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.
Qizheng et al. (Thu,) studied this question.