This paper presents a sensor fusion strategy to estimate the absolute position and to identify the gait events of an active ankle-foot orthosis (AAFO). Sensor fusion is implemented using a Kalman filter with the signals of a gyroscope and an accelerometer, both present in an inertial measurement unit (IMU), besides the signals of three force sensitive resistors (FSRs) mounted in a sole of shoe. For correcting the gyroscope-based position, the redundant accelerometer-based position must be used only under reliable conditions, which are obtained by a proposed switching logic to enable the Kalman filter. Experimental results of the AAFO confirm the efficiency of the proposed sensor fusion strategy.

Категория: Ищем научные статьи | 4 комментария »

Lifting patients is a demanding task for care providers. In addition, the number of patients is rising and the number of people with obesity is expanding. Current lifting aids are single purpose and time consuming to use. Exoskeletons can fulfill the demand for a versatile and easy to use lifting aid. A drawback of a typical exoskeleton is that in order to function correctly the axes need to be aligned to the human joints, which is time consuming. Furthermore, an exoskeleton for healthcare must reduce the reaction forces in the user while lifting. It is chosen to design an exoskeleton, which requires no adjustment and can cope with power enhancement. The goal of this paper is to design an exoske-leton that is fast, easy to use and reduces the reaction forces in the user. A design is proposed which easily fits different sized users. In addition the reaction forces in the human skeleton are eliminated. The model is tested by means of a demonstrator. Elastic tension elements are used as a gravity compensator. By reducing the potential energy fluctuation the required external input is reduced. Optimizing a number of design parameters leads to a calculated moment reduction of 98.8% and moment fluctuation reduction of 96.8%. This is the first exoskeleton to combine fast to use design with high-energy efficiency gravity compensation.

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Категория: Научные статьи | Нет комментариев »

Austin Whitney является испытателем экзоскелета Austin, который собственно и назван в его честь. Кроме того он является известным мотивационным оратором, пропагандирующим о вреде пьянства за рулем.Здесь приводятся его контактные данные.

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Категория: Austin | Нет комментариев »

Контактные данные профессора H. Kazerooni разработчика таких проектов как Austin.HULC.BLEEX.ExoClimber.eLEGS.ExoHiker

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Категория: Austin, BLEEX, eLEGS, ExoClimber, ExoHiker, HULC, Human Assisted Walking Machine | Нет комментариев »

Patients with damage to the cerebellum make reaching movements that are uncoordinated, or ataxic — movements can be misdirected, over- or under-shoot targets, and more variable than the movements of healthy people. In addition, it is thought that cerebellar patients cannot adapt to (learn) new dynamics. This dissertation explores models of cerebellar ataxia and the use of robotic tools to improve human motor performance. We began by developing a dynamic model of the human arm and a robot exoskeleton. This model was populated with parameter values obtained through direct measurement, system identification, and use of Dempster’s anthropometric table [1]. This system was then used to conduct three studies.

In the first study, we used an exoskeleton robot to record the movements of cerebellar patients performing a targeted reaching task. These data and our dynamic model were used to test hypotheses proposed in the literature about the role of the cerebellum. We found evidence supporting the general hypothesis that the cerebellum functions as an internal model for planning movements and that damage to the cerebellum results in a biased (inaccurate) model, although the model does not completely explain cerebellar behavior. Computer simulations found optimal, patient-specific perturbations to actual limb dynamics that are predicted to reduce root-mean-squared directional reaching errors by an average of 41%.

In the second study, we used the same robot to implement a change in dynamics predicted to assist each patient. This required a controller design, the addition of accelerometers to the robot, and characterization of the robot’s ability to render acceleration-dependent forces. We found that this approach may improve the reaching of some cerebellar patients and not for others, and there was no evidence of motor learning.

In the third study, we investigated how a different assistive controller can be used to improve reaching behavior. We used the robot to render force channels to constrain arm movements during reaching, and explored how the ordering of null (robot passive) and channel (robot active) trial blocks affected motor learning. We found that force channels resulted in significant improvements in reaching performance in the directions both parallel and orthogonal to the force channel. However, we found no evidence that reaching practice in these channels results in improved reaching performance after the channels are removed.

The use of tools from the field of robotics allows precise measurements and interventions to understand and treat human motor deficits. We believe that model-based and patient-specific robot assistance and rehabilitation paradigms will lead to an increased understanding of the brain and improved patient outcomes.

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