When developing a humanoid myo-control hand,not only the mechanical structure should be considered to afford a high dexterity,but also the myoelectric (electromyography,EMG) control capability should be taken into acc...When developing a humanoid myo-control hand,not only the mechanical structure should be considered to afford a high dexterity,but also the myoelectric (electromyography,EMG) control capability should be taken into account to fully accomplish the actuation tasks.This paper presents a novel humanoid robotic myocontrol hand (AR hand Ⅲ) which adopted an underac- tuated mechanism and a forearm myocontrol EMG method.The AR hand Ⅲ has five fingers and 15 joints,and actuated by three embedded motors.Underactuation can be found within each finger and between the rest three fingers (the middle finger,the ring finger and the little finger) when the hand is grasping objects.For the EMG control,two specific methods are proposed:the three-fingered hand gesture configuration of the AR hand Ⅲ and a pattern classification method of EMG signals based on a statistical learning algorithm-Support Vector Machine (SVM).Eighteen active hand gestures of a testee are recognized ef- fectively,which can be directly mapped into the motions of AR hand Ⅲ.An on-line EMG control scheme is established based on two different decision functions:one is for the discrimination between the idle and active modes,the other is for the recog- nition of the active modes.As a result,the AR hand Ⅲ can swiftly follow the gesture instructions of the testee with a time delay less than 100 ms.展开更多
According to the sampling statistics there are over 7 millions limb disabled persons in China from several times of those in the world. For the benefit to the amputees, thousands varies kinds of commencial Products of...According to the sampling statistics there are over 7 millions limb disabled persons in China from several times of those in the world. For the benefit to the amputees, thousands varies kinds of commencial Products of artificial hands, by scientific promotion have ben developed in the recent half century. Among those products the EMG controlling artificial upper limb brings the hope to the amputees. The later are now used widely form the earlest one in 1948.In 1978, Shanghai Jiao Tong University began to study and develop the EMG controlling artificial band mounted to amputes over thousand disabled during a couple of recent decades.The risidual muscles of an amputee are the signal source of the artificial hand controlled by EMG. The evoked EMG which can transmit the brain moving information is one of the bioelectricities from human body. The EMG signal, accumulated at skin surface with surface electrodes, passing through filtering and amplifying circuits controls the movement-this is the main principle of EMG controlling artificial hand.But, owing to the weakness of EMG (μv) and a group of muscles information plus the strong turbulence of electric field (v), the integrated EMG can’t completely reflect the brain moving act and will influence the accuracy of EMG-hand, especially those of the multi-multi-degree of freedom.In 1978, only 57% of the controlling aaccuracy of artificial hand with 3-degree of freedom could be reached by the Herbert’s research. In 80’s, the controlling accuracy raised up to 72% by Denning’s new method. Up to now, the accuracy is still not ideal, eventhrogh the Hi-tech of using pattern-recognitionand artincial neuro-net work. The electronic artificial hand will be considered successful and practical only with the moving accuracy more than 95%.Some research by using implant electrode for detecting the neuro-information or EEG controllingmethod met also dimculties for raising the accuracy of artificial hand.For breaking througll the threshold of accuracy limit, the EMG method as mentioned above must bechanged entirely, A newest creative research work on the electronic artificial hand controlled by a "regenerated finger" made by transplanting a toe to the stump is developing in Shanghai Jiao TongUniversity, which is without precedent in the world.The first experimental amputee using "regenerated finger’ to control an electronic artificial forearm with 3-degree of freedom reaches 100% accuracy of movements (i.e. no error within 100 tests). It has been proved that the use of a "regenerated finger" as a controlling signal command makes it possible to use the electronic artificeal hand with multi-degree of freedom without error Thecombining medical science with engineering in the area of designing an electronic artifical upper limb.Acknowledgement: The authors extend their hearty thanks to the famous academician Dr.Chen Zhongwei (Zhong-Sen Hospital) for their creative effort and successful micro-surgical operation for transplanting toe to stump of an amputee, also to the China Science Foundation for supporting. us the fund to develop this research.展开更多
This paper is concerned with a control method for an exoskeleton ankle with electromyography （EMG） signals. The EMG signals of human ankle and the exoskeleton ankle are introduced. Then a control method is proposed ...This paper is concerned with a control method for an exoskeleton ankle with electromyography （EMG） signals. The EMG signals of human ankle and the exoskeleton ankle are introduced. Then a control method is proposed to control the exoskeleton ankle using the EMG signals. The feed-forward neural network model applied here is composed of four layers and uses the back-propagation training algorithm. The output signals from neural network are processed by the wavelet transform. Finally the control orders generated from the output signals are passed to the motor controller and drive the exoskeleton to move. Through experiments, the equality of neural network prediction of ankle movement is evaluated by giving the correlation coefficient. It is shown from the experimental results that the proposed method can accurately control the movement of ankle joint.展开更多
This paper describes a man-machine interface system using EOG and EMG. A manipulator control system using EOG and EMG is developed according to EOG and EMG. With the eye movement, the system enabled us to control a ma...This paper describes a man-machine interface system using EOG and EMG. A manipulator control system using EOG and EMG is developed according to EOG and EMG. With the eye movement, the system enabled us to control a manipulator. EOG is using for moving the robot joint angles and EMG is using for object grasping. The EOG and EMG discrimination method is used to control the robot. The robot arm joint movements are determined by the EOG discrimination method where the polarity of eye gaze motion signals in each Ch1 and Ch2. The EMG discrimination method is used to control arm gripper to grasp and release the target object. In the robot control experiment, we are successfully control the uArmTM robot by using both EOG and EMG discrimination method as the control input. This control system brings the feasibility of man-machine interface for elderly person and handicapped person.展开更多
Human arm movements may be adversely affected in the event of stroke or spinal cord injuries, eventually causing the patient to lose control of arm movements. Electromyography (EMG) is con-sidered the most effective t...Human arm movements may be adversely affected in the event of stroke or spinal cord injuries, eventually causing the patient to lose control of arm movements. Electromyography (EMG) is con-sidered the most effective technique for the restoration of arm movement in such cases. The reha-bilitation period for such patients is usually long. Moreover, complex treatment techniques may demoralize them. Therefore, this study, attempts to contribute to the development of a relaxing rehabilitation environment through electromyography control of a computer model of the arm. The model is created using MATLAB? and Data LINK software and other requisite components for training the targeted participants to control their arm movements. Six male participants with no history of injury to the arms or back were selected using the set protocol. The results and data collected are analysed using three performance measures i.e. the number of target hits, average time to target, and path efficiency for each target. Then, the main results in terms of the obtained performance measures are discussed and compared with those of previous studies.展开更多
The objective of this paper is to present the advantages of Model reference adaptive control (MRAC) motion cueing algorithm against the classical motion cueing algorithm in terms of biomechanical reactions of the part...The objective of this paper is to present the advantages of Model reference adaptive control (MRAC) motion cueing algorithm against the classical motion cueing algorithm in terms of biomechanical reactions of the participants during the critical maneuvers like chicane in driving simulator real-time. This study proposes a method and an experimental validation to analyze the vestibular and neuromuscular dynamics responses of the drivers with respect to the type of the control used at the hexapod driving simulator. For each situation, the EMG (electromyography) data were registered from arm muscles of the drivers (flexor carpi radialis, brachioradialis). In addition, the roll velocity perception thresholds (RVT) and roll velocities (RV) were computed from the real-time vestibular level measurements from the drivers via a motion-tracking sensor. In order to process the data of the EMG and RVT, Pearson’s correlation and a two-way ANOVA with a significance level of 0.05 were assigned. Moreover, the relationships of arm muscle power and roll velocity with vehicle CG (center of gravity) lateral displacement were analyzed in order to assess the agility/alertness level of the drivers as well as the vehicle loss of control characteristics with a confidence interval of 95%. The results showed that the MRAC algorithm avoided the loss of adhesion, loss of control (LOA, LOC) more reasonably compared to the classical motion cueing algorithm. According to our findings, the LOA avoidance decreased the neuromuscular-visual cues level conflict with MRAC algorithm. It also revealed that the neuromuscular-vehicle dynamics conflict has influence on visuo-vestibular conflict;however, the visuo-vestibular cue conflict does not influence the neuromuscular-vehicle dynamics interactions.展开更多
基金supported by the National Natural Science Foundation (Grant No. 50435040 and 60675045)the National High Technology Research and Development Program (Grant No. 2006AA04Z228)the "111 Project" of China (No. B07018).
文摘When developing a humanoid myo-control hand,not only the mechanical structure should be considered to afford a high dexterity,but also the myoelectric (electromyography,EMG) control capability should be taken into account to fully accomplish the actuation tasks.This paper presents a novel humanoid robotic myocontrol hand (AR hand Ⅲ) which adopted an underac- tuated mechanism and a forearm myocontrol EMG method.The AR hand Ⅲ has five fingers and 15 joints,and actuated by three embedded motors.Underactuation can be found within each finger and between the rest three fingers (the middle finger,the ring finger and the little finger) when the hand is grasping objects.For the EMG control,two specific methods are proposed:the three-fingered hand gesture configuration of the AR hand Ⅲ and a pattern classification method of EMG signals based on a statistical learning algorithm-Support Vector Machine (SVM).Eighteen active hand gestures of a testee are recognized ef- fectively,which can be directly mapped into the motions of AR hand Ⅲ.An on-line EMG control scheme is established based on two different decision functions:one is for the discrimination between the idle and active modes,the other is for the recog- nition of the active modes.As a result,the AR hand Ⅲ can swiftly follow the gesture instructions of the testee with a time delay less than 100 ms.
文摘According to the sampling statistics there are over 7 millions limb disabled persons in China from several times of those in the world. For the benefit to the amputees, thousands varies kinds of commencial Products of artificial hands, by scientific promotion have ben developed in the recent half century. Among those products the EMG controlling artificial upper limb brings the hope to the amputees. The later are now used widely form the earlest one in 1948.In 1978, Shanghai Jiao Tong University began to study and develop the EMG controlling artificial band mounted to amputes over thousand disabled during a couple of recent decades.The risidual muscles of an amputee are the signal source of the artificial hand controlled by EMG. The evoked EMG which can transmit the brain moving information is one of the bioelectricities from human body. The EMG signal, accumulated at skin surface with surface electrodes, passing through filtering and amplifying circuits controls the movement-this is the main principle of EMG controlling artificial hand.But, owing to the weakness of EMG (μv) and a group of muscles information plus the strong turbulence of electric field (v), the integrated EMG can’t completely reflect the brain moving act and will influence the accuracy of EMG-hand, especially those of the multi-multi-degree of freedom.In 1978, only 57% of the controlling aaccuracy of artificial hand with 3-degree of freedom could be reached by the Herbert’s research. In 80’s, the controlling accuracy raised up to 72% by Denning’s new method. Up to now, the accuracy is still not ideal, eventhrogh the Hi-tech of using pattern-recognitionand artincial neuro-net work. The electronic artificial hand will be considered successful and practical only with the moving accuracy more than 95%.Some research by using implant electrode for detecting the neuro-information or EEG controllingmethod met also dimculties for raising the accuracy of artificial hand.For breaking througll the threshold of accuracy limit, the EMG method as mentioned above must bechanged entirely, A newest creative research work on the electronic artificial hand controlled by a "regenerated finger" made by transplanting a toe to the stump is developing in Shanghai Jiao TongUniversity, which is without precedent in the world.The first experimental amputee using "regenerated finger’ to control an electronic artificial forearm with 3-degree of freedom reaches 100% accuracy of movements (i.e. no error within 100 tests). It has been proved that the use of a "regenerated finger" as a controlling signal command makes it possible to use the electronic artificeal hand with multi-degree of freedom without error Thecombining medical science with engineering in the area of designing an electronic artifical upper limb.Acknowledgement: The authors extend their hearty thanks to the famous academician Dr.Chen Zhongwei (Zhong-Sen Hospital) for their creative effort and successful micro-surgical operation for transplanting toe to stump of an amputee, also to the China Science Foundation for supporting. us the fund to develop this research.
基金supported by the National High-Tech R&D Program (Grant No.2006AA04Z224)the Innovation Program of Shanghai Municipal Education Commission (Grant No.08ZZ48)the Shanghai Leading Academic Discipline Project (Grant No.Y0102)
文摘This paper is concerned with a control method for an exoskeleton ankle with electromyography （EMG） signals. The EMG signals of human ankle and the exoskeleton ankle are introduced. Then a control method is proposed to control the exoskeleton ankle using the EMG signals. The feed-forward neural network model applied here is composed of four layers and uses the back-propagation training algorithm. The output signals from neural network are processed by the wavelet transform. Finally the control orders generated from the output signals are passed to the motor controller and drive the exoskeleton to move. Through experiments, the equality of neural network prediction of ankle movement is evaluated by giving the correlation coefficient. It is shown from the experimental results that the proposed method can accurately control the movement of ankle joint.
文摘This paper describes a man-machine interface system using EOG and EMG. A manipulator control system using EOG and EMG is developed according to EOG and EMG. With the eye movement, the system enabled us to control a manipulator. EOG is using for moving the robot joint angles and EMG is using for object grasping. The EOG and EMG discrimination method is used to control the robot. The robot arm joint movements are determined by the EOG discrimination method where the polarity of eye gaze motion signals in each Ch1 and Ch2. The EMG discrimination method is used to control arm gripper to grasp and release the target object. In the robot control experiment, we are successfully control the uArmTM robot by using both EOG and EMG discrimination method as the control input. This control system brings the feasibility of man-machine interface for elderly person and handicapped person.
文摘Human arm movements may be adversely affected in the event of stroke or spinal cord injuries, eventually causing the patient to lose control of arm movements. Electromyography (EMG) is con-sidered the most effective technique for the restoration of arm movement in such cases. The reha-bilitation period for such patients is usually long. Moreover, complex treatment techniques may demoralize them. Therefore, this study, attempts to contribute to the development of a relaxing rehabilitation environment through electromyography control of a computer model of the arm. The model is created using MATLAB? and Data LINK software and other requisite components for training the targeted participants to control their arm movements. Six male participants with no history of injury to the arms or back were selected using the set protocol. The results and data collected are analysed using three performance measures i.e. the number of target hits, average time to target, and path efficiency for each target. Then, the main results in terms of the obtained performance measures are discussed and compared with those of previous studies.
文摘The objective of this paper is to present the advantages of Model reference adaptive control (MRAC) motion cueing algorithm against the classical motion cueing algorithm in terms of biomechanical reactions of the participants during the critical maneuvers like chicane in driving simulator real-time. This study proposes a method and an experimental validation to analyze the vestibular and neuromuscular dynamics responses of the drivers with respect to the type of the control used at the hexapod driving simulator. For each situation, the EMG (electromyography) data were registered from arm muscles of the drivers (flexor carpi radialis, brachioradialis). In addition, the roll velocity perception thresholds (RVT) and roll velocities (RV) were computed from the real-time vestibular level measurements from the drivers via a motion-tracking sensor. In order to process the data of the EMG and RVT, Pearson’s correlation and a two-way ANOVA with a significance level of 0.05 were assigned. Moreover, the relationships of arm muscle power and roll velocity with vehicle CG (center of gravity) lateral displacement were analyzed in order to assess the agility/alertness level of the drivers as well as the vehicle loss of control characteristics with a confidence interval of 95%. The results showed that the MRAC algorithm avoided the loss of adhesion, loss of control (LOA, LOC) more reasonably compared to the classical motion cueing algorithm. According to our findings, the LOA avoidance decreased the neuromuscular-visual cues level conflict with MRAC algorithm. It also revealed that the neuromuscular-vehicle dynamics conflict has influence on visuo-vestibular conflict;however, the visuo-vestibular cue conflict does not influence the neuromuscular-vehicle dynamics interactions.
