Nonadditive Entropy Application to Detrended Force Sensor Data to Indicate Balance Disorder of Patients with Vestibular System Dysfunction
The healthy function of the vestibular system (VS) is of vital importance for individuals to carry out their daily activities independently and safely. This study carries out Tsallis entropy (TE)-based analysis on insole force sensor data in order to extract features to differentiate between healthy...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Entropy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1099-4300/25/10/1385 |
| _version_ | 1797573942651650048 |
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| author | Harun Yaşar Köse Serhat İkizoğlu |
| author_facet | Harun Yaşar Köse Serhat İkizoğlu |
| author_sort | Harun Yaşar Köse |
| collection | DOAJ |
| description | The healthy function of the vestibular system (VS) is of vital importance for individuals to carry out their daily activities independently and safely. This study carries out Tsallis entropy (TE)-based analysis on insole force sensor data in order to extract features to differentiate between healthy and VS-diseased individuals. Using a specifically developed algorithm, we detrend the acquired data to examine the fluctuation around the trend curve in order to consider the individual’s walking habit and thus increase the accuracy in diagnosis. It is observed that the TE value increases for diseased people as an indicator of the problem of maintaining balance. As one of the main contributions of this study, in contrast to studies in the literature that focus on gait dynamics requiring extensive walking time, we directly process the instantaneous pressure values, enabling a significant reduction in the data acquisition period. The extracted feature set is then inputted into fundamental classification algorithms, with support vector machine (SVM) demonstrating the highest performance, achieving an average accuracy of 95%. This study constitutes a significant step in a larger project aiming to identify the specific VS disease together with its stage. The performance achieved in this study provides a strong motivation to further explore this topic. |
| first_indexed | 2024-03-10T21:16:14Z |
| format | Article |
| id | doaj.art-014e9054f4024a0f97b6c0e6223e6d2e |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T21:16:14Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-014e9054f4024a0f97b6c0e6223e6d2e2023-11-19T16:24:08ZengMDPI AGEntropy1099-43002023-09-012510138510.3390/e25101385Nonadditive Entropy Application to Detrended Force Sensor Data to Indicate Balance Disorder of Patients with Vestibular System DysfunctionHarun Yaşar Köse0Serhat İkizoğlu1Department of Mechatronics Engineering, Faculty of Electric and Electronics, Istanbul Technical University (ITU), 34469 Istanbul, TürkiyeDepartment of Control and Automation Engineering, Faculty of Electric and Electronics, Istanbul Technical University (ITU), 34469 Istanbul, TürkiyeThe healthy function of the vestibular system (VS) is of vital importance for individuals to carry out their daily activities independently and safely. This study carries out Tsallis entropy (TE)-based analysis on insole force sensor data in order to extract features to differentiate between healthy and VS-diseased individuals. Using a specifically developed algorithm, we detrend the acquired data to examine the fluctuation around the trend curve in order to consider the individual’s walking habit and thus increase the accuracy in diagnosis. It is observed that the TE value increases for diseased people as an indicator of the problem of maintaining balance. As one of the main contributions of this study, in contrast to studies in the literature that focus on gait dynamics requiring extensive walking time, we directly process the instantaneous pressure values, enabling a significant reduction in the data acquisition period. The extracted feature set is then inputted into fundamental classification algorithms, with support vector machine (SVM) demonstrating the highest performance, achieving an average accuracy of 95%. This study constitutes a significant step in a larger project aiming to identify the specific VS disease together with its stage. The performance achieved in this study provides a strong motivation to further explore this topic.https://www.mdpi.com/1099-4300/25/10/1385vestibular disordersinsole force sensorsgait analysisTsallis entropydetrendingfeature extraction |
| spellingShingle | Harun Yaşar Köse Serhat İkizoğlu Nonadditive Entropy Application to Detrended Force Sensor Data to Indicate Balance Disorder of Patients with Vestibular System Dysfunction Entropy vestibular disorders insole force sensors gait analysis Tsallis entropy detrending feature extraction |
| title | Nonadditive Entropy Application to Detrended Force Sensor Data to Indicate Balance Disorder of Patients with Vestibular System Dysfunction |
| title_full | Nonadditive Entropy Application to Detrended Force Sensor Data to Indicate Balance Disorder of Patients with Vestibular System Dysfunction |
| title_fullStr | Nonadditive Entropy Application to Detrended Force Sensor Data to Indicate Balance Disorder of Patients with Vestibular System Dysfunction |
| title_full_unstemmed | Nonadditive Entropy Application to Detrended Force Sensor Data to Indicate Balance Disorder of Patients with Vestibular System Dysfunction |
| title_short | Nonadditive Entropy Application to Detrended Force Sensor Data to Indicate Balance Disorder of Patients with Vestibular System Dysfunction |
| title_sort | nonadditive entropy application to detrended force sensor data to indicate balance disorder of patients with vestibular system dysfunction |
| topic | vestibular disorders insole force sensors gait analysis Tsallis entropy detrending feature extraction |
| url | https://www.mdpi.com/1099-4300/25/10/1385 |
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