As wearable robotic devices for human movement assistance and rehabilitation transition into real-world applications, their ability to autonomously and seamlessly adapt to varying environmental conditions and user needs is crucial. Lower-limb exoskeletons and prostheses, for example, must dynamically adjust their assistance profiles to accommodate different motor activities, such as level-ground walking or stair climbing. Achieving this requires not only recognizing user intentions but also gathering comprehensive information about the surroundings.
Computer vision offers rich, direct, and interpretable data that surpasses non-visual sensors like encoders and inertial measurement units, making it a promising tool for enhancing context awareness in wearable robots. However, integrating computer vision into wearable robotic control presents several challenges, including:
- Ensuring the real-time feasibility of vision model outputs
- Maintaining model robustness across diverse mobility contexts and dynamic user movements
- Effectively fusing onboard sensor data with visual information
This workshop aims to address these challenges by exploring the latest engineering solutions for computer vision-based human motion tracking and control strategies for wearable robotic systems designed to augment human locomotion. By bridging the gap between wearable robotics and computer vision researchers, as well as fostering collaboration between academia and industry, we seek to provide a roadmap for developing robust, adaptable, and context-aware vision-based control frameworks that can be successfully translated from the lab to real-world applications.
