Learning task-specific models for reach to grasp movements: Towards EMG-based teleoperation of robotic arm-hand systems

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

doi:10.1109/biorob.2012.6290724

Cited by 21 publications

(13 citation statements)

References 21 publications

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“…Dynamic 3D space experiments involve the transition between shoulder, elbow, and wrist joint angles while eliciting a target motion class [22,[143][144][145][146][147][148]. Generally, these protocols can be grouped into two subsets: reach-to-grasp, and activities of daily living (ADLs).…”

Section: Dynamic 3d Spacementioning

confidence: 99%

“…Within the reach-to-grasp protocol, subjects sequentially transition between a series of phases: a rest phase, arm-extension to object, contact with object, grasp of object, unhand object, return to initial position, rest. Variability can be introduced by requiring different object-related grips (for instance, palmar, pinch, or power grip) or by modifying the elevation or lateral position of the object to introduce shoulder flexion and abduction variability [143][144][145][146]. Within the activities of daily living protocol, progressions between limb positions are designed to model functional tasks: for instance, moving a glass from a tabletop to drinking position or moving from a relaxed position to reaching in a cupboard [22].…”

Section: Dynamic 3d Spacementioning

confidence: 99%

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Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity

Campbell

Phinyomark

Scheme

2020

Sensors

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This manuscript presents a hybrid study of a comprehensive review and a systematic (research) analysis. Myoelectric control is the cornerstone of many assistive technologies used in clinical practice, such as prosthetics and orthoses, and human-computer interaction, such as virtual reality control. Although the classification accuracy of such devices exceeds 90% in a controlled laboratory setting, myoelectric devices still face challenges in robustness to variability of daily living conditions. The intrinsic physiological mechanisms limiting practical implementations of myoelectric devices were explored: the limb position effect and the contraction intensity effect. The degradation of electromyography (EMG) pattern recognition in the presence of these factors was demonstrated on six datasets, where classification performance was 13% and 20% lower than the controlled setting for the limb position and contraction intensity effect, respectively. The experimental designs of limb position and contraction intensity literature were surveyed. Current state-of-the-art training strategies and robust algorithms for both effects were compiled and presented. Recommendations for future limb position effect studies include: the collection protocol providing exemplars of at least 6 positions (four limb positions and three forearm orientations), three-dimensional space experimental designs, transfer learning approaches, and multi-modal sensor configurations. Recommendations for future contraction intensity effect studies include: the collection of dynamic contractions, nonlinear complexity features, and proportional control.

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Section: Dynamic 3d Spacementioning

confidence: 99%

Section: Dynamic 3d Spacementioning

confidence: 99%

Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity

Campbell

Phinyomark

Scheme

2020

Sensors

View full text Add to dashboard Cite

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“…end-effector) positions. For doing so, we train task-specific random forests models as described in [31] and [32] that are able given robot end-effector positions to estimate with high accuracy the "ficticious" human elbow positions. In order to train the aforementioned models we use the dataset of the experiments conducted in [31].…”

Section: B Learning Human Elbow Position For Autonomous Operationmentioning

confidence: 99%

Functional Anthropomorphism for human to robot motion mapping

Liarokapis

Artemiadis

Kyriakopoulos

2012

2012 IEEE RO-MAN: The 21st IEEE International Symposium on Robot and Human Interactive Communication

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In this paper we propose a generic methodology for human to robot motion mapping for the case of a robotic arm hand system, allowing anthropomorphism. For doing so we discriminate between Functional Anthropomorphism and Perceptional Anthropomorphism, focusing on the first to achieve anthropomorphic solutions of the inverse kinematics for a redundant robot arm. Regarding hand motion mapping, a "wrist" (end-effector) offset to compensate for differences between human and robot hand dimensions is applied and the fingertips mapping methodology is used. Two different mapping scenarios are also examined: mapping for teleoperation and mapping for autonomous operation. The proposed methodology can be applied to a variety of human robot interaction applications, that require a special focus on anthropomorphism.

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“…Apart from postural synergies recent studies have proven the existence of a similar synergistic actuation of the human muscles responsible for controlling the movement of the hand, [13] , [14], [15]. In most of these studies EMG signals are used in order to train models that reconstruct human hand motion based on muscular activation and focus on teleoperation scenarios.…”

Section: Introductionmentioning

confidence: 99%

Relating postural synergies to low-D muscular activations: Towards bio-inspired control of robotic hands

Katsiaris

Artemiadis

Kyriakopoulos

2012

2012 IEEE 12th International Conference on Bioinformatics &Amp; Bioengineering (BIBE)

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Studying human motor control has received increased attention during the past decades. Both the design and control of robotic artifacts may benefit from observation of human behavior. In this paper a novel method for capturing the dynamic behavior of the human hand is presented. The low dimensional kinematics of the human hand, including the wrist, and the low dimensional representation of the muscular activations were correlated through a linear time invariant (LTI) state space model. A linear output regulation controller was used in order to drive a simulated hand and the resulting trajectories were compared with the experimentally captured trajectories.

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Learning task-specific models for reach to grasp movements: Towards EMG-based teleoperation of robotic arm-hand systems

Cited by 21 publications

References 21 publications

Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity

Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity

Functional Anthropomorphism for human to robot motion mapping

Relating postural synergies to low-D muscular activations: Towards bio-inspired control of robotic hands

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