Research Repository

Neural Networks and Learning Systems for Human Machine Interfacing

Ju, Zhaojie and Liu, Jinguo and Huang, Yong An and Kubota, Naoyuki and Gan, John Q (2020) 'Neural Networks and Learning Systems for Human Machine Interfacing.' Neurocomputing, 390. pp. 196-197. ISSN 0925-2312

1-s2.0-S092523121931447X-main.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (276kB) | Preview


With developments of the sensor and computing technologies, human-machine interfaces (HMIs) are designed to meet the increasing user demands of machines and systems. This is because human effects are becoming the key issues to allow some advanced mechanical devices, such as robots and biometric systems, to perform complicate tasks intelligently in an unknown environment. An effective HMI with learning ability can process, interpret, recognize, and simulate the intention and behaviors of human beings, and then utilize intelligent algorithms to drive the machine devices. The HMIs also enable us to bring humanistic intelligence and actions in robotic devices, biometric systems and other machines through two-ways interactions, such as using deep neural networks. In recent years, a growing number of researchers and studies focusing on this area have clearly demonstrated the importance of learning systems for HMIs.

Item Type: Article
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 08 Nov 2019 09:47
Last Modified: 15 Jan 2022 01:30

Actions (login required)

View Item View Item