Cargando…
A Locomotion Intent Prediction System Based on Multi-Sensor Fusion
Locomotion intent prediction is essential for the control of powered lower-limb prostheses to realize smooth locomotion transitions. In this research, we develop a multi-sensor fusion based locomotion intent prediction system, which can recognize current locomotion mode and detect locomotion transit...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2014
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4168424/ https://www.ncbi.nlm.nih.gov/pubmed/25014097 http://dx.doi.org/10.3390/s140712349 |
_version_ | 1782335539635027968 |
---|---|
author | Chen, Baojun Zheng, Enhao Wang, Qining |
author_facet | Chen, Baojun Zheng, Enhao Wang, Qining |
author_sort | Chen, Baojun |
collection | PubMed |
description | Locomotion intent prediction is essential for the control of powered lower-limb prostheses to realize smooth locomotion transitions. In this research, we develop a multi-sensor fusion based locomotion intent prediction system, which can recognize current locomotion mode and detect locomotion transitions in advance. Seven able-bodied subjects were recruited for this research. Signals from two foot pressure insoles and three inertial measurement units (one on the thigh, one on the shank and the other on the foot) are measured. A two-level recognition strategy is used for the recognition with linear discriminate classifier. Six kinds of locomotion modes and ten kinds of locomotion transitions are tested in this study. Recognition accuracy during steady locomotion periods (i.e., no locomotion transitions) is 99.71% ± 0.05% for seven able-bodied subjects. During locomotion transition periods, all the transitions are correctly detected and most of them can be detected before transiting to new locomotion modes. No significant deterioration in recognition performance is observed in the following five hours after the system is trained, and small number of experiment trials are required to train reliable classifiers. |
format | Online Article Text |
id | pubmed-4168424 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-41684242014-09-19 A Locomotion Intent Prediction System Based on Multi-Sensor Fusion Chen, Baojun Zheng, Enhao Wang, Qining Sensors (Basel) Article Locomotion intent prediction is essential for the control of powered lower-limb prostheses to realize smooth locomotion transitions. In this research, we develop a multi-sensor fusion based locomotion intent prediction system, which can recognize current locomotion mode and detect locomotion transitions in advance. Seven able-bodied subjects were recruited for this research. Signals from two foot pressure insoles and three inertial measurement units (one on the thigh, one on the shank and the other on the foot) are measured. A two-level recognition strategy is used for the recognition with linear discriminate classifier. Six kinds of locomotion modes and ten kinds of locomotion transitions are tested in this study. Recognition accuracy during steady locomotion periods (i.e., no locomotion transitions) is 99.71% ± 0.05% for seven able-bodied subjects. During locomotion transition periods, all the transitions are correctly detected and most of them can be detected before transiting to new locomotion modes. No significant deterioration in recognition performance is observed in the following five hours after the system is trained, and small number of experiment trials are required to train reliable classifiers. MDPI 2014-07-10 /pmc/articles/PMC4168424/ /pubmed/25014097 http://dx.doi.org/10.3390/s140712349 Text en © 2014 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 license (http://creativecommons.org/licenses/by/3.0/). |
spellingShingle | Article Chen, Baojun Zheng, Enhao Wang, Qining A Locomotion Intent Prediction System Based on Multi-Sensor Fusion |
title | A Locomotion Intent Prediction System Based on Multi-Sensor Fusion |
title_full | A Locomotion Intent Prediction System Based on Multi-Sensor Fusion |
title_fullStr | A Locomotion Intent Prediction System Based on Multi-Sensor Fusion |
title_full_unstemmed | A Locomotion Intent Prediction System Based on Multi-Sensor Fusion |
title_short | A Locomotion Intent Prediction System Based on Multi-Sensor Fusion |
title_sort | locomotion intent prediction system based on multi-sensor fusion |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4168424/ https://www.ncbi.nlm.nih.gov/pubmed/25014097 http://dx.doi.org/10.3390/s140712349 |
work_keys_str_mv | AT chenbaojun alocomotionintentpredictionsystembasedonmultisensorfusion AT zhengenhao alocomotionintentpredictionsystembasedonmultisensorfusion AT wangqining alocomotionintentpredictionsystembasedonmultisensorfusion AT chenbaojun locomotionintentpredictionsystembasedonmultisensorfusion AT zhengenhao locomotionintentpredictionsystembasedonmultisensorfusion AT wangqining locomotionintentpredictionsystembasedonmultisensorfusion |