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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...

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Autores principales: Hsu, Wei-Chun, Sugiarto, Tommy, Lin, Yi-Jia, Yang, Fu-Chi, Lin, Zheng-Yi, Sun, Chi-Tien, Hsu, Chun-Lung, Chou, Kuan-Nien
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
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.
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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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