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

Human beings adapt their motor patterns in response to their surroundings, utilizing sensory modalities such as visual inputs. This context-informed adaptive motor 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 controller with six subjects on a path comprising level walking and stairs, we achieved an overall detection accuracy of $93.0\pm 1.1\%$. 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 ($-18.9 \pm 4.1\%$ versus $-5.2 \pm 4.1\%$) and descent ($-10.1 \pm 3.6\%$ versus $-4.7 \pm 4.8\%$). 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 ($+33.9\pm 8.8\%$) and decrease while descending stairs ($-17.4\pm 6.0\%$) 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.