Eye-Hand Coordination Training for Sports with Mid-air VR

Batmaz, Anil Ufuk; Sun, Xintian; Taskiran, Dogu; Stuerzlinger, Wolfgang

doi:10.1145/3385956.3418971

Cited by 9 publications

(2 citation statements)

References 62 publications

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“…Simultaneously, the eye tracker recorded gaze data. A similar work is presented by [11] and [12] in a different area.…”

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confidence: 52%

Proposed Eye-Hand Correlation Assessment System: A Novel Approach for Evaluating Coordination

Copaci,

Pernalete,

Ortiz

et al. 2024

IEEE Access

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Post-stroke patients often experience difficulties with hand-eye coordination, resulting in reduced perceptual accuracy and finger dexterity. These impairments can significantly impact their overall quality of life. This study introduces a new eye-hand correlation assessment system with enhanced assessment metrics for evaluating coordination and upper limb disability. It utilizes a robotic haptic system and correlates eye-gaze with hand and arm movements. The experimental setup integrates a haptic device, eye tracker, and four inertial sensors to measure the upper limb joint angles displacement. The user performs various hand movement patterns in a virtual environment while data is simultaneously collected from the hand, eye, and arm movements. A metric for eye-hand coordination based on the Pearson coefficient has been developed (EHCM) and correlated with the upper limb movements, to detect possible upper limb disabilities. The system was tested with ten healthy volunteers, one of which had a type of upper-limb disability. Most of the EHCM scores showed a very strong correlation for those users without upper-limb disability or known eye-hand coordination problems. Also, the proposed system was preliminary tested with post-stroke patients to identify the limitations and weak points. More tests will be conducted to validate this metric as well as correlate it with a CNN classifier for detecting incorrect wrist-upper limb movements. The main objective of this work was to develop an evaluation tool that assists therapists in quantifying collected data from robot therapy, for assessing the degree of disability in patients.INDEX TERMS eye-gaze, eye-hand coordination metric, eye-hand correlation system, haptic device, upper limb movements I. INTRODUCTIONT HE human hand is one of the most complex organs, which gives us the ability to interact with the environment in an easy way and perform a variety of actions. From delicate tasks like writing to more robust ones like carrying a bag, the hand is an essential part of everyone's daily activities. However, injuries, disabilities, and neurological conditions often impair its function, significantly affecting a person's quality of life.According to the World Health Organization [1] , on average, 15 million people worldwide suffer from a stroke each year. Out of this number, more than 5 million people are left permanently or partially disabled, making it one of the common causes of upper-limb disabilities. Hand rehabilitation has become an increasingly important field of physical therapy, with the aim of improving hand function, reducing pain and stiffness, and enhancing overall mobility and dexterity.Eye-hand coordination refers to the ability of our visual system to coordinate the information received through the eyes in order to control, guide, and direct the movements of the arms and hands when performing a given task. Research has shown that repeatedly practicing specific tasks can enhance motor skills and contribute to motor recovery. However, in many cases, therapy for...

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“…Simultaneously, the eye tracker recorded gaze data. A similar work is presented by [11] and [12] in a different area.…”

mentioning

confidence: 52%

Proposed Eye-Hand Correlation Assessment System: A Novel Approach for Evaluating Coordination

Copaci,

Pernalete,

Ortiz

et al. 2024

IEEE Access

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“…Concerning the first factor, hand dominance, there is evidence that at least some kinematic properties of virtual hand reaching movements can be different when users reach using their dominant hand than when they perform the same reaches using their non-dominant hand (Arlati et al 2021;Batmaz et al 2020). For example, Batmaz and colleagues (2020; Study 1) had participants perform virtual hand reaches between targets in an evenly spaced array that was aligned with either the vertical or the horizonal plane.…”

Section: The Moderating Influence Of Hand Dominance and Interaction H...mentioning

confidence: 99%

Reaching interactions in virtual reality: the effect of movement direction, hand dominance, and hemispace on the kinematic properties of inward and outward reaches

Clark,

Iskandarani,

Riggs

2024

Virtual Reality

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Recent literature has revealed that when users reach to select objects in VR, they can adapt how they move (i.e., the kinematic properties of their reaches) depending on the: (1) direction they move, (2) hand they use, and (3) side of the body where the movement occurs. In the present work, we took a more detailed look at how kinematic properties of reaching movements performed in VR change as a function of movement direction for reaches performed on each side of the body using each hand. We focused on reaches in 12 different directions that either involved moving inward (toward the body midline) or outward (away from the body midline). Twenty users reached in each direction on both left and right sides of their body, using both their dominant and non-dominant hands. The results provided a fine-grained account of how kinematic properties of virtual hand reaches change as a function of movement direction when users reach on either side of their body using either hand. The findings provide practitioners insights on how to interpret the kinematic properties of reaching behaviors in VR, which has applicability in emerging contexts that include detecting VR usability issues and using VR for stroke rehabilitation.

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Using immersive virtual reality to remotely examine performance differences between dominant and non-dominant hands

2023

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Circle drawing may be a useful task to study upper-limb function in patient populations. However, previous studies rely on expensive and bulky robotics to measure performance. For clinics or hospitals with limited budgets and space, this may be unfeasible. Virtual reality (VR) provides a portable and low-cost tool with integrated motion capture. It offers potentially a more feasible medium by which to assess upper-limb motor function. Prior to use with patient populations, it is important to validate and test the capabilities of VR with healthy users. This study examined whether a VR-based circle drawing task, completed remotely using participant’s own devices, could capture differences between movement kinematics of the dominant and non-dominant hands in healthy individuals. Participants (n = 47) traced the outline of a circle presented on their VR head-mounted displays with each hand, while the positions of the hand-held controllers were continuously recorded. Although there were no differences observed in the size or roundness of circles drawn with each hand, consistent with prior literature our results did show that the circles drawn with the dominant hand were completed faster than those with the non-dominant hand. This provides preliminary evidence that a VR-based circle drawing task may be a feasible method for detecting subtle differences in function in clinical populations.

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Eye-Hand Coordination Training for Sports with Mid-air VR

Cited by 9 publications

References 62 publications

Proposed Eye-Hand Correlation Assessment System: A Novel Approach for Evaluating Coordination

Proposed Eye-Hand Correlation Assessment System: A Novel Approach for Evaluating Coordination

Reaching interactions in virtual reality: the effect of movement direction, hand dominance, and hemispace on the kinematic properties of inward and outward reaches

Using immersive virtual reality to remotely examine performance differences between dominant and non-dominant hands

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