Based on the regularity nature of lower-limb motion,an intent pattern recognition approach for above-knee prosthesis is proposed in this paper. To remedy the defects of recognizer based on electromyogram(EMG), we deve...Based on the regularity nature of lower-limb motion,an intent pattern recognition approach for above-knee prosthesis is proposed in this paper. To remedy the defects of recognizer based on electromyogram(EMG), we develop a pure mechanical sensor architecture for intent pattern recognition of lower-limb motion. The sensor system is composed of an accelerometer, a gyroscope mounted on the prosthetic socket, and two pressure sensors mounted under the sole. To compensate the delay in the control of prosthesis, the signals in the stance phase are used to predict the terrain and speed in the swing phase. Specifically, the intent pattern recognizer utilizes intraclass correlation coefficient(ICC) according to the Cartesian product of walking speed and terrain. Moreover, the sensor data are fused via DempsterShafer's theory. And hidden Markov model(HMM) is used to recognize the realtime motion state with the reference of the prior step. The proposed method can infer the prosthesis user's intent of walking on different terrain, which includes level ground,stair ascent, stair descent, up and down ramp. The experiments demonstrate that the intent pattern recognizer is capable of identifying five typical terrain-modes with the rate of 95.8%. The outcome of this investigation is expected to substantially improve the control performance of powered above-knee prosthesis.展开更多
With the rise in the aging population,an increase in the number of semidisabled elderly individuals has been noted,leading to notable challenges in medical and healthcare,exacerbated by a shortage of nursing staff.Thi...With the rise in the aging population,an increase in the number of semidisabled elderly individuals has been noted,leading to notable challenges in medical and healthcare,exacerbated by a shortage of nursing staff.This study aims to enhance the human feature recognition capabilities of bath scrubbing robots operating in a water fog environment.The investigation focuses on semantic segmentation of human features using deep learning methodologies.Initially,3D point cloud data of human bodies with varying sizes are gathered through light detection and ranging to establish human models.Subsequently,a hybrid filtering algorithm was employed to address the impact of the water fog environment on the modeling and extraction of human regions.Finally,the network is refined by integrating the spatial feature extraction module and the channel attention module based on PointNet.The results indicate that the algorithm adeptly identifies feature information for 3D human models of diverse body sizes,achieving an overall accuracy of 95.7%.This represents a 4.5%improvement compared with the PointNet network and a 2.5%enhancement over mean intersection over union.In conclusion,this study substantially augments the human feature segmentation capabilities,facilitating effective collaboration with bath scrubbing robots for caregiving tasks,thereby possessing significant engineering application value.展开更多
The accuracy of a fracture reduction robot(FRR)is critical for ensuring the safety of surgery.Improving the repositioning accuracy of a FRR,reducing the error,and realizing a safer and more stable folding motion is cr...The accuracy of a fracture reduction robot(FRR)is critical for ensuring the safety of surgery.Improving the repositioning accuracy of a FRR,reducing the error,and realizing a safer and more stable folding motion is critical.To achieve this,a sparrow search algorithm(SSA)based on the Levy flight operator was proposed in this study for self-tuning the robot controller parameters.An inverse kinematic analysis of the FRR was also performed.The robot dynamics model was established using Simulink,and the inverse dynamics controller for the fracture reduction mechanism was designed using the computed torque control method.Both simulation and physical experiments were also performed.The actual motion trajectory of the actuator drive rod and its error with a desired trajectory was obtained through simulation.An optimized Levy-sparrow search algorithm(Levy-SSA)crack reduction robot controller demonstrated an overall reduction of two orders of magnitude in the reduction error,with an average error reduction of 98.74%compared with the traditional unoptimized controller.The Levy-SSA increased the convergence of the crack reduction robot control system to the optimal solution,improved the accuracy of the motion trajectory,and exhibited important implications for robot controller optimization.展开更多
The lower-limb robotic prostheses can provide assistance for amputees’daily activities by restoring the biomechanical functions of missing limb(s).To set proper control strategies and develop the corresponding contro...The lower-limb robotic prostheses can provide assistance for amputees’daily activities by restoring the biomechanical functions of missing limb(s).To set proper control strategies and develop the corresponding controller for robotic prosthesis,a prosthesis user’s intent must be acquired in time,which is still a major challenge and has attracted intensive attentions.This work focuses on the robotic prosthesis user’s locomotion intent recognition based on the noninvasive sensing methods from the recognition task perspective(locomotion mode recognition,gait event detection,and continuous gait phase estimation)and reviews the state-ofthe-art intent recognition techniques in a lower-limb prosthesis scope.The current research status,including recognition approach,progress,challenges,and future prospects in the human’s intent recognition,has been reviewed.In particular for the recognition approach,the paper analyzes the recent studies and discusses the role of each element in locomotion intent recognition.This work summarizes the existing research results and problems and contributes a general framework for the intent recognition based on lower-limb prosthesis.展开更多
基金supported in part by the National Nature Science Fundation(61174009,61203323)Youth Foundation of Hebei Province(F2016202327)+3 种基金the Colleges and Universities in Hebei Province Science and Technology Research Project(ZC2016020)supported in part by Key Project of NSFC(61533009)111 Project(B08015)Research Project(JCYJ20150403161923519)
文摘Based on the regularity nature of lower-limb motion,an intent pattern recognition approach for above-knee prosthesis is proposed in this paper. To remedy the defects of recognizer based on electromyogram(EMG), we develop a pure mechanical sensor architecture for intent pattern recognition of lower-limb motion. The sensor system is composed of an accelerometer, a gyroscope mounted on the prosthetic socket, and two pressure sensors mounted under the sole. To compensate the delay in the control of prosthesis, the signals in the stance phase are used to predict the terrain and speed in the swing phase. Specifically, the intent pattern recognizer utilizes intraclass correlation coefficient(ICC) according to the Cartesian product of walking speed and terrain. Moreover, the sensor data are fused via DempsterShafer's theory. And hidden Markov model(HMM) is used to recognize the realtime motion state with the reference of the prior step. The proposed method can infer the prosthesis user's intent of walking on different terrain, which includes level ground,stair ascent, stair descent, up and down ramp. The experiments demonstrate that the intent pattern recognizer is capable of identifying five typical terrain-modes with the rate of 95.8%. The outcome of this investigation is expected to substantially improve the control performance of powered above-knee prosthesis.
基金This work was supported by National Key R&D Program of China(2020YFC2007700).
文摘With the rise in the aging population,an increase in the number of semidisabled elderly individuals has been noted,leading to notable challenges in medical and healthcare,exacerbated by a shortage of nursing staff.This study aims to enhance the human feature recognition capabilities of bath scrubbing robots operating in a water fog environment.The investigation focuses on semantic segmentation of human features using deep learning methodologies.Initially,3D point cloud data of human bodies with varying sizes are gathered through light detection and ranging to establish human models.Subsequently,a hybrid filtering algorithm was employed to address the impact of the water fog environment on the modeling and extraction of human regions.Finally,the network is refined by integrating the spatial feature extraction module and the channel attention module based on PointNet.The results indicate that the algorithm adeptly identifies feature information for 3D human models of diverse body sizes,achieving an overall accuracy of 95.7%.This represents a 4.5%improvement compared with the PointNet network and a 2.5%enhancement over mean intersection over union.In conclusion,this study substantially augments the human feature segmentation capabilities,facilitating effective collaboration with bath scrubbing robots for caregiving tasks,thereby possessing significant engineering application value.
基金supported by the Natural Science Foundation of Guangdong Province(2022A1515010487)Shenzhen Science and Technology Innovation Program(JCYJ20210324103800001)Shenzhen Science and Technology Innovation Program(JCYJ20220530112609022).
文摘The accuracy of a fracture reduction robot(FRR)is critical for ensuring the safety of surgery.Improving the repositioning accuracy of a FRR,reducing the error,and realizing a safer and more stable folding motion is critical.To achieve this,a sparrow search algorithm(SSA)based on the Levy flight operator was proposed in this study for self-tuning the robot controller parameters.An inverse kinematic analysis of the FRR was also performed.The robot dynamics model was established using Simulink,and the inverse dynamics controller for the fracture reduction mechanism was designed using the computed torque control method.Both simulation and physical experiments were also performed.The actual motion trajectory of the actuator drive rod and its error with a desired trajectory was obtained through simulation.An optimized Levy-sparrow search algorithm(Levy-SSA)crack reduction robot controller demonstrated an overall reduction of two orders of magnitude in the reduction error,with an average error reduction of 98.74%compared with the traditional unoptimized controller.The Levy-SSA increased the convergence of the crack reduction robot control system to the optimal solution,improved the accuracy of the motion trajectory,and exhibited important implications for robot controller optimization.
基金supported by the National Key R&D Program of China(Nos.2018YFB1307302,2018YFF0300606),the National Natural Science Foundation of China(No.91648207)the Beijing Natural Science Foundation(No.L182001)the Beijing Municipal Science and Technology Project(No.Z181100009218007).
文摘The lower-limb robotic prostheses can provide assistance for amputees’daily activities by restoring the biomechanical functions of missing limb(s).To set proper control strategies and develop the corresponding controller for robotic prosthesis,a prosthesis user’s intent must be acquired in time,which is still a major challenge and has attracted intensive attentions.This work focuses on the robotic prosthesis user’s locomotion intent recognition based on the noninvasive sensing methods from the recognition task perspective(locomotion mode recognition,gait event detection,and continuous gait phase estimation)and reviews the state-ofthe-art intent recognition techniques in a lower-limb prosthesis scope.The current research status,including recognition approach,progress,challenges,and future prospects in the human’s intent recognition,has been reviewed.In particular for the recognition approach,the paper analyzes the recent studies and discusses the role of each element in locomotion intent recognition.This work summarizes the existing research results and problems and contributes a general framework for the intent recognition based on lower-limb prosthesis.