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A Lightweight Exoskeleton-Based Portable Gait Data Collection System †
For the controller of wearable lower-limb assistive devices, quantitative understanding of human locomotion serves as the basis for human motion intent recognition and joint-level motion control. Traditionally, the required gait data are obtained in gait research laboratories, utilizing marker-based...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7865931/ https://www.ncbi.nlm.nih.gov/pubmed/33498956 http://dx.doi.org/10.3390/s21030781 |
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author | Haque, Md Rejwanul Imtiaz, Masudul H. Kwak, Samuel T. Sazonov, Edward Chang, Young-Hui Shen, Xiangrong |
author_facet | Haque, Md Rejwanul Imtiaz, Masudul H. Kwak, Samuel T. Sazonov, Edward Chang, Young-Hui Shen, Xiangrong |
author_sort | Haque, Md Rejwanul |
collection | PubMed |
description | For the controller of wearable lower-limb assistive devices, quantitative understanding of human locomotion serves as the basis for human motion intent recognition and joint-level motion control. Traditionally, the required gait data are obtained in gait research laboratories, utilizing marker-based optical motion capture systems. Despite the high accuracy of measurement, marker-based systems are largely limited to laboratory environments, making it nearly impossible to collect the desired gait data in real-world daily-living scenarios. To address this problem, the authors propose a novel exoskeleton-based gait data collection system, which provides the capability of conducting independent measurement of lower limb movement without the need for stationary instrumentation. The basis of the system is a lightweight exoskeleton with articulated knee and ankle joints. To minimize the interference to a wearer’s natural lower-limb movement, a unique two-degrees-of-freedom joint design is incorporated, integrating a primary degree of freedom for joint motion measurement with a passive degree of freedom to allow natural joint movement and improve the comfort of use. In addition to the joint-embedded goniometers, the exoskeleton also features multiple positions for the mounting of inertia measurement units (IMUs) as well as foot-plate-embedded force sensing resistors to measure the foot plantar pressure. All sensor signals are routed to a microcontroller for data logging and storage. To validate the exoskeleton-provided joint angle measurement, a comparison study on three healthy participants was conducted, which involves locomotion experiments in various modes, including overground walking, treadmill walking, and sit-to-stand and stand-to-sit transitions. Joint angle trajectories measured with an eight-camera motion capture system served as the benchmark for comparison. Experimental results indicate that the exoskeleton-measured joint angle trajectories closely match those obtained through the optical motion capture system in all modes of locomotion (correlation coefficients of 0.97 and 0.96 for knee and ankle measurements, respectively), clearly demonstrating the accuracy and reliability of the proposed gait measurement system. |
format | Online Article Text |
id | pubmed-7865931 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-78659312021-02-07 A Lightweight Exoskeleton-Based Portable Gait Data Collection System † Haque, Md Rejwanul Imtiaz, Masudul H. Kwak, Samuel T. Sazonov, Edward Chang, Young-Hui Shen, Xiangrong Sensors (Basel) Article For the controller of wearable lower-limb assistive devices, quantitative understanding of human locomotion serves as the basis for human motion intent recognition and joint-level motion control. Traditionally, the required gait data are obtained in gait research laboratories, utilizing marker-based optical motion capture systems. Despite the high accuracy of measurement, marker-based systems are largely limited to laboratory environments, making it nearly impossible to collect the desired gait data in real-world daily-living scenarios. To address this problem, the authors propose a novel exoskeleton-based gait data collection system, which provides the capability of conducting independent measurement of lower limb movement without the need for stationary instrumentation. The basis of the system is a lightweight exoskeleton with articulated knee and ankle joints. To minimize the interference to a wearer’s natural lower-limb movement, a unique two-degrees-of-freedom joint design is incorporated, integrating a primary degree of freedom for joint motion measurement with a passive degree of freedom to allow natural joint movement and improve the comfort of use. In addition to the joint-embedded goniometers, the exoskeleton also features multiple positions for the mounting of inertia measurement units (IMUs) as well as foot-plate-embedded force sensing resistors to measure the foot plantar pressure. All sensor signals are routed to a microcontroller for data logging and storage. To validate the exoskeleton-provided joint angle measurement, a comparison study on three healthy participants was conducted, which involves locomotion experiments in various modes, including overground walking, treadmill walking, and sit-to-stand and stand-to-sit transitions. Joint angle trajectories measured with an eight-camera motion capture system served as the benchmark for comparison. Experimental results indicate that the exoskeleton-measured joint angle trajectories closely match those obtained through the optical motion capture system in all modes of locomotion (correlation coefficients of 0.97 and 0.96 for knee and ankle measurements, respectively), clearly demonstrating the accuracy and reliability of the proposed gait measurement system. MDPI 2021-01-24 /pmc/articles/PMC7865931/ /pubmed/33498956 http://dx.doi.org/10.3390/s21030781 Text en © 2021 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 Haque, Md Rejwanul Imtiaz, Masudul H. Kwak, Samuel T. Sazonov, Edward Chang, Young-Hui Shen, Xiangrong A Lightweight Exoskeleton-Based Portable Gait Data Collection System † |
title | A Lightweight Exoskeleton-Based Portable Gait Data Collection System † |
title_full | A Lightweight Exoskeleton-Based Portable Gait Data Collection System † |
title_fullStr | A Lightweight Exoskeleton-Based Portable Gait Data Collection System † |
title_full_unstemmed | A Lightweight Exoskeleton-Based Portable Gait Data Collection System † |
title_short | A Lightweight Exoskeleton-Based Portable Gait Data Collection System † |
title_sort | lightweight exoskeleton-based portable gait data collection system † |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7865931/ https://www.ncbi.nlm.nih.gov/pubmed/33498956 http://dx.doi.org/10.3390/s21030781 |
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