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

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).

Contemporary issues and theories of motor control, motor learning, and neuroplasticity

2013 ◽  
pp. 69-98 ◽  
Author(s):  
MARGARET L. ROLLER ◽  
ROLANDO T. LAZARO ◽  
NANCY N. BYL ◽  
DARCY A. UMPHRED
Keyword(s):  
Motor Control ◽  
Motor Learning

scholarly journals Heritability of motor control and motor learning

2013 ◽  
Vol 1 (7) ◽  
pp. e00188 ◽  
Author(s):  
Julia Missitzi ◽  
Reinhard Gentner ◽  
Angelica Misitzi ◽  
Nickos Geladas ◽  
Panagiotis Politis ◽  
...  
Keyword(s):  
Motor Control ◽  
Motor Learning

scholarly journals Human-machine-human interaction in motor control and rehabilitation: a review

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Emek Barış Küçüktabak ◽  
Sangjoon J. Kim ◽  
Yue Wen ◽  
Kevin Lynch ◽  
Jose L. Pons
Keyword(s):  
Motor Control ◽  
Motor Learning ◽  
Task Performance ◽  
Human Interaction ◽  
Future Research ◽  
Physical Interaction ◽  
Interaction Type ◽  
Partner Characteristics ◽  
The Impact ◽  
Individual Motor

Abstract Background Human-human (HH) interaction mediated by machines (e.g., robots or passive sensorized devices), which we call human-machine-human (HMH) interaction, has been studied with increasing interest in the last decade. The use of machines allows the implementation of different forms of audiovisual and/or physical interaction in dyadic tasks. HMH interaction between two partners can improve the dyad’s ability to accomplish a joint motor task (task performance) beyond either partner’s ability to perform the task solo. It can also be used to more efficiently train an individual to improve their solo task performance (individual motor learning). We review recent research on the impact of HMH interaction on task performance and individual motor learning in the context of motor control and rehabilitation, and we propose future research directions in this area. Methods A systematic search was performed on the Scopus, IEEE Xplore, and PubMed databases. The search query was designed to find studies that involve HMH interaction in motor control and rehabilitation settings. Studies that do not investigate the effect of changing the interaction conditions were filtered out. Thirty-one studies met our inclusion criteria and were used in the qualitative synthesis. Results Studies are analyzed based on their results related to the effects of interaction type (e.g., audiovisual communication and/or physical interaction), interaction mode (collaborative, cooperative, co-active, and competitive), and partner characteristics. Visuo-physical interaction generally results in better dyadic task performance than visual interaction alone. In cases where the physical interaction between humans is described by a spring, there are conflicting results as to the effect of the stiffness of the spring. In terms of partner characteristics, having a more skilled partner improves dyadic task performance more than having a less skilled partner. However, conflicting results were observed in terms of individual motor learning. Conclusions Although it is difficult to draw clear conclusions as to which interaction type, mode, or partner characteristic may lead to optimal task performance or individual motor learning, these results show the possibility for improved outcomes through HMH interaction. Future work that focuses on selecting the optimal personalized interaction conditions and exploring their impact on rehabilitation settings may facilitate the transition of HMH training protocols to clinical implementations.

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