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Subject- and Environment-Based Sensor Variability for Wearable Lower-Limb Assistive Devices
Significant research effort has gone towards the development of powered lower limb prostheses that control power during gait. These devices use forward prediction based on electromyography (EMG), kinetics and kinematics to command the prosthesis which locomotion activity is desired. Unfortunately th...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6891559/ https://www.ncbi.nlm.nih.gov/pubmed/31717471 http://dx.doi.org/10.3390/s19224887 |
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author | Krausz, Nili E. Hu, Blair H. Hargrove, Levi J. |
author_facet | Krausz, Nili E. Hu, Blair H. Hargrove, Levi J. |
author_sort | Krausz, Nili E. |
collection | PubMed |
description | Significant research effort has gone towards the development of powered lower limb prostheses that control power during gait. These devices use forward prediction based on electromyography (EMG), kinetics and kinematics to command the prosthesis which locomotion activity is desired. Unfortunately these predictions can have substantial errors, which can potentially lead to trips or falls. It is hypothesized that one reason for the significant prediction errors in the current control systems for powered lower-limb prostheses is due to the inter- and intra-subject variability of the data sources used for prediction. Environmental data, recorded from a depth sensor worn on a belt, should have less variability across trials and subjects as compared to kinetics, kinematics and EMG data, and thus its addition is proposed. The variability of each data source was analyzed, once normalized, to determine the intra-activity and intra-subject variability for each sensor modality. Then measures of separability, repeatability, clustering and overall desirability were computed. Results showed that combining Vision, EMG, IMU (inertial measurement unit), and Goniometer features yielded the best separability, repeatability, clustering and desirability across subjects and activities. This will likely be useful for future application in a forward predictor, which will incorporate Vision-based environmental data into a forward predictor for powered lower-limb prosthesis and exoskeletons. |
format | Online Article Text |
id | pubmed-6891559 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-68915592019-12-18 Subject- and Environment-Based Sensor Variability for Wearable Lower-Limb Assistive Devices Krausz, Nili E. Hu, Blair H. Hargrove, Levi J. Sensors (Basel) Article Significant research effort has gone towards the development of powered lower limb prostheses that control power during gait. These devices use forward prediction based on electromyography (EMG), kinetics and kinematics to command the prosthesis which locomotion activity is desired. Unfortunately these predictions can have substantial errors, which can potentially lead to trips or falls. It is hypothesized that one reason for the significant prediction errors in the current control systems for powered lower-limb prostheses is due to the inter- and intra-subject variability of the data sources used for prediction. Environmental data, recorded from a depth sensor worn on a belt, should have less variability across trials and subjects as compared to kinetics, kinematics and EMG data, and thus its addition is proposed. The variability of each data source was analyzed, once normalized, to determine the intra-activity and intra-subject variability for each sensor modality. Then measures of separability, repeatability, clustering and overall desirability were computed. Results showed that combining Vision, EMG, IMU (inertial measurement unit), and Goniometer features yielded the best separability, repeatability, clustering and desirability across subjects and activities. This will likely be useful for future application in a forward predictor, which will incorporate Vision-based environmental data into a forward predictor for powered lower-limb prosthesis and exoskeletons. MDPI 2019-11-08 /pmc/articles/PMC6891559/ /pubmed/31717471 http://dx.doi.org/10.3390/s19224887 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 Krausz, Nili E. Hu, Blair H. Hargrove, Levi J. Subject- and Environment-Based Sensor Variability for Wearable Lower-Limb Assistive Devices |
title | Subject- and Environment-Based Sensor Variability for Wearable Lower-Limb Assistive Devices |
title_full | Subject- and Environment-Based Sensor Variability for Wearable Lower-Limb Assistive Devices |
title_fullStr | Subject- and Environment-Based Sensor Variability for Wearable Lower-Limb Assistive Devices |
title_full_unstemmed | Subject- and Environment-Based Sensor Variability for Wearable Lower-Limb Assistive Devices |
title_short | Subject- and Environment-Based Sensor Variability for Wearable Lower-Limb Assistive Devices |
title_sort | subject- and environment-based sensor variability for wearable lower-limb assistive devices |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6891559/ https://www.ncbi.nlm.nih.gov/pubmed/31717471 http://dx.doi.org/10.3390/s19224887 |
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