Virtual reality(VR)technology revitalises rehabilitation training by creating rich,interactive virtual rehabilitation scenes and tasks that deeply engage patients.Robotics with immersive VR environments have the poten...Virtual reality(VR)technology revitalises rehabilitation training by creating rich,interactive virtual rehabilitation scenes and tasks that deeply engage patients.Robotics with immersive VR environments have the potential to significantly enhance the sense of immersion for patients during training.This paper proposes a rehabilitation robot system.The system integrates a VR environment,the exoskeleton entity,and research on rehabilitation assessment metrics derived from surface electromyographic signal(sEMG).Employing more realistic and engaging virtual stimuli,this method guides patients to actively participate,thereby enhancing the effectiveness of neural connection reconstruction—an essential aspect of rehabilitation.Furthermore,this study introduces a muscle activation model that merges linear and non-linear states of muscle,avoiding the impact of non-linear shape factors on model accuracy present in traditional models.A muscle strength assessment model based on optimised generalised regression(WOAGRNN)is also proposed,with a root mean square error of 0.017,347 and a mean absolute percentage error of 1.2461%,serving as critical assessment indicators for the effectiveness of rehabilitation.Finally,the system is preliminarily applied in human movement experiments,validating the practicality and potential effectiveness of VRcentred rehabilitation strategies in medical recovery.展开更多
Supernumerary robotic arms(SuperLimb)are a new type of wearable robot that works closely with humans as a third hand to augment human operation capability.Accurate conveyance of wearers'intentions,allocation of ro...Supernumerary robotic arms(SuperLimb)are a new type of wearable robot that works closely with humans as a third hand to augment human operation capability.Accurate conveyance of wearers'intentions,allocation of roles,and humancentered interaction considerations are the key points in the process of human-SuperLimb collaboration.This paper proposes a human-centered intention-guided leader-follower controller that relies on the dynamic modeling of SuperLimb with application to load-carrying scenarios.The proposed leader-follower controller takes the human as the leader and the SuperLimb as the follower,achieving effective information communication,autonomous coordination,and good force compliance between SuperLimb,humans,and the environment under human safety assurance.First,the human-SuperLimb dynamic system is modeled to achieve force interaction with the environment and wearer.Second,to achieve the precise intention extraction of humans,pose data from five visual odometry sensors are fused to capture the human state,the generalized position,the velocity of hands,and the surface electromyography signals from two myoelectric bracelets sensors are processed to recognize the natural hand gestures during load-carrying scenarios by a designed Swin transformer network.Then,based on the real-time distance detection between human and mechanical limbs,the security assurance and force-compliant interaction of the human-SuperLimb system are realized.Finally,the human hand muscle intention recognition,human-robot safety strategy verification,and comparative load-carrying experiments with and without the proposed method are conducted on the SuperLimb prototype.Results showed that the task parameters are well estimated to produce more reasonable planning trajectories,and SuperLimb could well understand the wearer's intentions to switch different SuperLimb actions.The proposed sensor-based human-robot communication framework motivates future studies of other collaboration scenes for SuperLimb applications.展开更多
基金National Key Research and Development Program of China,Grant/Award Number:2022YFB4700701National Outstanding Youth Science Fund Project of National Natural Science Foundation of China,Grant/Award Number:52025054。
文摘Virtual reality(VR)technology revitalises rehabilitation training by creating rich,interactive virtual rehabilitation scenes and tasks that deeply engage patients.Robotics with immersive VR environments have the potential to significantly enhance the sense of immersion for patients during training.This paper proposes a rehabilitation robot system.The system integrates a VR environment,the exoskeleton entity,and research on rehabilitation assessment metrics derived from surface electromyographic signal(sEMG).Employing more realistic and engaging virtual stimuli,this method guides patients to actively participate,thereby enhancing the effectiveness of neural connection reconstruction—an essential aspect of rehabilitation.Furthermore,this study introduces a muscle activation model that merges linear and non-linear states of muscle,avoiding the impact of non-linear shape factors on model accuracy present in traditional models.A muscle strength assessment model based on optimised generalised regression(WOAGRNN)is also proposed,with a root mean square error of 0.017,347 and a mean absolute percentage error of 1.2461%,serving as critical assessment indicators for the effectiveness of rehabilitation.Finally,the system is preliminarily applied in human movement experiments,validating the practicality and potential effectiveness of VRcentred rehabilitation strategies in medical recovery.
基金supported by the National Outstanding Youth Science Fund Project of the National Natural Science Foundation of China(Grant No.52025054)the Youth Science Fund Project of the National Natural Science Foundation of China(Grant No.52105016)。
文摘Supernumerary robotic arms(SuperLimb)are a new type of wearable robot that works closely with humans as a third hand to augment human operation capability.Accurate conveyance of wearers'intentions,allocation of roles,and humancentered interaction considerations are the key points in the process of human-SuperLimb collaboration.This paper proposes a human-centered intention-guided leader-follower controller that relies on the dynamic modeling of SuperLimb with application to load-carrying scenarios.The proposed leader-follower controller takes the human as the leader and the SuperLimb as the follower,achieving effective information communication,autonomous coordination,and good force compliance between SuperLimb,humans,and the environment under human safety assurance.First,the human-SuperLimb dynamic system is modeled to achieve force interaction with the environment and wearer.Second,to achieve the precise intention extraction of humans,pose data from five visual odometry sensors are fused to capture the human state,the generalized position,the velocity of hands,and the surface electromyography signals from two myoelectric bracelets sensors are processed to recognize the natural hand gestures during load-carrying scenarios by a designed Swin transformer network.Then,based on the real-time distance detection between human and mechanical limbs,the security assurance and force-compliant interaction of the human-SuperLimb system are realized.Finally,the human hand muscle intention recognition,human-robot safety strategy verification,and comparative load-carrying experiments with and without the proposed method are conducted on the SuperLimb prototype.Results showed that the task parameters are well estimated to produce more reasonable planning trajectories,and SuperLimb could well understand the wearer's intentions to switch different SuperLimb actions.The proposed sensor-based human-robot communication framework motivates future studies of other collaboration scenes for SuperLimb applications.
