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      Evaluation of the Leap Motion Controller during the performance of visually-guided upper limb movements

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          Abstract

          Kinematic analysis of upper limb reaching provides insight into the central nervous system control of movements. Until recently, kinematic examination of motor control has been limited to studies conducted in traditional research laboratories because motion capture equipment used for data collection is not easily portable and expensive. A recently developed markerless system, the Leap Motion Controller (LMC), is a portable and inexpensive tracking device that allows recording of 3D hand and finger position. The main goal of this study was to assess the concurrent reliability and validity of the LMC as compared to the Optotrak, a criterion-standard motion capture system, for measures of temporal accuracy and peak velocity during the performance of upper limb, visually-guided movements. In experiment 1, 14 participants executed aiming movements to visual targets presented on a computer monitor. Bland-Altman analysis was conducted to assess the validity and limits of agreement for measures of temporal accuracy (movement time, duration of deceleration interval), peak velocity, and spatial accuracy (endpoint accuracy). In addition, a one-sample t-test was used to test the hypothesis that the error difference between measures obtained from Optotrak and LMC is zero. In experiment 2, 15 participants performed a Fitts’ type aiming task in order to assess whether the LMC is capable of assessing a well-known speed-accuracy trade-off relationship. Experiment 3 assessed the temporal coordination pattern during the performance of a sequence consisting of a reaching, grasping, and placement task in 15 participants. Results from the t-test showed that the error difference in temporal measures was significantly different from zero. Based on the results from the 3 experiments, the average temporal error in movement time was 40±44 ms, and the error in peak velocity was 0.024±0.103 m/s. The limits of agreement between the LMC and Optotrak for spatial accuracy measures ranged between 2–5 cm. Although the LMC system is a low-cost, highly portable system, which could facilitate collection of kinematic data outside of the traditional laboratory settings, the temporal and spatial errors may limit the use of the device in some settings.

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          Most cited references41

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          Analysis of the Accuracy and Robustness of the Leap Motion Controller

          The Leap Motion Controller is a new device for hand gesture controlled user interfaces with declared sub-millimeter accuracy. However, up to this point its capabilities in real environments have not been analyzed. Therefore, this paper presents a first study of a Leap Motion Controller. The main focus of attention is on the evaluation of the accuracy and repeatability. For an appropriate evaluation, a novel experimental setup was developed making use of an industrial robot with a reference pen allowing a position accuracy of 0.2 mm. Thereby, a deviation between a desired 3D position and the average measured positions below 0.2 mm has been obtained for static setups and of 1.2 mm for dynamic setups. Using the conclusion of this analysis can improve the development of applications for the Leap Motion controller in the field of Human-Computer Interaction.
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            Computational principles of sensorimotor control that minimize uncertainty and variability.

            Sensory and motor noise limits the precision with which we can sense the world and act upon it. Recent research has begun to reveal computational principles by which the central nervous system reduces the sensory uncertainty and movement variability arising from this internal noise. Here we review the role of optimal estimation and sensory filtering in extracting the sensory information required for motor planning, and the role of optimal control, motor adaptation and impedance control in the specification of the motor output signal.
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              Goal-directed aiming: two components but multiple processes.

              This article reviews the behavioral literature on the control of goal-directed aiming and presents a multiple-process model of limb control. The model builds on recent variants of Woodworth's (1899) two-component model of speed-accuracy relations in voluntary movement and incorporates ideas about dynamic online limb control based on prior expectations about the efferent and afferent consequences of a planned movement. The model considers the relationship between movement speed and accuracy, and how performers adjust their trial-to-trial aiming behavior to find a safe, but fast, zone for movement execution. The model also outlines how the energy and safety costs associated with different movement outcomes contribute to movement planning processes and the control of aiming trajectories. Our theoretical position highlights the importance of advance knowledge about the sensory information that will be available for online control and the need to develop a robust internal representation of expected sensory consequences. We outline how early practice contributes to optimizing strategic planning to avoid worst-case outcomes associated with inherent neural-motor variability. Our model considers the role of both motor development and motor learning in refining feed-forward and online control. The model reconciles procedural and representational accounts of the specificity-of-learning phenomenon. Finally, we examine the breakdown of perceptual-motor precision in several special populations (i.e., Down syndrome, Williams syndrome, autism spectrum disorder, normal aging) within the framework of a multiple-process approach to goal-directed aiming.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: InvestigationRole: MethodologyRole: ResourcesRole: SupervisionRole: Writing – original draft
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: Project administrationRole: Writing – review & editing
                Role: InvestigationRole: SoftwareRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: SoftwareRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                12 March 2018
                2018
                : 13
                : 3
                : e0193639
                Affiliations
                [1 ] Department of Kinesiology, University of Waterloo, Waterloo, Canada
                [2 ] Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, Canada
                University of Exeter, UNITED KINGDOM
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-5293-5161
                Article
                PONE-D-17-26947
                10.1371/journal.pone.0193639
                5846796
                29529064
                2c2de5cc-729f-46a4-8bd6-63595be09530
                © 2018 Niechwiej-Szwedo et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 18 July 2017
                : 15 February 2018
                Page count
                Figures: 11, Tables: 2, Pages: 25
                Funding
                The authors received no specific funding for this work.
                Categories
                Research Article
                Biology and Life Sciences
                Anatomy
                Musculoskeletal System
                Medicine and Health Sciences
                Anatomy
                Musculoskeletal System
                Physical Sciences
                Physics
                Classical Mechanics
                Deceleration
                Physical Sciences
                Physics
                Classical Mechanics
                Kinematics
                Physical Sciences
                Physics
                Classical Mechanics
                Motion
                Velocity
                Computer and Information Sciences
                Information Technology
                Data Processing
                Physical Sciences
                Physics
                Classical Mechanics
                Acceleration
                Biology and Life Sciences
                Anatomy
                Musculoskeletal System
                Limbs (Anatomy)
                Arms
                Hands
                Medicine and Health Sciences
                Anatomy
                Musculoskeletal System
                Limbs (Anatomy)
                Arms
                Hands
                Biology and Life Sciences
                Anatomy
                Musculoskeletal System
                Limbs (Anatomy)
                Arms
                Hands
                Thumbs
                Medicine and Health Sciences
                Anatomy
                Musculoskeletal System
                Limbs (Anatomy)
                Arms
                Hands
                Thumbs
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                All relevant data are within the paper and its Supporting Information files.

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