all of which consume significant power. The presented framework employs microelectromechanical systems (MEMS) based Continuous Time Recurrent Neural Network (CTRNN) to performHAR tasks very efficiently. In a real physical implementation, we show that the MEMS-CTRNN nodes can perform computing while consuming power on a nano-watts scale compared to the micro-watts state-of-the-art hardware. We also confirm that this huge power reduction doesn’t come at the expense of reduced …