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Implementation of CNN Model for Classification of Sitting Posture Based on Multiple Pressure Distribution  

Seo, Ji-Yun (Department of Computer Engineering, Dongseo University)
Noh, Yun-Hong (Department of Computer Engineering, Busan Digital University)
Jeong, Do-Un (Department of Computer Engineering, Dongseo University)
Publication Information
Journal of the Institute of Convergence Signal Processing / v.21, no.2, 2020 , pp. 73-78 More about this Journal
Abstract
Musculoskeletal disease is often caused by sitting down for long period's time or by bad posture habits. In order to prevent musculoskeletal disease in daily life, it is the most important to correct the bad sitting posture to the right one through real-time monitoring. In this study, to detect the sitting information of user's without any constraints, we propose posture measurement system based on multi-channel pressure sensor and CNN model for classifying sitting posture types. The proposed CNN model can analyze 5 types of sitting postures based on sitting posture information. For the performance assessment of posture classification CNN model through field test, the accuracy, recall, precision, and F1 of the classification results were checked with 10 subjects. As the experiment results, 99.84% of accuracy, 99.6% of recall, 99.6% of precision, and 99.6% of F1 were verified.
Keywords
Musculoskeletal disease; Posture correction; Pressure distribution; Real-time monitoring; CNN;
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Times Cited By KSCI : 8  (Citation Analysis)
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