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 towa...
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| Veröffentlicht in: | IEEE transactions on robotics Jg. 41; S. 3462 - 3479 |
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| Abstract | 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 <inline-formula><tex-math notation="LaTeX">93.0\pm 1.1\%</tex-math></inline-formula>. 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 (<inline-formula><tex-math notation="LaTeX">-18.9 \pm 4.1\%</tex-math></inline-formula> versus <inline-formula><tex-math notation="LaTeX">-5.2 \pm 4.1\%</tex-math></inline-formula>) and descent (<inline-formula><tex-math notation="LaTeX">-10.1 \pm 3.6\%</tex-math></inline-formula> versus <inline-formula><tex-math notation="LaTeX">-4.7 \pm 4.8\%</tex-math></inline-formula>). 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 (<inline-formula><tex-math notation="LaTeX">+33.9\pm 8.8\%</tex-math></inline-formula>) and decrease while descending stairs (<inline-formula><tex-math notation="LaTeX">-17.4\pm 6.0\%</tex-math></inline-formula>) 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. |
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| AbstractList | 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 <inline-formula><tex-math notation="LaTeX">93.0\pm 1.1\%</tex-math></inline-formula>. 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 (<inline-formula><tex-math notation="LaTeX">-18.9 \pm 4.1\%</tex-math></inline-formula> versus <inline-formula><tex-math notation="LaTeX">-5.2 \pm 4.1\%</tex-math></inline-formula>) and descent (<inline-formula><tex-math notation="LaTeX">-10.1 \pm 3.6\%</tex-math></inline-formula> versus <inline-formula><tex-math notation="LaTeX">-4.7 \pm 4.8\%</tex-math></inline-formula>). 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 (<inline-formula><tex-math notation="LaTeX">+33.9\pm 8.8\%</tex-math></inline-formula>) and decrease while descending stairs (<inline-formula><tex-math notation="LaTeX">-17.4\pm 6.0\%</tex-math></inline-formula>) 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. |
| Author | Piccolo, Giuseppe Gionfrida, Letizia Missiroli, Francesco Russo, Federica Zhang, Xiaohui Tricomi, Enrica Ficuciello, Fanny Xiloyannis, Michele Ferrari, Sandro Masia, Lorenzo |
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| SubjectTerms | Adaptive walking assistance Cameras Computer vision exosuits Geometric modeling geometric modeling (GM) Hip Legged locomotion physics-informed computer vision (CV) Real-time systems Robot vision systems Robots Stairs Training |
| Title | Leveraging Geometric Modeling-Based Computer Vision for Context Aware Control in a Hip Exosuit |
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