HeMoFi4Q: Morse Communication Based on Wi-Fi and Head Motion for Quadriplegia With Environmental Robustness

Abstract

Quadriplegics face a communication obstacle as their physical abilities are restricted, leaving them unable to speak or use their limbs, with only their upper neck being mobile. So, we propose a recognition system and a new communication language utilizing Morse code and head movements, to break this barrier. We aim to overcome the limitations of camera-based and wearable-sensor methods, including occlusion, privacy concerns, and user inconvenience. The goal is to passively detect quadriplegics’ head movements and map them to their corresponding character. The dataset including all 26 alphabet letters, was gathered in various settings, including single-user and multi-human environments, with multiple locations for each setting. For evaluation, 2% samples are randomly selected from the unseen environment to be used with the seen environment as a training dataset. Based on the results, our system demonstrates practical feasibility for real-world implementation, with accuracy rates of 94% and 80% achieved in single-user and multi-human environments, respectively.

Keywords

• Wireless sensing
• Wi-Fi CSI
• quadriplegia
• deep learning
• location diversity