910 research outputs found

    Haptic Augmented Reality to monitor human arm's stiffness in rehabilitation

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    Augmented Reality (AR) is a live, direct or indirect, view of a physical, real-world environment whose elements are overlaid by virtual, computer generated objects. In this paper, AR is combined with haptics in order to observe human arm's stiffness. A haptic, hand-held device is used to measure the human arm's impedance. While a computer vision system tracks and records the position of the hand, a computer screen displays the impedance diagrams superimposed on the hand in a real-time video feed. The visual augmentation is also performed using a video projector that project's the diagrams on the hand as it moves. © 2012 IEEE

    An assistive tabletop keyboard for stroke rehabilitation

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    We propose a tabletop keyboard that assists stroke patients in using computers. Using computers for purposes such as paying bills, managing bank accounts, sending emails, etc., which all include typing, is part of Activities of Daily Living (ADL) that stroke patients wish to recover. To date, stroke rehabilitation research has greatly focused on using computer-assisted technology for rehabilitation. However, working with computers as a skill that patients need to recover has been neglected. The conventional human computer interfaces are mouse and keyboard. Using keyboard stays the main challenge for hemiplegic stroke patients because typing is usually a bimanual task. Therefore, we propose an assistive tabletop keyboard which is not only a novel computer interface that is specially designed to facilitate patient-computer interaction but also a rehab medium through which patients practice the desired arm/hand functions. © 2013 Authors

    Real-time computer modeling of weakness following stroke optimizes robotic assistance for movement therapy

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    This paper describes the development of a novel control system for a robotic arm orthosis for assisting patients in motor training following stroke. The robot allows naturalistic motion of the arm and is as mechanically compliant as a human therapist's arms. This compliance preserves the connection between effort and error that appears essential for motor learning, but presents a challenge: accurately creating desired movements requires that the robot form a model of the patient's weakness, since the robot cannot simply stiffly drive the arm along the desired path. We show here that a standard model-based adaptive controller allows the robot to form such a model of the patient and complete movements accurately. However, we found that the human motor system, when coupled to such an adaptive controller, reduces its own participation, allowing the adaptive controller to take over the performance of the task. This presents a problem for motor training, since active engagement by the patient is important for stimulating neuroplasticity. We show that this problem can be solved by making the controller continuously attempt to reduce its assistance when errors are small. The resulting robot successfully assists stroke patients in moving in desired patterns with very small errors, but also encourages intense participation by the patient. Such robot assistance may optimally provoke neural plasticity, since it intensely engages both descending and ascending motor pathways. © 2007 IEEE
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