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      Balancing the playing field: collaborative gaming for physical training

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          Abstract

          Background

          Multiplayer video games promoting exercise-based rehabilitation may facilitate motor learning, by increasing motivation through social interaction. However, a major design challenge is to enable meaningful inter-subject interaction, whilst allowing for significant skill differences between players. We present a novel motor-training paradigm that allows real-time collaboration and performance enhancement, across a wide range of inter-subject skill mismatches, including disabled vs. able-bodied partnerships.

          Methods

          A virtual task consisting of a dynamic ball on a beam, is controlled at each end using independent digital force-sensing handgrips. Interaction is mediated through simulated physical coupling and locally-redundant control. Game performance was measured in 16 healthy-healthy and 16 patient-expert dyads, where patients were hemiparetic stroke survivors using their impaired arm. Dual-player was compared to single-player performance, in terms of score, target tracking, stability, effort and smoothness; and questionnaires probing user-experience and engagement.

          Results

          Performance of less-able subjects (as ranked from single-player ability) was enhanced by dual-player mode, by an amount proportionate to the partnership’s mismatch. The more abled partners’ performances decreased by a similar amount. Such zero-sum interactions were observed for both healthy-healthy and patient-expert interactions. Dual-player was preferred by the majority of players independent of baseline ability and subject group; healthy subjects also felt more challenged, and patients more skilled.

          Conclusion

          This is the first demonstration of implicit skill balancing in a truly collaborative virtual training task leading to heightened engagement, across both healthy subjects and stroke patients.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12984-017-0319-x) contains supplementary material, which is available to authorized users.

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          Most cited references20

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          Optimizing performance through intrinsic motivation and attention for learning: The OPTIMAL theory of motor learning.

          Effective motor performance is important for surviving and thriving, and skilled movement is critical in many activities. Much theorizing over the past few decades has focused on how certain practice conditions affect the processing of task-related information to affect learning. Yet, existing theoretical perspectives do not accommodate significant recent lines of evidence demonstrating motivational and attentional effects on performance and learning. These include research on (a) conditions that enhance expectancies for future performance, (b) variables that influence learners' autonomy, and (c) an external focus of attention on the intended movement effect. We propose the OPTIMAL (Optimizing Performance through Intrinsic Motivation and Attention for Learning) theory of motor learning. We suggest that motivational and attentional factors contribute to performance and learning by strengthening the coupling of goals to actions. We provide explanations for the performance and learning advantages of these variables on psychological and neuroscientific grounds. We describe a plausible mechanism for expectancy effects rooted in responses of dopamine to the anticipation of positive experience and temporally associated with skill practice. Learner autonomy acts perhaps largely through an enhanced expectancy pathway. Furthermore, we consider the influence of an external focus for the establishment of efficient functional connections across brain networks that subserve skilled movement. We speculate that enhanced expectancies and an external focus propel performers' cognitive and motor systems in productive "forward" directions and prevent "backsliding" into self- and non-task focused states. Expected success presumably breeds further success and helps consolidate memories. We discuss practical implications and future research directions.
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            On the analysis of movement smoothness

            Quantitative measures of smoothness play an important role in the assessment of sensorimotor impairment and motor learning. Traditionally, movement smoothness has been computed mainly for discrete movements, in particular arm, reaching and circle drawing, using kinematic data. There are currently very few studies investigating smoothness of rhythmic movements, and there is no systematic way of analysing the smoothness of such movements. There is also very little work on the smoothness of other movement related variables such as force, impedance etc. In this context, this paper presents the first step towards a unified framework for the analysis of smoothness of arbitrary movements and using various data. It starts with a systematic definition of movement smoothness and the different factors that influence smoothness, followed by a review of existing methods for quantifying the smoothness of discrete movements. A method is then introduced to analyse the smoothness of rhythmic movements by generalising the techniques developed for discrete movements. We finally propose recommendations for analysing smoothness of any general sensorimotor behaviour. Electronic supplementary material The online version of this article (doi:10.1186/s12984-015-0090-9) contains supplementary material, which is available to authorized users.
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              CNS learns stable, accurate, and efficient movements using a simple algorithm.

              We propose a new model of motor learning to explain the exceptional dexterity and rapid adaptation to change, which characterize human motor control. It is based on the brain simultaneously optimizing stability, accuracy and efficiency. Formulated as a V-shaped learning function, it stipulates precisely how feedforward commands to individual muscles are adjusted based on error. Changes in muscle activation patterns recorded in experiments provide direct support for this control scheme. In simulated motor learning of novel environmental interactions, muscle activation, force and impedance evolved in a manner similar to humans, demonstrating its efficiency and plausibility. This model of motor learning offers new insights as to how the brain controls the complex musculoskeletal system and iteratively adjusts motor commands to improve motor skills with practice.
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                Author and article information

                Contributors
                m.mace11@imperial.ac.uk
                nawal.kinany@epfl.ch
                paul.rinne@imperial.ac.uk
                anthony.rayner12@imperial.ac.uk
                p.bentley@imperial.ac.uk
                e.burdet@imperial.ac.uk
                Journal
                J Neuroeng Rehabil
                J Neuroeng Rehabil
                Journal of NeuroEngineering and Rehabilitation
                BioMed Central (London )
                1743-0003
                20 November 2017
                20 November 2017
                2017
                : 14
                : 116
                Affiliations
                [1 ]ISNI 0000 0001 2113 8111, GRID grid.7445.2, Department of Bioengineering, , Imperial College of Science, Technology and Medicine, ; London, UK
                [2 ]ISNI 0000 0001 2113 8111, GRID grid.7445.2, Division of Brain Sciences, , Imperial College of Science, Technology and Medicine, ; London, UK
                [3 ]ISNI 0000000121839049, GRID grid.5333.6, Center for Neuroprosthetics, , École Polytechnique Fédérale de Lausanne, ; Lausanne, Switzerland
                [4 ]ISNI 0000 0001 2224 0361, GRID grid.59025.3b, School of Mechanical and Aerospace Engineering, , Nanyang Technological University, ; Singapore, Singapore
                Author information
                http://orcid.org/0000-0001-9599-448X
                Article
                319
                10.1186/s12984-017-0319-x
                5694911
                29151360
                7b01b49c-424f-42e2-b6fe-751a06735d4c
                © The Author(s) 2017

                Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 23 May 2017
                : 16 October 2017
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100011199, FP7 Ideas: European Research Council;
                Award ID: PEOPLE-ITN-317488-CONTEST
                Funded by: FundRef http://dx.doi.org/10.13039/100011199, FP7 Ideas: European Research Council;
                Award ID: ICT-601003 BALANCE
                Funded by: FundRef http://dx.doi.org/10.13039/100011199, FP7 Ideas: European Research Council;
                Award ID: ICT-2013-10 SYMBITRON
                Funded by: FundRef http://dx.doi.org/10.13039/100010663, H2020 European Research Council;
                Award ID: ICT-644727 COGIMON
                Funded by: FundRef http://dx.doi.org/10.13039/501100000761, Imperial College London;
                Award ID: Imperial Confidence in Concept (ICiC) Award
                Funded by: FundRef http://dx.doi.org/10.13039/501100000266, Engineering and Physical Sciences Research Council;
                Award ID: EP/N029003/1 MOTION
                Categories
                Research
                Custom metadata
                © The Author(s) 2017

                Neurosciences
                social interaction,collaboration,rehabilitation,stroke,physical exercise,patient engagement,exergames,robotics

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