Human Hand: Kinematics, Statics, and Dynamics

Chen, Fai Chen; Favetto, Alain; Mousavi, Mohamad Mehdi Seyed; Ambrosio, Elisa Paola; Appendino, Silvia; Battezzato, Alessandro; Manfredi, Diego; Pescarmona, Francesco; Bona, Basilio

doi:10.2514/6.2011-5249

Cited by 19 publications

(9 citation statements)

References 12 publications

(9 reference statements)

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“…The desired angular velocity at the termination of the movement of the finger is 0 rad/s. As can be seen from the figures, the angular velocity has an initial value of 6.4 rad/s that is physiologically acceptable for demonstrating the natural speed of the PIP joint of the index finger as given in Darling and Cole (1990) and Chen Chen et al (2011). Comparison of results shows that for the LQG control scheme, the angular velocity reaches 0 rad/s in 25 seconds of simulation time for a complete movement of the finger from extension to flexion and back to the extended position.…”

Section: Simulation Resultsmentioning

confidence: 84%

“…The desired angular velocity at the termination of the movement of the finger is 0 rad/s. As can be observed from the figure, the angular velocity has a physiologically acceptable initial value of 6.4 rad/s for demonstrating the natural speed of the PIP joint of the index finger as described by Darling and Cole (1990) and Chen Chen et al (2011). An extension to flexion and then back to extension movement of the finger is completed in 1.6 seconds, with a minor undershoot.…”

Section: Experimental Validation Of the Simulation Resultsmentioning

confidence: 86%

“…In Figure 13, the extension position of the joint is given by 0 rad; the finger then attains a flexion angle of 0.135 rad, and settles again to an extension angle of 0 rad. The range of extension/flexion angle is 0–1.92 rad for the PIP joint of a human index finger as given in Cobos et al (2008) and Chen Chen et al (2011). The maximum feedback force due to the LQI control scheme is nearly 178 N as in Figure 14 which does not exceed the maximum flexion force of 223 N of a human index finger (Cobos et al, 2008).…”

Section: Simulation Resultsmentioning

confidence: 96%

“…In Figure 13, the joint extends at 0 seconds, then flexes by 0.135 rad, and extends to 0 rad again. According to Cobos et al (2008) and Chen Chen et al (2011), the range of extension/flexion angle for a human index finger’s PIP joint is 0–1.92 rad. The peak feedback force from the LQI control system is nearly 178 N as in Figure 14, which does not exceed the maximum flexion force of 223 N of a human index finger (Cobos et al, 2008).…”

Section: Experimental Validation Of the Simulation Resultsmentioning

confidence: 99%

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Bond graph modeling with linear quadratic integral control synthesis of a robotic digit in a human impaired hand for anthropomorphic coordination

Iqbal

Imtiaz

Mahmood

2022

Transactions of the Institute of Measurement and Control

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The movement coordination of the robotic digit(s) with the central nervous system (CNS) and the natural digit(s) is a complex task that needs to be executed successfully in an anthropomorphic hand. The task is challenging to resolve because of the CNS. We developed a theoretical framework for the biomechanical model of a partially impaired human hand utilizing the bond graph modeling technique by incorporating inertia, muscle, and visco-elastic dynamics. The research presents a partially impaired human hand model with a robotic digit and four natural digits having 21 degrees of freedom. We formulated a linear quadratic Gaussian (LQG) integral control technique for the 21st-order model to regulate the flexion and extension movement of the robotic digit while considering the disturbances. We have simulated the modeling scheme in MATLAB/Simulink. The flexion and extension movement and the angular velocity of the robotic finger are shown to be following all the physiological constraints of a natural finger. The settling time is achieved at 1.6 seconds, with a maximum flexion angle of 0.135 rad. The sensitivity analysis shows that the model is robust against disturbances. The simulation results exhibit the application of this scheme toward upper limb rehabilitation and improvement in prosthetic and exoskeleton designs.

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Section: Simulation Resultsmentioning

confidence: 84%

Section: Experimental Validation Of the Simulation Resultsmentioning

confidence: 86%

Section: Simulation Resultsmentioning

confidence: 96%

Section: Experimental Validation Of the Simulation Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Bond graph modeling with linear quadratic integral control synthesis of a robotic digit in a human impaired hand for anthropomorphic coordination

Iqbal

Imtiaz

Mahmood

2022

Transactions of the Institute of Measurement and Control

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“…We assume all the keypoints are parallel to the AruCo marker board, which is defined as the XY plane in the AruCo markers coordinate. To compensate for the thickness of the hand, we set the Z value of each keypoint to be 1.15 cm as the mean of human hand thickness is 2.3 cm (ChenChen et al , 2011). Then, the 3D positions of hand keypoints Pihand3d=true[Xi,Yi,Z=1.15true],i=1…66 in AruCo markers coordinate can be calculated as below: where pihand2d=true[ui,vitrue], i=1,…,66 denotes the 2D positions of hand keypoints in image coordinates; K denotes camera intrinsic parameters; and s is a scale factor.…”

Section: Hand Pose Trackingmentioning

confidence: 99%

Robot programming by demonstration with a monocular RGB camera

Wang¹,

Tang²

2022

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Purpose This paper aims to present a new approach for robot programming by demonstration, which generates robot programs by tracking 6 dimensional (6D) pose of the demonstrator’s hand using a single red green blue (RGB) camera without requiring any additional sensors. Design/methodology/approach The proposed method learns robot grasps and trajectories directly from a single human demonstration by tracking the movements of both human hands and objects. To recover the 6D pose of an object from a single RGB image, a deep learning–based method is used to detect the keypoints of the object first and then solve a perspective-n-point problem. This method is first extended to estimate the 6D pose of the nonrigid hand by separating fingers into multiple rigid bones linked with hand joints. The accurate robot grasp can be generated according to the relative positions between hands and objects in the 2 dimensional space. Robot end-effector trajectories are generated from hand movements and then refined by objects’ start and end positions. Findings Experiments are conducted on a FANUC LR Mate 200iD robot to verify the proposed approach. The results show the feasibility of generating robot programs by observing human demonstration once using a single RGB camera. Originality/value The proposed approach provides an efficient and low-cost robot programming method with a single RGB camera. A new 6D hand pose estimation approach, which is used to generate robot grasps and trajectories, is developed.

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