Upper Limb Joint Angular Velocity Synergies of Human Reaching Movements

Tang, Shangjie; Barsotti, Michele; Stroppa, Fabio; Frisoli, Antonio; Wu, Xiaoying; Hou, Wensheng

doi:10.1109/cbs.2018.8612235

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…The results show that 3 synergies were sufficient to represent approximately 90% of the variance in the recorded data for 10 joint angles, corresponding to 7 DOFs of the arm and 3 DOFs of the shoulder girdle. The analysis in [32] also featured similar results using joint angular velocities during reaching motions. These results justify that fewer kinematic synergies, with certain losses in accuracy depending on the number of synergies, can be used to represent and reconstruct the natural reaching movements of the human arm.…”

Section: Motor Synergiessupporting

confidence: 66%

“…PCA has been used in numerous studies to investigate natural movements, such as catching [31] or reaching [32] tasks, where it was successful in representing the observed physiological complexities using fewer numbers of principal components (PCs) or synergies. One reason for this is that when capturing variances, the PCA uses a more intuitive method to exploit the coupling of the DOFs.…”

Section: Kinematic Synergy Extractionmentioning

confidence: 99%

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Synergy-Space Recurrent Neural Network for Transferable Forearm Motion Prediction from Residual Limb Motion

Ahmed

Chai

Shimoda

et al. 2023

Sensors

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Transhumeral amputees experience considerable difficulties with controlling a multifunctional prosthesis (powered hand, wrist, and elbow) due to the lack of available muscles to provide electromyographic (EMG) signals. The residual limb motion strategy has become a popular alternative for transhumeral prosthesis control. It provides an intuitive way to estimate the motion of the prosthesis based on the residual shoulder motion, especially for target reaching tasks. Conventionally, a predictive model, typically an artificial neural network (ANN), is directly trained and relied upon to map the shoulder–elbow kinematics using the data from able-bodied subjects without extracting any prior synergistic information. However, it is essential to explicitly identify effective synergies and make them transferable across amputee users for higher accuracy and robustness. To overcome this limitation of the conventional ANN learning approach, this study explicitly combines the kinematic synergies with a recurrent neural network (RNN) to propose a synergy-space neural network for estimating forearm motions (i.e., elbow joint flexion–extension and pronation–supination angles) based on residual shoulder motions. We tested 36 training strategies for each of the 14 subjects, comparing the proposed synergy-space and conventional neural network learning approaches, and we statistically evaluated the results using Pearson’s correlation method and the analysis of variance (ANOVA) test. The offline cross-subject analysis indicates that the synergy-space neural network exhibits superior robustness to inter-individual variability, demonstrating the potential of this approach as a transferable and generalized control strategy for transhumeral prosthesis control.

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Section: Motor Synergiessupporting

confidence: 66%

Section: Kinematic Synergy Extractionmentioning

confidence: 99%

Synergy-Space Recurrent Neural Network for Transferable Forearm Motion Prediction from Residual Limb Motion

Ahmed

Chai

Shimoda

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…The instructions for reaching movements were given with a randomized interval between 1 and 3 s. Each of these ten Participants were required to avoid any redundant movement during the whole procedure of task performance. A more detailed experimental procedure was described in a previous report (Tang et al, 2018).…”

Section: Methodsmentioning

confidence: 99%

Kinematic Synergy of Multi-DoF Movement in Upper Limb and Its Application for Rehabilitation Exoskeleton Motion Planning

et al. 2019

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It is important for rehabilitation exoskeletons to move with a spatiotemporal motion patterns that well match the upper-limb joint kinematic characteristics. However, few efforts have been made to manipulate the motion control based on human kinematic synergies. This work analyzed the spatiotemporal kinematic synergies of right arm reaching movement and investigated their potential usage in upper limb assistive exoskeleton motion planning. Ten right-handed subjects were asked to reach 10 target button locations placed on a cardboard in front. The kinematic data of right arm were tracked by a motion capture system. Angular velocities over time for shoulder flexion/extension, shoulder abduction/adduction, shoulder internal/external rotation, and elbow flexion/extension were computed. Principal component analysis (PCA) was used to derive kinematic synergies from the reaching task for each subject. We found that the first four synergies can explain more than 94% of the variance. Moreover, the joint coordination patterns were dynamically regulated over time as the number of kinematic synergy (PC) increased. The synergies with different order played different roles in reaching movement. Our results indicated that the low-order synergies represented the overall trend of motion patterns, while the high-order synergies described the fine motions at specific moving phases. A 4-DoF upper limb assistive exoskeleton was modeled in SolidWorks to simulate assistive exoskeleton movement pattern based on kinematic synergy. An exoskeleton Denavit-Hartenberg (D-H) model was established to estimate the exoskeleton moving pattern in reaching tasks. The results further confirmed that kinematic synergies could be used for exoskeleton motion planning, and different principal components contributed to the motion trajectory and end-point accuracy to some extent. The findings of this study may provide novel but simplified strategies for the development of rehabilitation and assistive robotic systems approximating the motion pattern of natural upper-limb motor function.

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Towards online myoelectric control based on muscle synergies-to-force mapping for robotic applications

et al. 2021

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Upper Limb Joint Angular Velocity Synergies of Human Reaching Movements

Cited by 5 publications

References 19 publications

Synergy-Space Recurrent Neural Network for Transferable Forearm Motion Prediction from Residual Limb Motion

Synergy-Space Recurrent Neural Network for Transferable Forearm Motion Prediction from Residual Limb Motion

Kinematic Synergy of Multi-DoF Movement in Upper Limb and Its Application for Rehabilitation Exoskeleton Motion Planning

Towards online myoelectric control based on muscle synergies-to-force mapping for robotic applications

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