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Design and development of an intelligent real-time pressure sensing system for sitting posture monitoring

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  • Ang Qizheng

  • Lim Way Soong

  • Yeo Boon Chin

  • Petch Jearanaisilawong

Abstract

Poor sitting posture is a common issue that can lead to musculoskeletal disorders and long-term health complications, especially with the rise in sedentary work. This study aims to design and develop an intelligent, real-time pressure sensing system to monitor and classify sitting posture accurately. The system uses Velostat-based pressure mats positioned on a seat and backrest, connected to an ESP32 microcontroller, to collect real-time data. A support vector machine (SVM) model processes this data to classify ten distinct postures. A Bluetooth interface transmits data to a graphical user interface (GUI), which offers real-time feedback and tracks the duration of poor posture. The SVM model achieved 100% classification accuracy on a dataset collected from 25 participants using a 90/10 train-test split. Cross-validation further confirmed the model’s reliability, with an average accuracy of 99%. The system’s precise classification and intuitive feedback make it a practical tool for posture correction in office and home settings. These results suggest significant potential for reducing posture-related health risks through early intervention and real-time monitoring.

Suggested Citation

  • Ang Qizheng & Lim Way Soong & Yeo Boon Chin & Petch Jearanaisilawong, 2025. "Design and development of an intelligent real-time pressure sensing system for sitting posture monitoring," Edelweiss Applied Science and Technology, Learning Gate, vol. 9(9), pages 440-455.
    • Handle: RePEc:ajp:edwast:v:9:y:2025:i:9:p:440-455:id:9819
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