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Smart Wearable Robots with Bioinspired Sensory-Motor Skills (BioMot)
Date du début: 1 oct. 2013, Date de fin: 30 sept. 2016 PROJET  TERMINÉ 

Wearable robots (WR) are person-oriented devices, usually in the form of exoskeletons. These devices are worn by human operators to enhance or support a daily function, such as walking. WRs find applications in the enhancement of intact operators or in clinical environments, e.g. rehabilitation of gait function in neurologically injured patients. Most advanced WRs for human locomotion still fail to provide the real-time adaptability and flexibility presented by humans when confronted with natural perturbations, due to voluntary control or environmental constraints. Current WRs are extra body structures inducing fixed motion patterns on its user.The main objective of the project is to improve existing wearable robotic exoskeletons exploiting dynamic sensorimotor interactions and developing cognitive capabilities that can lead to symbiotic gait behavior in the interaction of a human with a wearable robot.BioMot will use and adapt available tools to reveal how neural circuits generate behavior, and to yield new strategies for co-adaptation during use of wearable robots for walking:- The systems will fuse the information from both interaction with the environment and human gait dynamics, and exploit this information for safe and natural locomotion adjusted to the user's intentions and capabilities.- The proposed bioinspired cognition for WRs will consider the interplay between biomechanical and sensorimotor levels with a developmentally guided coordination.- It will establish advanced computational neuromusculoskeletal models based on dynamic sensorimotor interactions that are suitable as controllers for symbiotic interaction.- BioMot will produce guidelines and benchmarking for WRs to become available for human locomotion in realistic scenarios.BioMot proposes a cognitive architecture for WRs exploiting neuronal control and learning mechanisms which main goal is to enable positive co-adaptation and seamless interaction with humans.



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