Evaluation of Vibration Signals Measured by 3-Axis MEMS Accelerometer on Human Face using Wavelet Transform and Classifications
Various studies have been conducted to reveal and analyse tissues from humans with distinct properties. The interpretation of human facial tissues was the subject of a few of these investigations. The aim of this study was to look at the energy ratios of vibration signals recorded from the human fac...
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| Format: | Article |
| Language: | English |
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Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
2022-01-01
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| Series: | Tehnički Vjesnik |
| Subjects: | |
| Online Access: | https://hrcak.srce.hr/file/394980 |
| _version_ | 1797206799963652096 |
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| author | Okan Oral* Suleyman Bilgin Mehmet Umit Ak |
| author_facet | Okan Oral* Suleyman Bilgin Mehmet Umit Ak |
| author_sort | Okan Oral* |
| collection | DOAJ |
| description | Various studies have been conducted to reveal and analyse tissues from humans with distinct properties. The interpretation of human facial tissues was the subject of a few of these investigations. The aim of this study was to look at the energy ratios of vibration signals recorded from the human face using a 3-axis Micro-Electro-Mechanic System accelerometer sensor. 9 various measurement points on the faces of the subjects used to receive the signals are then analysed using frequency characteristics. During the analysis process, wavelet transformation values are estimated and evaluated. Thus, these regions' frequency ranges can be calculated. In addition, critical properties extracted from signals of vibration using wavelet packet transformation analysis were used as inputs of classification methods. In the next step, multilayer perceptual neural networks (MLPNN) were evaluated. In addition, the support vector machine (SVM), decision tree (DT) and binary convolution neural networks (CNN) methods were evaluated, and the success rates were compared. Finally, it is seen that the energy ratios of the signals in the hard regions are low and the energy ratios of the signals in the soft regions are high. And it has been observed that higher accuracy rate is achieved with binary CNN than with other methods. |
| first_indexed | 2024-04-24T09:12:46Z |
| format | Article |
| id | doaj.art-765daa806b95404486b4af8d662cbf2d |
| institution | Directory Open Access Journal |
| issn | 1330-3651 1848-6339 |
| language | English |
| last_indexed | 2024-04-24T09:12:46Z |
| publishDate | 2022-01-01 |
| publisher | Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek |
| record_format | Article |
| series | Tehnički Vjesnik |
| spelling | doaj.art-765daa806b95404486b4af8d662cbf2d2024-04-15T17:33:50ZengFaculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in OsijekTehnički Vjesnik1330-36511848-63392022-01-0129235536210.17559/TV-20210820150837Evaluation of Vibration Signals Measured by 3-Axis MEMS Accelerometer on Human Face using Wavelet Transform and ClassificationsOkan Oral*0Suleyman Bilgin1Mehmet Umit Ak2Akdeniz University, Department of Mechatronics Engineering, Engineering Faculty Dumlupinar Boulevard 07058 Campus Antalya TurkeyAkdeniz University, Department of Electric and Electronics Engineering, Engineering Faculty Dumlupinar Boulevard 07058 Campus Antalya TurkeyAkdeniz University, Department of Electric and Electronics Engineering, Engineering Faculty Dumlupinar Boulevard 07058 Campus Antalya TurkeyVarious studies have been conducted to reveal and analyse tissues from humans with distinct properties. The interpretation of human facial tissues was the subject of a few of these investigations. The aim of this study was to look at the energy ratios of vibration signals recorded from the human face using a 3-axis Micro-Electro-Mechanic System accelerometer sensor. 9 various measurement points on the faces of the subjects used to receive the signals are then analysed using frequency characteristics. During the analysis process, wavelet transformation values are estimated and evaluated. Thus, these regions' frequency ranges can be calculated. In addition, critical properties extracted from signals of vibration using wavelet packet transformation analysis were used as inputs of classification methods. In the next step, multilayer perceptual neural networks (MLPNN) were evaluated. In addition, the support vector machine (SVM), decision tree (DT) and binary convolution neural networks (CNN) methods were evaluated, and the success rates were compared. Finally, it is seen that the energy ratios of the signals in the hard regions are low and the energy ratios of the signals in the soft regions are high. And it has been observed that higher accuracy rate is achieved with binary CNN than with other methods.https://hrcak.srce.hr/file/394980Binary CNNMulti-layer perceptron neural networksVibration signalsWavelet packet transform |
| spellingShingle | Okan Oral* Suleyman Bilgin Mehmet Umit Ak Evaluation of Vibration Signals Measured by 3-Axis MEMS Accelerometer on Human Face using Wavelet Transform and Classifications Tehnički Vjesnik Binary CNN Multi-layer perceptron neural networks Vibration signals Wavelet packet transform |
| title | Evaluation of Vibration Signals Measured by 3-Axis MEMS Accelerometer on Human Face using Wavelet Transform and Classifications |
| title_full | Evaluation of Vibration Signals Measured by 3-Axis MEMS Accelerometer on Human Face using Wavelet Transform and Classifications |
| title_fullStr | Evaluation of Vibration Signals Measured by 3-Axis MEMS Accelerometer on Human Face using Wavelet Transform and Classifications |
| title_full_unstemmed | Evaluation of Vibration Signals Measured by 3-Axis MEMS Accelerometer on Human Face using Wavelet Transform and Classifications |
| title_short | Evaluation of Vibration Signals Measured by 3-Axis MEMS Accelerometer on Human Face using Wavelet Transform and Classifications |
| title_sort | evaluation of vibration signals measured by 3 axis mems accelerometer on human face using wavelet transform and classifications |
| topic | Binary CNN Multi-layer perceptron neural networks Vibration signals Wavelet packet transform |
| url | https://hrcak.srce.hr/file/394980 |
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