Environment-based assistance modulation for a hip exosuit via computer vision

• Verfasst von: Tricomi, Enrica [VerfasserIn]
• Verfasst von: Mossini, Mirko [VerfasserIn]
• Verfasst von: Missiroli, Francesco [VerfasserIn]
• Verfasst von: Lotti, Nicola [VerfasserIn]
• Verfasst von: Zhang, Xiaohui [VerfasserIn]
• Verfasst von: Xiloyannis, Michele [VerfasserIn]
• Verfasst von: Roveda, Loris [VerfasserIn]
• Verfasst von: Masia, Lorenzo [VerfasserIn]
• Titel: Environment-based assistance modulation for a hip exosuit via computer vision
• Verf.angabe: Enrica Tricomi, Mirko Mossini, Francesco Missiroli, Nicola Lotti, Xiaohui Zhang, Michele Xiloyannis, Loris Roveda, Lorenzo Masia
• E-Jahr: 2023
• Jahr: 13 March 2023
• Umfang: 8 S.
• Fussnoten: Gesehen am 22.05.2023
• Titel Quelle: Enthalten in: IEEE Robotics and automation letters
• Ort Quelle: New York, N.Y. : Institute of Electrical and Electronics Engineers, 2016
• Jahr Quelle: 2023
• Band/Heft Quelle: 8(2023), 5 vom: März, Seite 2550-2557
• ISSN Quelle: 2377-3766
• DOI: doi:10.1109/LRA.2023.3256135
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: adaptive walking assistance
• Sach-SW: assistive robotics
• Sach-SW: Cameras
• Sach-SW: computer vision
• Sach-SW: Computer vision
• Sach-SW: Exosuits
• Sach-SW: Hip
• Sach-SW: Legged locomotion
• Sach-SW: Modulation
• Sach-SW: Real-time systems
• Sach-SW: Stairs
• K10plus-PPN: 184588552X

Abstract

Just like in humans vision plays a fundamental role in guiding adaptive locomotion, when designing the control strategy for a walking assistive technology, the use of computer vision may substantially improve modulation of the assistance based on the external environment. In this letter, we developed a hip exosuit controller able to distinguish among three different walking terrains through the use of an RGB camera and to adapt the assistance accordingly. The system was tested with seven healthy participants walking throughout an overground path comprising of staircases and level ground. Subjects performed the task with the exosuit disabled (Exo Off), constant assistance profile (Vision Off), and with assistance modulation (Vision On). Our results showed that the controller was able to classify in real-time the path in front of the user with an overall accuracy per class above the 85%, and to perform assistance modulation accordingly. Evaluation related to the effects on the user showed that Vision On was able to outperform the other two conditions: we obtained significantly higher metabolic savings than Exo Off, with a peak of \approx -20% when climbing up the staircase and \approx -16% in the overall path, and than Vision Off when ascending or descending stairs. Such advancements in the field may yield to a step forward for the exploitation of lightweight walking assistive technologies in real-life scenarios.