One-shot learning HMI for people with disabilities

Abstract

For people with physical disabilities, it is often desirable to regain control over their personal environment and communication tools. This paper introduces a novel Human- Machine Interface (HMI) using one-shot learning for individualized control signals without extensive training or specialized hardware. Our work suggests a modular system that utilizes common, easily accessible devices like webcams to interpret user-defined gestures and commands through a single demonstration. As a feasibility study on healthy volunteers, we investigate the control of a computer mouse by head movements only. We demonstrate the technical details of the HMI and discuss its potential applications in enhancing the autonomy and interaction capabilities of users with disabilities. By combining usercentric design principles with the advancements in one-shot learning, we aim to forge a more inclusive, accessible path forward in the development of assistive technologies.

Keywords

• one-shot learning
• webcam
• neural network
• human-machine interface