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      Intensity- and Duration-Adaptive Functional Electrical Stimulation Using Fuzzy Logic Control and a Linear Model for Dropfoot Correction

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          Abstract

          Functional electrical stimulation (FES) is important in gait rehabilitation for patients with dropfoot. Since there are time-varying velocities during FES-assisted walking, it is difficult to achieve a good movement performance during walking. To account for the time-varying walking velocities, seven poststroke subjects were recruited and fuzzy logic control and a linear model were applied in FES-assisted walking to enable intensity- and duration-adaptive stimulation (IDAS) for poststroke subjects with dropfoot. In this study, the performance of IDAS was evaluated using kinematic data, and was compared with the performance under no stimulation (NS), FES-assisted walking triggered by heel-off stimulation (HOS), and speed-adaptive stimulation. A larger maximum ankle dorsiflexion angle in the IDAS condition than those in other conditions was observed. The ankle plantar flexion angle in the IDAS condition was similar to that of normal walking. Improvement in the maximum ankle dorsiflexion and plantar flexion angles in the IDAS condition could be attributed to having the appropriate stimulation intensity and duration. In summary, the intensity- and duration-adaptive controller can attain better movement performance and may have great potential in future clinical applications.

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          Most cited references38

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          Two simple methods for determining gait events during treadmill and overground walking using kinematic data.

          The determination of gait events such as heel strike and toe-off provide the basis for defining stance and swing phases of gait cycles. Two algorithms for determining event times for treadmill and overground walking based solely on kinematic data are presented. Kinematic data from treadmill walking trials lasting 20-45s were collected from three subject populations (healthy young, n=7; multiple sclerosis, n=7; stroke, n=4). Overground walking trials consisted of approximately eight successful passes over two force plates for a healthy subject population (n=5). Time of heel strike and toe-off were determined using the two new computational techniques and compared to events detected using vertical ground reaction force (GRF) as a gold standard. The two algorithms determined 94% of the treadmill events from healthy subjects within one frame (0.0167s) of the GRF events. In the impaired populations, 89% of treadmill events were within two frames (0.0334s) of the GRF events. For overground trials, 98% of events were within two frames. Automatic event detection from the two kinematic-based algorithms will aid researchers by accurately determining gait events during the analysis of treadmill and overground walking.
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            "Stops walking when talking" as a predictor of falls in elderly people.

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              The impact of physical therapy on functional outcomes after stroke: what's the evidence?

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                Author and article information

                Contributors
                Journal
                Front Neurol
                Front Neurol
                Front. Neurol.
                Frontiers in Neurology
                Frontiers Media S.A.
                1664-2295
                19 March 2018
                2018
                : 9
                : 165
                Affiliations
                [1] 1Key Laboratory of Sensing Technology and Biomedical Instrument of Guangdong Province, School of Engineering, Sun Yat-Sen University , Guangzhou, China
                [2] 2The Guangdong Work Injury Rehabilitation Center , Guangzhou, China
                Author notes

                Edited by: Valerie Moyra Pomeroy, University of East Anglia, United Kingdom

                Reviewed by: Andrew Kerr, University of Strathclyde, United Kingdom; Ugo Carraro, Università degli Studi di Padova, Italy

                *Correspondence: Rong Song, songrong@ 123456mail.sysu.edu.cn

                Specialty section: This article was submitted to Stroke, a section of the journal Frontiers in Neurology

                Article
                10.3389/fneur.2018.00165
                5868077
                29615963
                be8e3cdb-004c-45da-b173-48301f717a06
                Copyright © 2018 Chen, Shen, Zhuang, Wang and Song.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 15 January 2018
                : 05 March 2018
                Page count
                Figures: 5, Tables: 4, Equations: 0, References: 49, Pages: 8, Words: 6313
                Categories
                Neuroscience
                Original Research

                Neurology
                fuzzy logic control,linear model,dropfoot,functional electrical stimulation,treadmill
                Neurology
                fuzzy logic control, linear model, dropfoot, functional electrical stimulation, treadmill

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