Control Interface for Hands-free Navigation of Standing Mobility Vehicles based on Upper-Body Natural Movements

TL;DR

This paper introduces a novel hands-free human-machine interface for controlling standing mobility vehicles using upper-body natural movements, aiming to assist users with lower-body impairments.

Contribution

It presents a new HMI based on gaze tracking and body posture sensing, with a personalized intent recognition system for improved control of mobility robots.

Findings

Main control muscles are Rectus Abdominis and Erector Spinae.

Joystick outperforms HMI in usability and controllability.

HMI enhances perceived safety and anthropomorphism.

Abstract

In this paper, we propose and evaluate a novel human-machine interface (HMI) for controlling a standing mobility vehicle or person carrier robot, aiming for a hands-free control through upper-body natural postures derived from gaze tracking while walking. We target users with lower-body impairment with remaining upper-body motion capabilities. The developed HMI bases on a sensing array for capturing body postures; an intent recognition algorithm for continuous mapping of body motions to robot control space; and a personalizing system for multiple body sizes and shapes. We performed two user studies: first, an analysis of the required body muscles involved in navigating with the proposed control; and second, an assessment of the HMI compared with a standard joystick through quantitative and qualitative metrics in a narrow circuit task. We concluded that the main user control contribution comes from Rectus Abdominis and Erector Spinae muscle groups at different levels. Finally, the comparative study showed that a joystick still outperforms the proposed HMI in usability perceptions and controllability metrics, however, the smoothness of user control was similar in jerk and fluency. Moreover, users' perceptions showed that hands-free control made it more anthropomorphic, animated, and even safer.