What Is A Characteristic Of Augmented Reality? FutureUniverseTV Presents Practical Real Life Examples.
The use of augmented feedback has been shown to accelerate motor learning in previous studies. However, it remains unclear how specific feedback characteristics such as complexity and intermittency might influence the learning of a challenging, force-driven motor task. In this study, we examine how variations in the complexity and intermittency of augmented visual guidance affect the performance of an isometric muscle control task on a computerized platform. With the use of this new platform, upper-extremity function can be rehabilitated following neuromuscular dysfunction (e.g., spinal cord injury, stroke) by combining:
1) An arm brace that is position-adjustable;
2) An interface based on myoelectricity;
The use of virtual reality (VR) to train motor skills. This study reveals new principles of motor control and suggests how augmented guidance can be used to design VR motor rehabilitation programs that are highly flexible and customizable to meet the needs of each individual. As a result of this study, it was demonstrated that simpler and more intermittent feedback led to better performance (i.e., shorter computerized motion path lengths). Moreover, these feedback modes appear to reduce cognitive load (e.g., alpha/beta band magnitudes in electroencephalography) and increase physical arousal (e.g., higher skin conductance). Overall, this study indicates that augmented guidance must be presented selectively for complex, force-driven tasks in order to accelerate performance gains. In this study, it is suggested that providing simple and intermittent feedback to users allows them to avoid cognitive overload while encouraging physical engagement that contributes to better performance.
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In previous studies, augmented sensory feedback has been evaluated for technology-enhanced motor rehabilitation platforms, concentrating on upper-extremity function. In rehabilitation applications, however, it remains difficult to identify and apply specific feedback characteristics (e.g., type, timing, frequency, nature) in order to precipitate the desired motor learning outcomes.
Augmented visual guidance can facilitate improved functional performance of a muscle-based (myoelectric command) training task for upper-extremity rehabilitation. To perform reach-to-touch tasks while receiving augmented visual guidance during training, we utilize a novel computerized platform that includes myoelectric control of a virtual robot avatar.
It utilizes a position-adjustable upper extremity brace to assist users, such as those with spinal cord injuries who are unable to move their limbs against gravity. The brace supports resistance strength and coordination training at varying arm positions by holding the arm isometrically. As a result, we are examining the effects of variation in augmented guidance for the performance of a force-driven rehabilitation task.
Concurrent feedback is examined, which is important and novel since previous bandwidth investigations have used terminal feedback. During concurrent feedback, performance feedback is provided while the movement is in progress, whereas terminal feedback is provided after the movement has been completed.
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