Simulation for a Mems-Based CTRNN Ultra-Low Power Implementation of Human Activity Recognition

Simulation for a Mems-Based CTRNN Ultra-Low Power Implementation of Human Activity Recognition

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....

Full description

Bibliographic Details
Main Authors: Muhammad Emad-Ud-Din, Mohammad H. Hasan, Roozbeh Jafari, Siavash Pourkamali, Fadi Alsaleem
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-09-01
Series:Frontiers in Digital Health
Subjects:
MEMS
human activity recognition
LSTM – long short-term memory
continuous time recurrent neural network
recurrent neural networks
Online Access:https://www.frontiersin.org/articles/10.3389/fdgth.2021.731076/full

Internet

https://www.frontiersin.org/articles/10.3389/fdgth.2021.731076/full

Similar Items