Catching the Integration Train: A Look Into the Next 10 Years of Motor-Control and Motor-Learning Research

Cheryl M. Glazebrook

doi:10.1123/kr.2018-0013

Catching the Integration Train: A Look Into the Next 10 Years of Motor-Control and Motor-Learning Research

Kinesiology Review ◽

10.1123/kr.2018-0013 ◽

2018 ◽

Vol 7 (2) ◽

pp. 130-141 ◽

Cited By ~ 1

Author(s):

Cheryl M. Glazebrook

Keyword(s):

Motor Control ◽

Motor Learning ◽

Learning Research

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Handlebar Robotic System for Bimanual Motor Control and Learning Research

Sensors ◽

10.3390/s21185991 ◽

2021 ◽

Vol 21 (18) ◽

pp. 5991

Author(s):

Lucas R. L. Cardoso ◽

Leonardo M. Pedro ◽

Arturo Forner-Cordero

Keyword(s):

Motor Control ◽

Motor Learning ◽

Impedance Control ◽

Human Interaction ◽

Unexpected Result ◽

Dominant Hand ◽

Movement Velocity ◽

Bimanual Task ◽

Learning Research ◽

Robotic Devices

Robotic devices can be used for motor control and learning research. In this work, we present the construction, modeling and experimental validation of a bimanual robotic device. We tested some hypotheses that may help to better understand the motor learning processes involved in the interlimb coordination function. The system emulates a bicycle handlebar with rotational motion, thus requiring bilateral upper limb control and a coordinated sequence of joint sub-movements. The robotic handlebar is compact and portable and can register in a fast rate both position and forces independently from arms, including prehension forces. An impedance control system was implemented in order to promote a safer environment for human interaction and the system is able to generate force fields, suitable for implementing motor learning paradigms. The novelty of the system is the decoupling of prehension and manipulation forces of each hand, thus paving the way for the investigation of hand dominance function in a bimanual task. Experiments were conducted with ten healthy subjects, kinematic and dynamic variables were measured during a rotational set of movements. Statistical analyses showed that movement velocity decreased with practice along with an increase in reaction time. This suggests an increase of the task planning time. Prehension force decreased with practice. However, an unexpected result was that the dominant hand did not lead the bimanual task, but helped to correct the movement, suggesting different roles for each hand during a cooperative bimanual task.

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OnPoint: A package for online experiments in motor control and motor learning

10.31234/osf.io/hwmpy ◽

2020 ◽

Author(s):

Jonathan Sanching Tsay ◽

Alan S. Lee ◽

Guy Avraham ◽

Darius E. Parvin ◽

Jeremy Ho ◽

...

Keyword(s):

Motor Control ◽

Motor Learning ◽

Open Source Software ◽

Research Progress ◽

Global Pandemic ◽

Large N ◽

Open Source Software Package ◽

Potential Applications ◽

Temporal And Spatial ◽

The One

Motor learning experiments are typically run in-person, exploiting finely calibrated setups (digitizing tablets, robotic manipulandum, full VR displays) that provide high temporal and spatial resolution. However, these experiments come at a cost, not limited to the one-time expense of purchasing equipment but also the substantial time devoted to recruiting participants and administering the experiment. Moreover, exceptional circumstances that limit in-person testing, such as a global pandemic, may halt research progress. These limitations of in-person motor learning research have motivated the design of OnPoint, an open-source software package for motor control and motor learning researchers. As with all online studies, OnPoint offers an opportunity to conduct large-N motor learning studies, with potential applications to do faster pilot testing, replicate previous findings, and conduct longitudinal studies (GitHub repository: https://github.com/alan-s-lee/OnPoint).

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