Intention Detection of Gait Adaptation in Natural Settings

TL;DR

This paper introduces a novel gait adaptation detection method using a single RGB camera and wireless EEG in natural environments, enabling real-time monitoring of gait adjustments without specialized equipment.

Contribution

It presents a new scheme for detecting gait adaptation in real-world settings using minimal equipment, combining visual and EEG data.

Findings

Successfully detects gait adaptation steps

Accurately identifies pace adjustments to different speeds

Operates effectively in natural, equipment-free environments

Abstract

Gait adaptation is an important part of gait analysis and its neuronal origin and dynamics has been studied extensively. In neurorehabilitation, it is important as it perturbs neuronal dynamics and allows patients to restore some of their motor function. Exoskeletons and robotics of the lower limbs are increasingly used to facilitate rehabilitation as well as supporting daily function. Their efficiency and safety depends on how well can sense the human intention to move and adapt the gait accordingly. This paper presents a gait adaptation scheme in natural settings. It allows monitoring of subjects in more realistic environment without the requirement of specialized equipment such as treadmill and foot pressure sensors. We extract gait characteristics based on a single RBG camera whereas wireless EEG signals are monitored simultaneously. We demonstrate that the method can not only successfully detect adaptation steps but also detect efficiently whether the subject adjust their pace to higher or lower speed.