Adapting motion capture data using weighted real-time inverse kinematics

Meredith, Michael P.; Maddock, Steve

doi:10.1145/1057270.1057281

Cited by 36 publications

(17 citation statements)

References 11 publications

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“…In substantially extending the protocol from Ref. [7], this study employed such techniques to robustly determine digit kinematics from the designated minimal marker set in an analytical manner without consideration of individualized motion [34]. The next natural extension of this protocol would be to incorporate these procedures for tabulating digit kinematics with protocols that include applied force measurements such that inverse analyses to determine joint kinetics, such as those for gait, could be employed [35].…”

Section: Discussionmentioning

confidence: 99%

Robust Identification of Three-Dimensional Thumb and Index Finger Kinematics With a Minimal Set of Markers

Nataraj

2013

Journal of Biomechanical Engineering

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This study presents a methodology to determine thumb and index finger kinematics while utilizing a minimal set of markers. The motion capture of skin-surface markers presents inherent challenges for the accurate and comprehensive measurement of digit kinematics. As such, it is desirable to utilize robust methods for assessing digit kinematics with fewer markers. The approach presented in this study involved coordinate system alignment, locating joint centers of rotation, and a solution model to estimate three-dimensional (3-D) digit kinematics. The solution model for each digit was based on assumptions of rigid-body interactions, specific degrees of freedom (DOFs) at each located joint, and the aligned coordinate system definitions. Techniques of inverse kinematics and optimization were applied to calculate the 3-D position and orientation of digit segments during pinching between the thumb and index finger. The 3-D joint center locations were reliably fitted with mean coefficients of variation below 5%. A parameterized form of the solution model yielded feasible solutions that met specified tolerance and convergence criteria for over 85% of the test points. The solution results were intuitive to the pinching function. The thumb was measured to be rotated about the CMC joint to bring it into opposition to the index finger and larger rotational excursions (>10 deg) were observed in flexion/extension compared to abduction/adduction and axial rotation for all joints. While the solution model produced results similar to those computed from a full marker set, the model facilitated the usage of fewer markers, which inherently lessened the effects of passive motion error and reduced the post-experimental effort required for marker processing.

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Section: Discussionmentioning

confidence: 99%

Robust Identification of Three-Dimensional Thumb and Index Finger Kinematics With a Minimal Set of Markers

Nataraj

2013

Journal of Biomechanical Engineering

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“…A commercial haptic device, Phantom Omni, has been used for guiding a GRIPS slave robot of Kraft Telerobotics Inc. The slave robot has a complex configuration and numerical methods has been adopted to solve the inverse kinematics [7,8]. The master movements are scaled 1:3 in relation to the slave when the rate-position controller is activated.…”

Section: State: 'Rate Collision'mentioning

confidence: 99%

A remote handling rate-position controller for telemanipulating in a large workspace

Barrio

Ferré

Suárez-Ruiz

et al. 2014

Fusion Engineering and Design

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This paper presents a new Haptic Rate-Position controller, which allows manipulating a slave robot in a large workspace using a small haptic device. This control algorithm is very effective when the master device is much smaller than the slave device. Haptic information is displayed to the user so as to be informed when a change in the operation mode occurs. This controller allows performing tasks in a large remote workspace by using a haptic device with a reduced workspace such as Phantom. Experimental results have been carried out using a slave robot from Kraft Telerobotics and a commercial haptic interface as a master device. A curvature path following task has been simulated using the proposed controller which was compared with the force-position control algorithm. Results obtained show that higher accuracy is obtained when the proposed method is used, spending a similar amount of time to perform the task.

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“…Thus z is projected to the nullspace of J. For θ that is already out of range, we enforce a hard constraints as introduced in [Meredith and Maddock 2005] through ∆θ = w∆θ, 0 ≤ w < 1 to effectively suppress further deviation from its active range. Final θ solutions are recorded as the observed rotations o in tuple (s, o).…”

Section: Ik Solver Clamped Dls With Constraintsmentioning

confidence: 99%

Data-driven glove calibration for hand motion capture

Wang

Neff

2013

Proceedings of the 12th ACM SIGGRAPH/Eurographics Symposium on Computer Animation

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Hand motion is an important component of human motion, playing a central role in communication. However, it is difficult to capture hand motion optically, especially in conjunction with full body motion. Due to a lack of appropriate calibration methods, data gloves also do not provide sufficiently accurate hand motion. In this paper, we present a novel glove calibration approach that can map raw sensor readings to hand motion data with both accurate joint rotations and fingertip positions. Our method elegantly handles the sensor coupling problem by treating calibration as a flexible mapping from sensor readings to joint rotations. A sampling process collects data tuples according to accuracy requirements, and organizes all the tuples in a training set. From these data, a specially designed Gaussian Process Regression model is trained to infer the calibration function, and the learned model can be used to calibrate new sensor readings. For real-time hand motion capture, a sparse approximation of the model is used to enhance performance. Evaluation experiments demonstrate that our approach provides significantly better results that have more accurate hand shapes and fingertip positions, compared to other calibration methods.

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Adapting motion capture data using weighted real-time inverse kinematics

Cited by 36 publications

References 11 publications

Robust Identification of Three-Dimensional Thumb and Index Finger Kinematics With a Minimal Set of Markers

Robust Identification of Three-Dimensional Thumb and Index Finger Kinematics With a Minimal Set of Markers

A remote handling rate-position controller for telemanipulating in a large workspace

Data-driven glove calibration for hand motion capture

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