2019
DOI: 10.3390/s19061288
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Robot-Assisted Eccentric Contraction Training of the Tibialis Anterior Muscle Based on Position and Force Sensing

Abstract: The purpose of this study was to determine the clinical effects of a training robot that induced eccentric tibialis anterior muscle contraction by controlling the strength and speed. The speed and the strength are controlled simultaneously by introducing robot training with two different feedbacks: velocity feedback in the robot controller and force bio-feedback based on force visualization. By performing quantitative eccentric contraction training, it is expected that the fall risk reduces owing to the improv… Show more

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Cited by 3 publications
(2 citation statements)
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References 25 publications
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“…The second paper, by Kubota et al [ 2 ], presents an evaluation of a lower-limb rehabilitation robot that induced eccentric tibialis anterior muscle contraction by controlling strength and speed using a combination of velocity and force feedback. In a long-term evaluation with 11 elderly participants, the authors found significant differences between training and control phases, though they did not find positive results in a cross-over test.…”
Section: Contributionsmentioning
confidence: 99%
“…The second paper, by Kubota et al [ 2 ], presents an evaluation of a lower-limb rehabilitation robot that induced eccentric tibialis anterior muscle contraction by controlling strength and speed using a combination of velocity and force feedback. In a long-term evaluation with 11 elderly participants, the authors found significant differences between training and control phases, though they did not find positive results in a cross-over test.…”
Section: Contributionsmentioning
confidence: 99%
“…Specifically, methods such as hidden Markov models [8], support vector machines [9], [10], feedforward neural networks [11], and recurrent neural networks [12] are currently being utilized. Ankle torque can be estimated from the measurement data of a training device [13], and from measurements by force sensors [14], [15]. Siu et al…”
Section: Introductionmentioning
confidence: 99%