Classification of subjects with balance disorders using 1D-CNN and inertial sensors
The article presents the concept of detecting subjects with balance disorders by the use of machine learning techniques. The proposed solution has been developed and tested based on a group of 40 subjects, the group included both patients with uncompensated dysfunction in the vestibular system and h...
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| Published in | IEEE access Vol. 10; p. 1 |
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| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Piscataway
IEEE
01.01.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2169-3536 2169-3536 |
| DOI | 10.1109/ACCESS.2022.3225521 |
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| Abstract | The article presents the concept of detecting subjects with balance disorders by the use of machine learning techniques. The proposed solution has been developed and tested based on a group of 40 subjects, the group included both patients with uncompensated dysfunction in the vestibular system and healthy volunteers. Presence of dysfunction was verified prior to the study by detailed clinical examination. The data for the study were collected with the use of miniature micromachine sensors, mounted on the body at selected locations. The task performed by the subjects consisted of free gait over a distance of three meters; the task was selected to make it easy to perform in any surroundings and not requiring additional equipment. The collected data was used as an input to an artificial neural network based on a one-dimensional convolution kernel. The proposed solution allows to classify subjects into healthy and non-healthy with an accuracy of 87.5%. |
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| AbstractList | The article presents the concept of detecting subjects with balance disorders by the use of machine learning techniques. The proposed solution has been developed and tested based on a group of 40 subjects, the group included both patients with uncompensated dysfunction in the vestibular system and healthy volunteers. Presence of dysfunction was verified prior to the study by detailed clinical examination. The data for the study were collected with the use of miniature micromachine sensors, mounted on the body at selected locations. The task performed by the subjects consisted of free gait over a distance of three meters; the task was selected to make it easy to perform in any surroundings and not requiring additional equipment. The collected data was used as an input to an artificial neural network based on a one-dimensional convolution kernel. The proposed solution allows to classify subjects into healthy and non-healthy with an accuracy of 87.5%. |
| Author | Zamyslowska-Szmytke, Ewa Marciniak, Pawel Napieralski, Jan Kotas, Rafal Kaminski, Marek Janc, Magdalena Tylman, Wojciech Jozefowicz-Korczynska, Magdalena |
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| SubjectTerms | Artificial neural networks balance disorder Biological neural networks CNN Costs Data collection Disorders fall risk Hardware Inertial sensing devices Machine learning Sensors Support vector machines Task analysis |
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| Title | Classification of subjects with balance disorders using 1D-CNN and inertial sensors |
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