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Multiple-Wearable-Sensor-Based Gait Classification and Analysis in Patients with Neurological Disorders
The aim of this study was to conduct a comprehensive analysis of the placement of multiple wearable sensors for the purpose of analyzing and classifying the gaits of patients with neurological disorders. Seven inertial measurement unit (IMU) sensors were placed at seven locations: the lower back (L5...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6210399/ https://www.ncbi.nlm.nih.gov/pubmed/30314269 http://dx.doi.org/10.3390/s18103397 |
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author | Hsu, Wei-Chun Sugiarto, Tommy Lin, Yi-Jia Yang, Fu-Chi Lin, Zheng-Yi Sun, Chi-Tien Hsu, Chun-Lung Chou, Kuan-Nien |
author_facet | Hsu, Wei-Chun Sugiarto, Tommy Lin, Yi-Jia Yang, Fu-Chi Lin, Zheng-Yi Sun, Chi-Tien Hsu, Chun-Lung Chou, Kuan-Nien |
author_sort | Hsu, Wei-Chun |
collection | PubMed |
description | The aim of this study was to conduct a comprehensive analysis of the placement of multiple wearable sensors for the purpose of analyzing and classifying the gaits of patients with neurological disorders. Seven inertial measurement unit (IMU) sensors were placed at seven locations: the lower back (L5) and both sides of the thigh, distal tibia (shank), and foot. The 20 subjects selected to participate in this study were separated into two groups: stroke patients (11) and patients with neurological disorders other than stroke (brain concussion, spinal injury, or brain hemorrhage) (9). The temporal parameters of gait were calculated using a wearable device, and various features and sensor configurations were examined to establish the ideal accuracy for classifying different groups. A comparison of the various methods and features for classifying the three groups revealed that a combination of time domain and gait temporal feature-based classification with the Multilayer Perceptron (MLP) algorithm outperformed the other methods of feature-based classification. The classification results of different sensor placements revealed that the sensor placed on the shank achieved higher accuracy than the other sensor placements (L5, foot, and thigh). The placement-based classification of the shank sensor achieved 89.13% testing accuracy with the Decision Tree (DT) classifier algorithm. The results of this study indicate that the wearable IMU device is capable of differentiating between the gait patterns of healthy patients, patients with stroke, and patients with other neurological disorders. Moreover, the most favorable results were reported for the classification that used the combination of time domain and gait temporal features as the model input and the shank location for sensor placement. |
format | Online Article Text |
id | pubmed-6210399 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-62103992018-11-02 Multiple-Wearable-Sensor-Based Gait Classification and Analysis in Patients with Neurological Disorders Hsu, Wei-Chun Sugiarto, Tommy Lin, Yi-Jia Yang, Fu-Chi Lin, Zheng-Yi Sun, Chi-Tien Hsu, Chun-Lung Chou, Kuan-Nien Sensors (Basel) Article The aim of this study was to conduct a comprehensive analysis of the placement of multiple wearable sensors for the purpose of analyzing and classifying the gaits of patients with neurological disorders. Seven inertial measurement unit (IMU) sensors were placed at seven locations: the lower back (L5) and both sides of the thigh, distal tibia (shank), and foot. The 20 subjects selected to participate in this study were separated into two groups: stroke patients (11) and patients with neurological disorders other than stroke (brain concussion, spinal injury, or brain hemorrhage) (9). The temporal parameters of gait were calculated using a wearable device, and various features and sensor configurations were examined to establish the ideal accuracy for classifying different groups. A comparison of the various methods and features for classifying the three groups revealed that a combination of time domain and gait temporal feature-based classification with the Multilayer Perceptron (MLP) algorithm outperformed the other methods of feature-based classification. The classification results of different sensor placements revealed that the sensor placed on the shank achieved higher accuracy than the other sensor placements (L5, foot, and thigh). The placement-based classification of the shank sensor achieved 89.13% testing accuracy with the Decision Tree (DT) classifier algorithm. The results of this study indicate that the wearable IMU device is capable of differentiating between the gait patterns of healthy patients, patients with stroke, and patients with other neurological disorders. Moreover, the most favorable results were reported for the classification that used the combination of time domain and gait temporal features as the model input and the shank location for sensor placement. MDPI 2018-10-11 /pmc/articles/PMC6210399/ /pubmed/30314269 http://dx.doi.org/10.3390/s18103397 Text en © 2018 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 Hsu, Wei-Chun Sugiarto, Tommy Lin, Yi-Jia Yang, Fu-Chi Lin, Zheng-Yi Sun, Chi-Tien Hsu, Chun-Lung Chou, Kuan-Nien Multiple-Wearable-Sensor-Based Gait Classification and Analysis in Patients with Neurological Disorders |
title | Multiple-Wearable-Sensor-Based Gait Classification and Analysis in Patients with Neurological Disorders |
title_full | Multiple-Wearable-Sensor-Based Gait Classification and Analysis in Patients with Neurological Disorders |
title_fullStr | Multiple-Wearable-Sensor-Based Gait Classification and Analysis in Patients with Neurological Disorders |
title_full_unstemmed | Multiple-Wearable-Sensor-Based Gait Classification and Analysis in Patients with Neurological Disorders |
title_short | Multiple-Wearable-Sensor-Based Gait Classification and Analysis in Patients with Neurological Disorders |
title_sort | multiple-wearable-sensor-based gait classification and analysis in patients with neurological disorders |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6210399/ https://www.ncbi.nlm.nih.gov/pubmed/30314269 http://dx.doi.org/10.3390/s18103397 |
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