Humanlike, task-specific reaching and grasping with redundant arms and low-complexity hands

Liarokapis, Minas; Dollar, Aaron M.; Kyriakopoulos, Kostas J.

doi:10.1109/icar.2015.7251501

Cited by 13 publications

(6 citation statements)

References 37 publications

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“…To achieve these goals, research has focused on mapping human motion to anthropomorphic robot motion through non-linear, constrained optimization methods [86–89]. The anthropomorphic robot trajectories are then projected into low-dimensional manifolds of upper limb “principal components”, where appropriate Navigation Function models can be trained [90].…”

Section: From Biology To Roboticsmentioning

confidence: 99%

Hand synergies: Integration of robotics and neuroscience for understanding the control of biological and artificial hands

Santello

Bianchi

Gabiccini

et al. 2016

Physics of Life Reviews

218

160

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The term ‘synergy’ – from the Greek synergia – means ‘working together’. The concept of multiple elements working together towards a common goal has been extensively used in neuroscience to develop theoretical frameworks, experimental approaches, and analytical techniques to understand neural control of movement, and for applications for neuro-rehabilitation. In the past decade, roboticists have successfully applied the framework of synergies to create novel design and control concepts for artificial hands, i.e., robotic hands and prostheses. At the same time, robotic research on the sensorimotor integration underlying the control and sensing of artificial hands has inspired new research approaches in neuroscience, and has provided useful instruments for novel experiments. The ambitious goal of integrating expertise and research approaches in robotics and neuroscience to study the properties and applications of the concept of synergies is generating a number of multidisciplinary cooperative projects, among which the recently finished 4-year European project “The Hand Embodied” (THE). This paper reviews the main insights provided by this framework. Specifically, we provide an overview of neuroscientific bases of hand synergies and introduce how robotics has leveraged the insights from neuroscience for innovative design in hardware and controllers for biomedical engineering applications, including myoelectric hand prostheses, devices for haptics research, and wearable sensing of human hand kinematics. The review also emphasizes how this multidisciplinary collaboration has generated new ways to conceptualize a synergy-based approach for robotics, and provides guidelines and principles for analyzing human behavior and synthesizing artificial robotic systems based on a theory of synergies.

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Section: From Biology To Roboticsmentioning

confidence: 99%

Hand synergies: Integration of robotics and neuroscience for understanding the control of biological and artificial hands

Santello

Bianchi

Gabiccini

et al. 2016

Physics of Life Reviews

218

160

View full text Add to dashboard Cite

show abstract

“…Robots designed to interact and cooperate with humans are deemed more esthetically pleasing when exhibiting anthropomorphic motion and are believed to be safer since human-like motions are more readily predictable than robotic ones (Duffy, 2003;Hegel et al, 2008). Control schemes aimed at anthropomorphizing robotic motions through the use of synergies derived from reach-and-grasp tasks have been carried out by Liarokapis et al (2015) and reviewed by Santello et al (2016). Whereas this work leveraged postural synergies, our results indicate that time-varying synergies could be used to produce more accurate replications of human motion.…”

Section: Discussionmentioning

confidence: 99%

Applications of Synergies in Human Machine Interfaces

Vinjamuri¹,

Mao²,

Maybhate³

2019

Frontiers Research Topics

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Application of neural machine interface in individuals with chronic hemiparetic stroke is regarded as a great challenge, especially for classification of the hand opening and grasping during a functional upper extremity movement such as reach-to-grasp. The overall accuracy of classifying hand movements, while actively lifting the paretic arm, is subject to a significant reduction compared to the accuracy when the arm is fully supported. Such a reduction is believed to be due to the expression of flexion synergy, which couples shoulder abduction (SABD) with elbow/wrist and finger flexion, and is common in up to 60% of the stroke population. Little research has been done to develop methods to reduce the impact of flexion synergy on the classification of hand opening and grasping. In this study, we proposed a novel approach to classify hand opening and grasping in the context of the flexion synergy using a wavelet coherence-based filter. We first identified the frequency ranges where the coherence between the SABD muscle and wrist/finger flexion muscles is significant in each participant, and then removed the synergy-induced electromyogram (EMG) component with a subject-specific and muscle-specific coherence-based filter. The new approach was tested in 21 stroke individuals with moderate to severe motor impairments. Employing the filter, 14 participants gained improvement in classification accuracy with a range of 0.1 to 14%, while four showed 0.3 to 1.2% reduction. The remaining three participants were excluded from comparison due to the lack of significant coherence, thus no filters were applied. The improvement in classification accuracy is significant (p = 0.017) when the SABD loading equals 50% of the maximal torque. Our findings suggest that the coherence-based filters can reduce the impact of flexion synergy by removing the synergy-induced EMG component and have the potential to improve the overall classification accuracy of hand movements in individuals with poststroke flexion synergy.

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“…Robots designed to interact and cooperate with humans are deemed more esthetically pleasing when exhibiting anthropomorphic motion and are believed to be safer since human-like motions are more readily predictable than robotic ones (Duffy, 2003 ; Hegel et al, 2008 ). Control schemes aimed at anthropomorphizing robotic motions through the use of synergies derived from reach-and-grasp tasks have been carried out by Liarokapis et al ( 2015 ) and reviewed by Santello et al ( 2016 ). Whereas this work leveraged postural synergies, our results indicate that time-varying synergies could be used to produce more accurate replications of human motion.…”

Section: Discussionmentioning

confidence: 99%

Low-Dimensional Synergistic Representation of Bilateral Reaching Movements

Burns

Patel

Florescu

et al. 2017

Front. Bioeng. Biotechnol.

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Kinematic and neuromuscular synergies have been found in numerous aspects of human motion. This study aims to determine how effectively kinematic synergies in bilateral upper arm movements can be used to replicate complex activities of daily living (ADL) tasks using a sparse optimization algorithm. Ten right-handed subjects executed 18 rapid and 11 natural-paced ADL tasks requiring bimanual coordination while sitting at a table. A position tracking system was used to track the subjects’ arms in space, and angular velocities over time for shoulder abduction, shoulder flexion, shoulder internal rotation, and elbow flexion for each arm were computed. Principal component analysis (PCA) was used to generate kinematic synergies from the rapid-paced task set for each subject. The first three synergies accounted for 80.3 ± 3.8% of variance, while the first eight accounted for 94.8 ± 0.85%. The first and second synergies appeared to encode symmetric reaching motions which were highly correlated across subjects. The first three synergies were correlated between left and right arms within subjects, whereas synergies four through eight were not, indicating asymmetries between left and right arms in only the higher order synergies. The synergies were then used to reconstruct each natural-paced task using the l1-norm minimization algorithm. Temporal dilations of the synergies were introduced in order to model the temporal scaling of movement patterns achieved by the cerebellum and basal ganglia as reported previously in the literature. Reconstruction error was reduced by introducing synergy dilations, and cumulative recruitment of several synergies was significantly reduced in the first 10% of training task time by introducing temporal dilations. The outcomes of this work could open new scenarios for the applications of postural synergies to the control of robotic systems, with potential applications in rehabilitation. These synergies not only help in providing near-natural control but also provide simplified strategies for design and control of artificial limbs. Potential applications of these bilateral synergies were discussed and future directions were proposed.

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Humanlike, task-specific reaching and grasping with redundant arms and low-complexity hands

Cited by 13 publications

References 37 publications

Hand synergies: Integration of robotics and neuroscience for understanding the control of biological and artificial hands

Hand synergies: Integration of robotics and neuroscience for understanding the control of biological and artificial hands

Applications of Synergies in Human Machine Interfaces

Low-Dimensional Synergistic Representation of Bilateral Reaching Movements

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