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About

William McNally is a researcher at the forefront of intelligent assistive robotics, specializing in computer vision, deep learning, and the automated control of robotic lower-limb prostheses and exoskeletons. His work addresses a critical challenge in rehabilitation engineering: enabling powered assistive devices to autonomously recognize and adapt to real-world locomotion environments, reducing the cognitive burden placed on users with physical disabilities or age-related mobility limitations. McNally's most influential contribution, "Environment Classification for Robotic Leg Prostheses and Exoskeletons Using Deep Convolutional Neural Networks" (2022, 79 citations), demonstrated the power of image-based sensing over traditional mechanical and inertial approaches for locomotion mode recognition. His 2019 pioneering study (70 citations) was among the first to apply machine vision and deep learning to wearable biomechatronic control, drawing inspiration from autonomous vehicle technology. Complementing this body of work, McNally developed the ExoNet Database, a publicly available dataset of locomotion environment images designed to accelerate community-wide research in this emerging field. With nearly 200 cumulative citations across a focused publication record, McNally's research is reshaping how robotic assistive devices interact with the world, offering meaningful quality-of-life improvements for users navigating complex, everyday environments.