Virtual reality simulators seek to immerse users in realistic interactive environments. However, at present, while several provide kinesthetic feedback, most lack tactile feedback. Current means of tactile feedback do not generate enough force to the digits, deliver a non-intuitive sense of feedback and are too large or heavy to be used in hand-worn configurations. The tactile feedback system developed herein uses electroactive polymers to create light touch and vibratory sensation to the fingertips, and DC motors to constrict the distal digit. In essence, when current is passed through the electroactive polymer in the shape of a cantilever (7 mm long by 19 mm wide), it bends on its long axis, providing a forces of 25 mN. Vibratory feedback is created by varying input voltage with a sinusoidal waveform. To generate fingertip constriction, two DC motors cinch a wire attached to a rubber thimble. These hardware components are controlled by a computer running X3D software, an ISO standard for representing 3D graphics, which affords a virtual environment for the tracking of one’s hand. Upon contact with a virtual object, the actuators generate prescribed forces or vibrations. With this setup, a series of human-subjects experiments will be conducted whereby the task is to contact and differentiate virtual spheres of differing stiffness. Experiment 1 will test the electroactive polymers to determine the threshold for recognizing light touch, Experiment 2 will test vibrational discrimination, and Experiment 3 will test the ability of the user to differentiate constriction forces.

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The Intuitive Leg Assist Device (I-LAD) is designed to address a demographic of people with lower extremity weakness. It optimizes cost, weight, and functionality, and provides the power and support needed to perform natural ambulation. The design mimics the human gait cycle by coupling the actuation of the knee and hip via a dual four-bar linkage system modified from a crank rocker system, with a 12.8% and 14.1% error in the hip and knee movement, respectively. The patient supports the device using an over the shoulder harness and modified work belt, both connected to an acrylic plate. The plate serves as the mounting for the motor, planetary gear system, and electrical components that drive the linkage system. The linkage system is connected to a knee brace and ankle foot orthosis that minimizes the effect of foot drop. The design implements feedback and safety by incorporating four sensor inputs-two force sensors beneath the feet and two goniometers on the hips-and a microprocessor, which controls motor actuation. The program logic ensures that the device shuts down upon unintended user movement, minimizing safety hazards.

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In recent years, gait robots have become increasingly common for gait rehabilitation in non-ambulatory stroke patients. Cardiovascular treadmill training, which has been shown to provide great benefit to stroke survivors, cannot be performed with non-ambulatory patients. We there-fore integrated cardiovascular training in robot-assisted gait therapy to combine the benefits of both training modi. We developed a model of human heart rate as a function of exercise parameters during robot-assisted gait training and applied it for automatic control purposes. This structural model of the physio-logical processes describes the change in heart rate caused by treadmill speed and the power exchanged bet-ween robot and subject. We performed physiological parameter estimation for each tested individual and de-signed a model-based feedback controller to guide heart rate to a desired time profile. Five healthy sub-jects and eight stroke patients were recorded for model parameter identification, which was successfully used for heart rate control of three healthy subjects.We showed that a model-based control approach can take into account patient-specific limitations of treadmill speed as well as individual power expenditure.

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A brain–computer interface (BCI) provides a non-muscular communication channel to people with and without disabilities. BCI devices consist of hardware and software. BCI hardware records signals from the brain, either invasively or non-invasively, using a series of device components. BCI software then translates these signals into device output commands and provides feedback. One may categorize different types of BCI applications into the following four categories: basic research, clinical/translational research, consumer products, and emerging applications. These four categories use BCI hardware and software, but have different sets of requirements. For example, while basic research needs to explore a wide range of system configurations, and thus requires a wide range of hardware and software capabilities, applications in the other three categories may be designed for relatively narrow purposes and thus may only need a very limited subset of capabilities. This paper summarizes technical aspects for each of these four categories of BCI applications. The results indicate that BCI technology is in transition from isolated demonstrations to systematic research and commercial development. This process requires several multidisciplinary efforts, including the development of better integrated and more robust BCI hardware and software, the definition of standardized interfaces, and the development of certification, dissemination and reimbursement procedures.

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We propose a novel method for movement assistance that is based on adaptive oscillators, i.e., mathematical tools that are capable of extracting the high-level features (amplitude, frequency, and offset) of a periodic signal. Such an oscillator acts like a filter on these features, but keeps its output in phase with respect to the input signal. Using a simple inverse model, we predicted the torque produced by human participants during rhythmic flexion extension of the elbow. Feeding back a fraction of this estimated torque to the participant through an elbow exoskeleton, we were able to prove the assistance efficiency through a marked decrease of the biceps and triceps electromyography. Importantly, since the oscillator adapted to the movement imposed by the user, the method flexibly allowed us to change the movement pattern and was still efficient during the nonstationary epochs. This method holds promise for the development of new robot-assisted rehabilitation protocols because it does not require prespecifying a reference trajectory and does not require complex signal sensing or single-user calibration: the only signal that is measured is the position of the augmented joint. In this paper, we further demonstrate that this assistance was very intuitive for the participants who adapted almost instantaneously.

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