Modeling anthropomorphism in dynamic human arm movements

Katsiaris, Pantelis T.; Artemiadis, Panagiotis; Kyriakopoulos, Kostas J.

doi:10.1109/iros.2010.5651834

Cited by 10 publications

(8 citation statements)

References 23 publications

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“…Some datasets offer limitations regarding the amount of data presented, are limited to grasp type classification 6,7 or consider hand kinematics from just three markers on the hand 8 . Furthermore, datasets with several DoF present other limitations: Tasks : Only reaching and grasping movements 9–14 , static grasp postures 9,10,12,14–17 or exploratory/haptic tasks 18 were recorded during product manipulation. These tasks lack representativeness of ADL because of the limited range of activities considered but also because subjects performed the tasks following precise instructions. Objects used : Some of the datasets recorded tasks simulating the use of objects, but not using any object 11,15–17,19,20 . Type of data presented : Some datasets only provide raw data from the motion capture system (cameras or gloves) 12,19,20 instead of offering anatomical angles. Number of subjects : Some of those datasets provide data from only one subject 9,11,15–17,19,20 . Number of hands studied : All the datasets cited only studied subjects’ dominant hand.…”

Section: Background and Summarymentioning

confidence: 99%

Human hand kinematic data during feeding and cooking tasks

et al. 2019

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This work presents a database of human hand kinematics containing data collected during the performance of a wide variety of activities of daily living involving feeding and cooking. The data were recorded using CyberGlove instrumented gloves on both hands measuring 18 degrees of freedom on each. A total of 20 subjects participated in each part of the experiment, and the objects and their arrangement were the same across subjects, although they performed the tasks in a natural non-directed way. This dataset contains a total of 1160 continuous calibrated recordings taken at 100 Hz during the performance of the tasks, with filtered signal. Statistical descriptive analyses from these data are presented. This database can be useful for machine learning purposes and prostheses control, as well as for the characterization of healthy human hand kinematics.

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Section: Background and Summarymentioning

confidence: 99%

Human hand kinematic data during feeding and cooking tasks

et al. 2019

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“…rehabilitation, human motion assistance, etc.) [34,35]. The robot structure which is shown in figure 3 is obtained by using the Mitsubishi PA-10 anthropomorphic robot arm.…”

Section: Simulation Resultsmentioning

confidence: 99%

Performance Analysis of Numerical Integration Methods in the Trajectory Tracking Application of Redundant Robot Manipulators

Sarıyıldız¹,

Temeltaş²

2011

International Journal of Advanced Robotic Systems

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Differential kinematic is one of the most important solution methods in robot kinematics. The main advantage of the differential kinematic method is that it can be easily implemented any kind of mechanisms. Also, an accurate and efficient kinematic based trajectory tracking application can be easily implemented by using this method. In differential kinematic method, we use Jacobian as a mapping operator in the velocity space. Inversion of Jacobian matrix transforms the desired trajectory velocities, which are the linear and angular velocities of the end effector, into the joint velocities. The joint velocities are required to be integrated to obtain the pose of the robot manipulator. This integration can be evaluated by using numerical integration methods, since the inverse kinematic equations are highly complex and nonlinear. Therefore, the performance of the trajectory tracking application of the robot manipulator is directly affected by the chosen numerical integration method. This paper compares the performances of numerical integration methods in the trajectory tracking application of redundant robot manipulators. Several widely used numerical integration methods are implemented into the trajectory tracking application of the 7-DOF redundant robot manipulator named PA-10 and simulation results are given.

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“…In the absence of global solutions within the area of wearable elbow device control, a large number of motion models has been proposed [ 3 , 4 , 5 , 6 , 7 , 8 , 9 ]. These models can be arranged along a spectrum based on the level of decomposition of human motion properties.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Muscle Activation Models for Elbow Motion Estimation

Desplenter

Trejos

2018

Sensors

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Adoption of wearable assistive technologies relies heavily on improvement of existing control system models. Knowing which models to use and how to improve them is difficult to determine due to the number of proposed solutions with relatively little broad comparisons. One type of these models, muscle activation models, describes the nonlinear relationship between neural inputs and mechanical activation of the muscle. Many muscle activation models can be found in the literature, but no comparison is available to guide the community on limitations and improvements. In this research, an EMG-driven elbow motion model is developed for the purpose of evaluating muscle activation models. Seven muscle activation models are used in an optimization procedure to determine which model has the best performance. Root mean square errors in muscle torque estimation range from 1.67–2.19 Nm on average over varying input trajectories. The computational resource demand was also measured during the optimization procedure, as it is an important aspect for determining if a model is feasible for use in a particular wearable assistive device. This study provides insight into the ability of these models to estimate elbow motion and the trade-off between estimation accuracy and computational demand.

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Modeling anthropomorphism in dynamic human arm movements

Cited by 10 publications

References 23 publications

Human hand kinematic data during feeding and cooking tasks

Human hand kinematic data during feeding and cooking tasks

Performance Analysis of Numerical Integration Methods in the Trajectory Tracking Application of Redundant Robot Manipulators

Evaluating Muscle Activation Models for Elbow Motion Estimation

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