Analysis of Motor Synergies Utilization for Optimal Movement Generation for a Human-like Robotic Arm

Fu, Kin Chung Denny; Nakamura, Yutaka; Yamamoto, Tomoko; Ishiguro, Hiroshi

doi:10.1007/s11633-013-0749-2

Cited by 11 publications

(3 citation statements)

References 13 publications

(17 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…More recently, other elaborate optimization models were developed aiming at integrating multiple costs at different levels of the motor hierarchy in order to provide a complete solution. For example, many models [25][26][27][28][29][30][31][32] have emphasized the significance of optimal feedback selection for successful generation of multi-joint movements, based on the optimal feedback control framework developed by Todorov & Jordan [33], and some others have exploited machine learning techniques [34,35].…”

Section: Introductionmentioning

confidence: 99%

Human arm redundancy: a new approach for the inverse kinematics problem

Barliya,

Krausz,

Naaman

et al. 2024

R. Soc. Open Sci.

View full text Add to dashboard Cite

The inverse kinematics (IK) problem addresses how both humans and robotic systems coordinate movement to resolve redundancy, as in the case of arm reaching where more degrees of freedom are available at the joint versus hand level. This work focuses on which coordinate frames best represent human movements, enabling the motor system to solve the IK problem in the presence of kinematic redundancies. We used a multi-dimensional sparse source separation method to derive sets of basis (or source) functions for both the task and joint spaces, with joint space represented by either absolute or anatomical joint angles. We assessed the similarities between joint and task sources in each of these joint representations, finding that the time-dependent profiles of the absolute reference frame’s sources show greater similarity to corresponding sources in the task space. This result was found to be statistically significant. Our analysis suggests that the nervous system represents multi-joint arm movements using a limited number of basis functions, allowing for simple transformations between task and joint spaces. Additionally, joint space seems to be represented in an absolute reference frame to simplify the IK transformations, given redundancies. Further studies will assess this finding’s generalizability and implications for neural control of movement.

show abstract

Section: Introductionmentioning

confidence: 99%

Human arm redundancy: a new approach for the inverse kinematics problem

Barliya,

Krausz,

Naaman

et al. 2024

R. Soc. Open Sci.

View full text Add to dashboard Cite

show abstract

“…For example, many models ( Denny Fu et al, 2013; Menner et al, 2021; Isableu et al, 2016; Mombaur et al, 2010; Berret et al, 2011; Oguz et al, 2018; Sharif Razavian et al, 2015; Scott, 2004 ) have emphasized the significance of optimal feedback selection for successful generation of multi-joint movements, based on the optimal feedback control framework developed by Todorov and Jordan (2002 ), and some others have exploited machine learning techniques ( Srisuk et al, 2017; Liu and Liu, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Human Arm Redundancy - A New Approach for the Inverse Kinematics Problem

Barliya

Krausz

Naaman

et al. 2023

Preprint

View full text Add to dashboard Cite

The inverse kinematics problem deals with the question of how the nervous system coordinates movement to resolve redundancy, such as in the case of arm reaching movements where more degrees of freedom are available at the joint versus hand level. In particular, this work focuses on determining which coordinate frames can best represent human movements, allowing the motor system to solve the inverse kinematics problem in the presence of kinematic redundancies. In particular, in this work we used a multi-dimension sparse source separation method called FADA to derive sets of basis functions (here called sources) for both the task and joint spaces, with joint space being represented in terms of either the absolute or anatomical joint angles. We assessed the similarities between the joint and task sources in each of these joint representations. We found that the time-dependent profiles of the absolute reference frame's sources show greater similarity to those of the corresponding sources in the task space. This result was found to be statistically significant. Hence, our analysis suggests that the nervous system represents multi-joint arm movements using a limited number of basis functions, to allow for simple transformations between task and joint spaces. Importantly, joint space seems to be represented in terms of an absolute reference frame to achieve successful performance and simplify inverse kinematics transformations in the face of the existing kinematic redundancies. Further studies will be needed to determine the generalizability of this finding and its implications concerning neural control of movement.

show abstract

“…Synergies derived using NMF have also been applied to optimal movement generation for virtual arms (Fu et al, 2013) as well as myocontrol of a multi-DoF planar robotic arm using muscle synergies (Lunardini et al, 2015). So far, work has been focused on using time-invariant postural synergies in the kinematic domain and restricted to unimanual processes.…”

Section: Original Research Introductionmentioning

confidence: 99%

Applications of Synergies in Human Machine Interfaces

Vinjamuri¹,

Mao²,

Maybhate³

2019

Frontiers Research Topics

View full text Add to dashboard Cite

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.

show abstract

Analysis of Motor Synergies Utilization for Optimal Movement Generation for a Human-like Robotic Arm

Cited by 11 publications

References 13 publications

Human arm redundancy: a new approach for the inverse kinematics problem

Human arm redundancy: a new approach for the inverse kinematics problem

Human Arm Redundancy - A New Approach for the Inverse Kinematics Problem

Applications of Synergies in Human Machine Interfaces

Contact Info

Product

Resources

About