Fusion of Human Gaze and Machine Vision for Predicting Intended Locomotion Mode.

Predicting the user's intended locomotion mode is critical for wearable robot control to assist the user's seamless transitions when walking on changing terrains. Although machine vision has recently proven to be a promising tool in identifying upcoming terrains in the travel path, existing approaches are limited to environment perception rather than human intent recognition that is essential for coordinated wearable robot operation. Hence, in this study, we aim to develop a novel system that fuses the human gaze (representing user intent) and machine vision (capturing environmental information) for accurate prediction of the user's locomotion mode.

Reliable Vision-Based Grasping Target Recognition for Upper Limb Prostheses.

Computer vision has shown promising potential in wearable robotics applications (e.g., human grasping target prediction and context understanding).

Detection of driver manual distraction via image-based hand and ear recognition.

Driving distraction is a leading cause of fatal car accidents, and almost nine people are killed in the US each day because of distracting activities. Therefore, reducing the number of distraction-affected traffic accidents remains an imperative issue. A novel algorithm for detection of drivers' manual distraction was proposed in this manuscript.

Gaze Fixation Comparisons Between Amputees and Able-bodied Individuals in Approaching Stairs and Level-ground Transitions: A Pilot Study.

This paper aims to investigate the visual strategy of transtibial amputees while they are approaching the transition between level-ground and stairs and compare it with that of able-bodied individuals. To this end, we conducted a pilot study where two transtibial amputee subjects and two able-bodied subjects transitioned from level-ground to stairs and vice versa while wearing eye tracking glasses to record gaze fixations. To investigate how vision functioned to both populations for preparing locomotion on new terrains, gaze fixation behavior before the new terrains were analyzed and compared between two populations across all transition cases in the study.

A Framework for Physiological Response Prediction with Joint Activity State optimization.

Physiological responses are essential for health monitoring. However, modeling the complex interactions be- tween them across activity and environmental factors can be challenging. In this paper, we introduce a framework that identifies the state of an individual based on their activity, trains predictive models for their physiological response within these states, and jointly optimizes for the states and the models.

Visual Terrain Identification and Surface Inclination Estimation for Improving Human Locomotion with a Lower-Limb Prosthetic.

Lower-limb robotic prosthetics can benefit from context awareness to provide comfort and safety to the amputee. In this work, we developed a terrain identification and surface inclination estimation system for a prosthetic leg using visual and inertial sensors. We built a dataset from which images with high sharpness are selected using the IMU signal.

Dynamically Modulated Mask Sparse Tracking.

Visual tracking is a critical task in many computer vision applications such as surveillance and robotics. However, although the robustness to local corruptions has been improved, prevailing trackers are still sensitive to large scale corruptions, such as occlusions and illumination variations. In this paper, we propose a novel robust object tracking technique depends on subspace learning-based appearance model.