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Feasibility of a Sensor-Based Gait Event Detection Algorithm for Triggering Functional Electrical Stimulation during Robot-Assisted Gait Training

Technologies such as robot-assisted gait trainers or functional electrical stimulation can improve the rehabilitation process of people affected with gait disorders due to stroke or other neurological defects. By combining both technologies, the potential disadvantages of each technology could be co...

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Autores principales: Schicketmueller, Andreas, Rose, Georg, Hofmann, Marc
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6864796/
https://www.ncbi.nlm.nih.gov/pubmed/31694188
http://dx.doi.org/10.3390/s19214804
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author Schicketmueller, Andreas
Rose, Georg
Hofmann, Marc
author_facet Schicketmueller, Andreas
Rose, Georg
Hofmann, Marc
author_sort Schicketmueller, Andreas
collection PubMed
description Technologies such as robot-assisted gait trainers or functional electrical stimulation can improve the rehabilitation process of people affected with gait disorders due to stroke or other neurological defects. By combining both technologies, the potential disadvantages of each technology could be compensated and simultaneously, therapy effects could be improved. Thus, an algorithm was designed that aims to detect the gait cycle of a robot-assisted gait trainer. Based on movement data recorded with inertial measurement units, gait events can be detected. These events can further be used to trigger functional electrical stimulation. This novel setup offers the possibility of equipping a broad range of potential robot-assisted gait trainers with functional electrical stimulation. The aim of this paper in particular was to test the feasibility of a system using inertial measurement units for gait event detection during robot-assisted gait training. Thus, a 39-year-old healthy male adult executed a total of six training sessions with two robot-assisted gait trainers (Lokomat and Lyra). The measured data from the sensors were analyzed by a custom-made gait event detection algorithm. An overall detection rate of 98.1% ± 5.2% for the Lokomat and 94.1% ± 6.8% for the Lyra was achieved. The mean type-1 error was 0.3% ± 1.2% for the Lokomat and 1.9% ± 4.3% for the Lyra. As a result, the setup provides promising results for further research and a technique that can enhance robot-assisted gait trainers by adding functional electrical stimulation to the rehabilitation process.
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spelling pubmed-68647962019-12-06 Feasibility of a Sensor-Based Gait Event Detection Algorithm for Triggering Functional Electrical Stimulation during Robot-Assisted Gait Training Schicketmueller, Andreas Rose, Georg Hofmann, Marc Sensors (Basel) Article Technologies such as robot-assisted gait trainers or functional electrical stimulation can improve the rehabilitation process of people affected with gait disorders due to stroke or other neurological defects. By combining both technologies, the potential disadvantages of each technology could be compensated and simultaneously, therapy effects could be improved. Thus, an algorithm was designed that aims to detect the gait cycle of a robot-assisted gait trainer. Based on movement data recorded with inertial measurement units, gait events can be detected. These events can further be used to trigger functional electrical stimulation. This novel setup offers the possibility of equipping a broad range of potential robot-assisted gait trainers with functional electrical stimulation. The aim of this paper in particular was to test the feasibility of a system using inertial measurement units for gait event detection during robot-assisted gait training. Thus, a 39-year-old healthy male adult executed a total of six training sessions with two robot-assisted gait trainers (Lokomat and Lyra). The measured data from the sensors were analyzed by a custom-made gait event detection algorithm. An overall detection rate of 98.1% ± 5.2% for the Lokomat and 94.1% ± 6.8% for the Lyra was achieved. The mean type-1 error was 0.3% ± 1.2% for the Lokomat and 1.9% ± 4.3% for the Lyra. As a result, the setup provides promising results for further research and a technique that can enhance robot-assisted gait trainers by adding functional electrical stimulation to the rehabilitation process. MDPI 2019-11-05 /pmc/articles/PMC6864796/ /pubmed/31694188 http://dx.doi.org/10.3390/s19214804 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Schicketmueller, Andreas
Rose, Georg
Hofmann, Marc
Feasibility of a Sensor-Based Gait Event Detection Algorithm for Triggering Functional Electrical Stimulation during Robot-Assisted Gait Training
title Feasibility of a Sensor-Based Gait Event Detection Algorithm for Triggering Functional Electrical Stimulation during Robot-Assisted Gait Training
title_full Feasibility of a Sensor-Based Gait Event Detection Algorithm for Triggering Functional Electrical Stimulation during Robot-Assisted Gait Training
title_fullStr Feasibility of a Sensor-Based Gait Event Detection Algorithm for Triggering Functional Electrical Stimulation during Robot-Assisted Gait Training
title_full_unstemmed Feasibility of a Sensor-Based Gait Event Detection Algorithm for Triggering Functional Electrical Stimulation during Robot-Assisted Gait Training
title_short Feasibility of a Sensor-Based Gait Event Detection Algorithm for Triggering Functional Electrical Stimulation during Robot-Assisted Gait Training
title_sort feasibility of a sensor-based gait event detection algorithm for triggering functional electrical stimulation during robot-assisted gait training
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6864796/
https://www.ncbi.nlm.nih.gov/pubmed/31694188
http://dx.doi.org/10.3390/s19214804
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