3DOM: A 3 Degree of Freedom Manipulandum to Investigate Redundant Motor Control

Klein, Julius; Roach, Nick; Burdet, Etienne

doi:10.1109/toh.2013.59

Cited by 17 publications

(17 citation statements)

References 27 publications

(37 reference statements)

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“…Customized chest belts fixed participants’ trunk orientation and shoulder position throughout the experimental session. Participants made 15cm straight reaching movements in a horizontal plane at shoulder level (Fig 1) while their right hand was attached to a robotic manipulandum [29]. The robotic manipulandum allowed for free planar movement.…”

Section: Methodsmentioning

confidence: 99%

“…The main novelty in the current design was that we only implicitly reinforced a specific joint configuration (more flexion or extension in the wrist joint relative to baseline behavior). This advancement was possible by using a novel robotic manipulandum capable of measuring and controlling the three main joints of the arm (shoulder, elbow and wrist) [29], which ensured the same starting position and joint configuration for the beginning of each trial. To encourage specific movement solutions, we added noise to the visual endpoint feedback, similarly to the implicit feedback condition used by Manley et al [23].…”

Section: Introductionmentioning

confidence: 99%

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Minimizing endpoint variability through reinforcement learning during reaching movements involving shoulder, elbow and wrist

et al. 2017

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Reaching movements are comprised of the coordinated action across multiple joints. The human skeleton is redundant for this task because different joint configurations can lead to the same endpoint in space. How do people learn to use combinations of joints that maximize success in goal-directed motor tasks? To answer this question, we used a 3-degree-of-freedom manipulandum to measure shoulder, elbow and wrist joint movements during reaching in a plane. We tested whether a shift in the relative contribution of the wrist and elbow joints to a reaching movement could be learned by an implicit reinforcement regime. Unknown to the participants, we decreased the task success for certain joint configurations (wrist flexion or extension, respectively) by adding random variability to the endpoint feedback. In return, the opposite wrist postures were rewarded in the two experimental groups (flexion and extension group). We found that the joint configuration slowly shifted towards movements that provided more control over the endpoint and hence higher task success. While the overall learning was significant, only the group that was guided to extend the wrist joint more during the movement showed substantial learning. Importantly, all changes in movement pattern occurred independent of conscious awareness of the experimental manipulation. These findings suggest that the motor system is generally sensitive to its output variability and can optimize joint-space solutions that minimize task-relevant output variability. We discuss biomechanical biases (e.g. joint’s range of movement) that could impose hurdles to the learning process.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Minimizing endpoint variability through reinforcement learning during reaching movements involving shoulder, elbow and wrist

et al. 2017

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“…2). Similar to [13] we can derive the differential kinematics by using the Jacobian J(ρ) = [δp i /δρ j ] (find the Jacobian in [31]) and can calculate the condition number over the manipulandum workspace (see Fig. 3(a)).…”

Section: A Kinematic Analysismentioning

confidence: 99%

“…Planar human arm motor studies are typically performed with a specific type of manipulandum: an asymmetric 5R parallel mechanism design [7], [8], [9], [10], [11]. Competing designs include the 5R symmetric parallel mechanism design [12], [13], the 2P cable-driven approach [14], and a compact cam disc-based device with one active degree of freedom [15].…”

Section: Introductionmentioning

confidence: 99%

Two-dimensional orthoglide mechanism for revealing areflexive human arm mechanical properties

Höppner

Grebenstein

Smagt

2015

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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The most accurate and dependable approach to the in-vivo identification of human limb stiffness is by position perturbation. Moving the limb over a small distance and measuring the effective force gives, when states are steady, direct information about said stiffness. However, existing manipulandi are comparatively slow and/or not very stiff, such that a lumped stiffness is measured. This lumped stiffness includes the limb response during or after reflexes influenced by both, the passive musculotendon and active neuronal component. As this approach usually leads to inconsistencies between the data and the stiffness model, we argue in favour of fast, pre-reflex impedance measurementsi.e., completing the perturbation movement and collecting the data before effects of spinal reflexes or even from the motor cortex can influence the measurements. To obtain such fast planar movements, we constructed a dedicated orthoglide robot while focusing on a lightweight and stiff design. Our subject study of a force task with this device lead to very clean data with always positive definite Cartesian stiffness matrices. By representing them as ellipses, we found them to be substantially bigger in comparison to standard literature which we address to a larger number of recruited motor units. While ellipses orientation and the length of their main axis increased, the shape decreased with the exerted force. The device will be used to derive design criteria for variable-stiffness robots, and to investigate the relation between muscular activity and areflexive joint stiffness for teleoperational approaches.

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“…Force feedback devices have provided powerful tools for investigation of sensorimotor control and learning in humans [1], [2], [3], [4]. Instrumentation of haptic interfaces in magnetic resonance (MR) imaging can be used to study neural mechanisms of human motor control.…”

Section: Introductionmentioning

confidence: 99%

Development and evaluation of a portable MR compatible haptic interface for human motor control

Farkhatdinov

Garnier

Burdet

2015

2015 IEEE World Haptics Conference (WHC)

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Abstract-This paper presents the development and evaluation of an MR compatible haptic interface for human motor control studies, which can be easily installed and removed from the scanner room. The interface is actuated by a powerful shielded DC motor located 2.1 m away from the 3T MR scanner. Rotational movements are transmitted to a subject's wrist through preloaded cable transmission which drives the handle unit. The handle of the interface is designed to be adjustable to different hands size, enabling comfortable and natural wrist movements. The nominal achievable wrist torque of the interface is up to 2Nm. The interface is easily transportable due to its design characteristics. A dynamic model of the interface is presented and identified for position and torque control modes. Phantom MR compatibility test in clinical environment showed that the interface is compatible with strong magnetic field and radio frequency emission and its operation does not affect the quality of MR images.

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3DOM: A 3 Degree of Freedom Manipulandum to Investigate Redundant Motor Control

Cited by 17 publications

References 27 publications

Minimizing endpoint variability through reinforcement learning during reaching movements involving shoulder, elbow and wrist

Minimizing endpoint variability through reinforcement learning during reaching movements involving shoulder, elbow and wrist

Two-dimensional orthoglide mechanism for revealing areflexive human arm mechanical properties

Development and evaluation of a portable MR compatible haptic interface for human motor control

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