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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MDPI AG
2018-10-01
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| author | Wei-Chun Hsu Tommy Sugiarto Yi-Jia Lin Fu-Chi Yang Zheng-Yi Lin Chi-Tien Sun Chun-Lung Hsu Kuan-Nien Chou |
| author_facet | Wei-Chun Hsu Tommy Sugiarto Yi-Jia Lin Fu-Chi Yang Zheng-Yi Lin Chi-Tien Sun Chun-Lung Hsu Kuan-Nien Chou |
| author_sort | Wei-Chun Hsu |
| collection | DOAJ |
| 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 | Article |
| id | doaj-art-24d484d603e146888243d694affc49c5 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2018-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-24d484d603e146888243d694affc49c52025-01-30T15:18:01ZengMDPI AGSensors1424-82202018-10-011810339710.3390/s18103397s18103397Multiple-Wearable-Sensor-Based Gait Classification and Analysis in Patients with Neurological DisordersWei-Chun Hsu0Tommy Sugiarto1Yi-Jia Lin2Fu-Chi Yang3Zheng-Yi Lin4Chi-Tien Sun5Chun-Lung Hsu6Kuan-Nien Chou7Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei 10607, TaiwanGraduate Institute of Applied Science and Technology, National Taiwan University of Science and Technology, Taipei 10607, TaiwanGraduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei 10607, TaiwanDepartment of Neurology, Tri-Service General Hospital, National Defense Medical Center, Taipei 11490, TaiwanDepartment of Physical Medicine and Rehabilitation, Taipei City Hospital Zhongxing Branch, Datong District, Taipei 10341, TaiwanDivision of Embedded System and SoC Technology, System Integration and Application Department, Information and Communication Research Laboratory, Industrial Technology Research Institute, Hsinchu 31057, TaiwanDivision of Embedded System and SoC Technology, System Integration and Application Department, Information and Communication Research Laboratory, Industrial Technology Research Institute, Hsinchu 31057, TaiwanNeurosurgery Department, Tri-Service General Hospital, Taipei 11490, TaiwanThe 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.https://www.mdpi.com/1424-8220/18/10/3397wearable devicegait analysisIMU sensorsgait classificationstroke patientsneurological disorders |
| spellingShingle | Wei-Chun Hsu Tommy Sugiarto Yi-Jia Lin Fu-Chi Yang Zheng-Yi Lin Chi-Tien Sun Chun-Lung Hsu Kuan-Nien Chou Multiple-Wearable-Sensor-Based Gait Classification and Analysis in Patients with Neurological Disorders Sensors wearable device gait analysis IMU sensors gait classification stroke patients 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 | wearable device gait analysis IMU sensors gait classification stroke patients neurological disorders |
| url | https://www.mdpi.com/1424-8220/18/10/3397 |
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