Highlights from the 30th Annual Meeting of the Society for the Neural Control of Movement

Russo, Marta; Ozeri-Engelhard, Nofar; Hupfeld, Kathleen E.; Nettekoven, Caroline; Thibault, Simon; Sedaghat-Nejad, Ehsan; Buchwald, Daniela; Xing, David; Zobeiri, Omid A.; Kilteni, Konstantina; Albert, Scott T.; Ariani, Giacomo

doi:10.1152/jn.00334.2021

Cited by 6 publications

(4 citation statements)

References 60 publications

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“…In the bottom-up approach, the starting points are experimental motor tasks of lower complexity, while, in the top-down approach, the starting point is a complex real world motor task. The definition for ‘a complex motor skill’ is still under debate [ 241 , 242 ], but we here describe ‘complex motor skills’ as motor tasks with an infinite number of solutions to execute them. Due to the higher complexity of these tasks, it generally takes longer to train a complex real world motor skill [ 5 ] (e.g., hours, weeks or months) compared to the motor task of lower complexity in a laboratory environment.…”

Section: How To Transfer Motor Learning Principles To Complex Real Wo...mentioning

confidence: 99%

Using Artificial Intelligence for Assistance Systems to Bring Motor Learning Principles into Real World Motor Tasks

Vandevoorde

Vollenkemper

Schwan

et al. 2022

Sensors

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Humans learn movements naturally, but it takes a lot of time and training to achieve expert performance in motor skills. In this review, we show how modern technologies can support people in learning new motor skills. First, we introduce important concepts in motor control, motor learning and motor skill learning. We also give an overview about the rapid expansion of machine learning algorithms and sensor technologies for human motion analysis. The integration between motor learning principles, machine learning algorithms and recent sensor technologies has the potential to develop AI-guided assistance systems for motor skill training. We give our perspective on this integration of different fields to transition from motor learning research in laboratory settings to real world environments and real world motor tasks and propose a stepwise approach to facilitate this transition.

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Section: How To Transfer Motor Learning Principles To Complex Real Wo...mentioning

confidence: 99%

Using Artificial Intelligence for Assistance Systems to Bring Motor Learning Principles into Real World Motor Tasks

Vandevoorde

Vollenkemper

Schwan

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…A recent experimental study examined how humans strike a target with a whip manipulating it in discrete and rhythmic fashion. 5 , 6 Analysis of both movement styles showed that the hand motion exhibited a pronounced speed peak during the throwing action. The motion profile was reminiscent of the bell-shaped speed profiles observed in a plethora of other behaviors: discrete human actions such as point-to-point motions exhibit a robustly repeatable pattern with a roughly symmetrical bell-shaped speed profile, both for horizontal-plane motions (which are generally straight) and for vertical-plane motions (which are usually curved).…”

Section: Introductionmentioning

confidence: 99%

Learning to manipulate a whip with simple primitive actions – A simulation study

Nah¹,

Krotov²,

Russo³

et al. 2023

iScience

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“…While such simplifications in the experimental testbeds are necessary for initial advances as they limit confounding variability, some essential aspects of the dynamics are necessarily ‘controlled out’. Little is known about how or whether the control frameworks developed for simple two-dimensional reaching movements scale up to multi-joint movements in three dimensions, let alone to interactions with complex objects, such as a whip [7]. Therefore, this study did not start with specific hypotheses derived from existent control theories, but rather adopted an object-centered or task-dynamic approach to allow insights emerging from the data, specifically from the whip.…”

Section: Introductionmentioning

confidence: 99%

Motor control beyond reach—how humans hit a target with a whip

et al. 2022

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Humans are strikingly adept at manipulating complex objects, from tying shoelaces to cracking a bullwhip. These motor skills have highly nonlinear interactive dynamics that defy reduction into parts. Yet, despite advances in data recording and processing, experiments in motor neuroscience still prioritize experimental reduction over realistic complexity. This study embraced the fully unconstrained behaviour of hitting a target with a 1.6-m bullwhip, both in rhythmic and discrete fashion. Adopting an object-centered approach to test the hypothesis that skilled movement simplifies the whip dynamics, the whip's evolution was characterized in relation to performance error and hand speed. Despite widely differing individual strategies, both discrete and rhythmic styles featured a cascade-like unfolding of the whip. Whip extension and orientation at peak hand speed predicted performance error, at least in the rhythmic style, suggesting that humans accomplished the task by setting initial conditions. These insights may inform further studies on human and robot control of complex objects.

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Highlights from the 30th Annual Meeting of the Society for the Neural Control of Movement

Cited by 6 publications

References 60 publications

Using Artificial Intelligence for Assistance Systems to Bring Motor Learning Principles into Real World Motor Tasks

Using Artificial Intelligence for Assistance Systems to Bring Motor Learning Principles into Real World Motor Tasks

Learning to manipulate a whip with simple primitive actions – A simulation study

Motor control beyond reach—how humans hit a target with a whip

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