On the Relationship Between Human Motor Control Performance and Kinematic Synergies in Upper Limb Prosthetics

2018 ◽  
Author(s):  
Ricardo Garcia-Rosas ◽  
Denny Oetomo ◽  
Chris Manzie ◽  
Ying Tan ◽  
Peter Choong
Keyword(s):  
Motor Control ◽  
Upper Limb ◽  
Control Performance ◽  
Human Motor Control ◽  
Kinematic Synergies ◽  
Human Motor ◽  
The Relationship ◽  
Upper Limb Prosthetics

scholarly journals A Human-like Upper-limb Motion Planner: Generating naturalistic movements for humanoid robots

2021 ◽  
Vol 18 (2) ◽  
pp. 172988142199858
Author(s):  
Gianpaolo Gulletta ◽  
Eliana Costa e Silva ◽  
Wolfram Erlhagen ◽  
Ruud Meulenbroek ◽  
Maria Fernanda Pires Costa ◽  
...  
Keyword(s):  
Motor Control ◽  
Upper Limb ◽  
Planning Process ◽  
Computational Cost ◽  
Humanoid Robots ◽  
Efficient Manner ◽  
Daily Lives ◽  
Human Motor Control ◽  
Planning Algorithm ◽  
Human Motor

As robots are starting to become part of our daily lives, they must be able to cooperate in a natural and efficient manner with humans to be socially accepted. Human-like morphology and motion are often considered key features for intuitive human–robot interactions because they allow human peers to easily predict the final intention of a robotic movement. Here, we present a novel motion planning algorithm, the Human-like Upper-limb Motion Planner, for the upper limb of anthropomorphic robots, that generates collision-free trajectories with human-like characteristics. Mainly inspired from established theories of human motor control, the planning process takes into account a task-dependent hierarchy of spatial and postural constraints modelled as cost functions. For experimental validation, we generate arm-hand trajectories in a series of tasks including simple point-to-point reaching movements and sequential object-manipulation paradigms. Being a major contribution to the current literature, specific focus is on the kinematics of naturalistic arm movements during the avoidance of obstacles. To evaluate human-likeness, we observe kinematic regularities and adopt smoothness measures that are applied in human motor control studies to distinguish between well-coordinated and impaired movements. The results of this study show that the proposed algorithm is capable of planning arm-hand movements with human-like kinematic features at a computational cost that allows fluent and efficient human–robot interactions.

scholarly journals Adiabatic invariants drive rhythmic human motion in variable gravity

2019 ◽  
Author(s):  
N. Boulanger ◽  
F. Buisseret ◽  
V. Dehouck ◽  
F. Dierick ◽  
O. White
Keyword(s):  
Motor Control ◽  
Human Motion ◽  
Adiabatic Invariants ◽  
Human Motor Control ◽  
Changing Environments ◽  
Human Movements ◽  
Human Motor ◽  
Variable Gravity ◽  
Points Of View ◽  
Motor Strategies

AbstractNatural human movements are stereotyped. They minimise cost functions that include energy, a natural candidate from mechanical and physiological points of view. In time-changing environments, however, motor strategies are modified since energy is no longer conserved. Adiabatic invariants are relevant observables in such cases, although they have not been investigated in human motor control so far. We fill this gap and show that the theory of adiabatic invariants explains how humans move when gravity varies.

Effects of Scaling and Sequence on Performance of Dynamic Bimanual Path Following Tasks

2020 ◽  
pp. 2042001 ◽  
Author(s):  
Jacob R. Boehm ◽  
Nicholas P. Fey ◽  
Ann Majewicz Fey
Keyword(s):  
Motor Control ◽  
Control System ◽  
Path Following ◽  
Vital Role ◽  
Human Motor Control ◽  
Human In The Loop ◽  
Right Hand ◽  
Human Motor ◽  
The Right ◽  
Manual Performance

Bimanual coordination plays a vital role in many haptic and robotic system operations. However, theories in bimanual human motor control are rarely integrated into the control system for human-in-the-loop robots, potentially limiting the usability and collaborative potential between the human and robot, particularly for complex tasks such as robotic surgery. To inform future integration, we investigate unknown manual performance relationships regarding the scaling (the size of one hand’s motions compared to the other) and sequence (the order in which the hands move) of complex bimanual path following tasks. For scaling variations, either the left or right hand desired trajectory amplitude was increased. For sequence, the task was split so that the hands moved sequentially or simultaneously. The experiment is performed by 11 inexperienced, able bodied subjects (all right-handed) in a virtual environment while using haptic devices. Results show significant ([Formula: see text]) decreased manual performance for one hand when the opposite hand is scaled, thus suggesting an increase in the scale of one hand will decrease the performance of the contralateral. Results also show a significant decrease in performance for the left hand when moving simultaneous with the right, but the right hand does not show such a decrease in performance. This might suggest that only the nondominant hand suffers from simultaneous motion conditions. These results may lead to unique opportunities to integrate theories related to human motor control into the control system for haptic and robotic systems used in complex bimanual upper-limb tasks.

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