Leveraging Geometric Modeling-Based Computer Vision for Context Aware Control in a Hip Exosuit

Human beings adapt theirmotor patterns in response to their surroundings, utilizing sensory modalities such as visual inputs. This context-informed adaptivemotor behavior has increased interest in integrating computer vision (CV) algorithms into robotic assistive technologies,marking a shift toward context aware control. However, such integration has rarely been achieved so far, with current methods mostly relying on data-driven approaches. In this study,we introduce a novel control framework for a soft hip exosuit, employing instead a physics-informed CV method grounded on geometric modeling of the captured scene for assistance tuning during stairs and level walking. This approach promises to provide a viable solution that is more computationally efficient and does not depend on training examples. Evaluating the controllerwith six subjects on a path comprising level walking and stairs, we achieved an overall detection accuracy of 93.0 ± 1.1%. CV-based assistance provided significantly greater metabolic benefits compared to non-vision-based assistance, with larger energy reductions relative to being unassisted during stair ascent (-18.9 ± 4.1% versus -5.2 ± 4.1%) and descent (-10.1 ± 3.6% versus -4.7 ± 4.8%). Such a result is a consequence of the adaptive nature of the device, enabled by the context aware controller that allowed for more effective walking support, i.e., the assistive torque showed a significant increase while ascending stairs (+33.9 ± 8.8%) and decrease while descending stairs (-17.4 ± 6.0%) compared to a condition without assistance modulation enabled by vision. These results highlight the potential of the approach, promoting effective real-time embedded applications in assistive robotics.