Biodynamic modeling, system identification, and variability of multi-finger movements

Lee, Sang Wook; Zhang, Xudong

doi:10.1016/j.jbiomech.2007.04.021

Cited by 15 publications

(11 citation statements)

References 30 publications

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“…A MPC architecture; actuated through human finger joint trajectories that can generate desired motions through online error minimization is proposed. The simulation results for TU Biomimetic Hand finger joint trajectories anticipated by the proposed control architecture are in close conformity with that of the natural finger joint trajectories and in line with the results reported in literature [18]. The developed controller is experimentally tested for the index finger of the TU Biomimetic Hand.…”

Section: Introductionsupporting

confidence: 86%

“…4 more closely than that reported in [18]. Further, MPC best fit to the reference trajectories with a maximum control horizon less that 0.2 sec compared to the error throughout the whole flexion operation in following the reference trajectories as reported in [18].…”

Section: A Simulation Resultssupporting

confidence: 62%

“…Fig. 8 shows the finger joint trajectories (excluding the thumb) for the dynamic model of human hand finger proposed in [18]. The curves in red are the simulated trajectories with respect to the reference trajectories in blue.…”

Section: A Simulation Resultsmentioning

confidence: 99%

“…8 in terms of the typical right skewed bell shaped profiles. These profiles describe the natural characteristics of finger movements [18]. Human fingers carry out manipulation in a specific RoM caused by static constraints [23].…”

Section: B Experimental Resultsmentioning

confidence: 99%

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Model predictive control for finger joint trajectory of TU Biomimetic hand

Kakoty

Hazarika

Koul

et al. 2014

2014 IEEE International Conference on Mechatronics and Automation

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A long standing goal in rehabilitation robotics is to emulate the human-like stable grasping operations by upper limb prosthesis. For stable grasping and manipulation by a prosthetic hand, the finger joints should follow the trajectories of the natural counterparts. Although a number of commercial prosthetic hands and research prototypes have been developed, mimicking the human-like movement is still a challenge. One of the main reasons for this is the lack of fast on-line error minimizing control approach during trajectory tracking.In this paper, we propose a model predictive control (MPC) architecture that can generate desired motions through online error minimization. Focus is on emulating the human-like finger joint trajectories by Tezpur University (TU) Biomimetic Hand. The MPC perform the online error minimization and control in a unified way by tracking reference trajectories. The reference trajectories were generated by recording the finger joint trajectories of human hand. The proposed MPC was implemented on the TU Biomimetic Hand index finger; wherein finger joint trajectories of human finger was used to actuate the control architecture. The simulation results show that the finger joint trajectories of TU Biomimetic Hand are in close conformity to that of the human finger. The experimental results for the TU Biomimetic Hand index finger satisfies the dynamic constraints of human hand and thus ensures stable grasping by the prosthetic hand.

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Section: Introductionsupporting

confidence: 86%

Section: A Simulation Resultssupporting

confidence: 62%

Section: A Simulation Resultsmentioning

confidence: 99%

Section: B Experimental Resultsmentioning

confidence: 99%

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Model predictive control for finger joint trajectory of TU Biomimetic hand

Kakoty

Hazarika

Koul

et al. 2014

2014 IEEE International Conference on Mechatronics and Automation

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“…The joints move satisfying the dynamic constraints of human finger joint angles [21]. The finger joint trajectories of human hand as reported in [22] is shown in Figure 9(c). As can be seen, the joint trajectories of the Prototype 1.0 are in close approximation to human finger joint trajectories.…”

Section: Performance Characteristics and Evaluationmentioning

confidence: 78%

A Two Layered Control Architecture for Prosthetic Grasping

Kakoty

Hazarika

2013

Paladyn, Journal of Behavioral Robotics

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This paper presents a two layered control architecture -Superior hand control (SHC) followed by Local hand control (LHC) for an extreme upper limb prosthesis. The control architecture is for executing grasping operations involved in 70% of daily living activities. Forearm electromyogram actuated SHC is for recognition of user' s intended grasp. LHC control the fingers to be actuated for the recognized grasp. The finger actuation is controlled through a proportionalintegral-derivative controller customized with fingertip force sensor. LHC controls joint angles and velocities of the fingers in the prosthetic hand. Fingers in the prosthetic hand emulate the dynamic constraints of human hand fingers. The joint angle trajectories and velocity profiles of the prosthetic hand finger are in close approximation to those of the human finger.

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