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

Ang Qizheng (), Lim Way Soong (), Yeo Boon Chin () and Petch Jearanaisilawong ()

Edelweiss Applied Science and Technology, 2025, vol. 9, issue 9, 440-455

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.

Keywords: Sitting Posture Monitoring; Support Vector Machine; Velostat Pressure Sensor. (search for similar items in EconPapers)
Date: 2025
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