Advanced Optical Fiber Sensor-Based Smart Chair for Real-Time Monitoring of Student Physical Measurements and Physiological Signals

Considering that student physical measurement data are one of the important features, this helps to protect general health, improve physical performance, and ensure the well-being of every individual student. Particularly in educational settings, the students are supposed to sit for a long time, which causes mental stress, bad posture, and cardiovascular problems. Most authors suggest using wearable technology with advanced sensor options to avoid these problems. However, due to their unadaptable capacity, these technologies are still ineffective in capturing the exact physical health data. Sometimes, these technologies are not comfortable to wear for a long time. Unfortunately, some mechanical errors happen due to unsafe usage. Also, it involves manual labor to operate it, and they cannot collect the data continuously because of the continuous human motions. So, additional sensors are required to balance this situation. To avoid these limitations, the proposed study introduces the advanced optical fiber sensor-based smart chair device to continuously monitor the student’s physical measurement data, particularly physiological signals, posture data, and body measurement data, because this approach is mainly designed to monitor the physical data of students during their seating position. The smart cushion is the best approach in an educational setting where the students spend lots of time in their respective seats. Accurately monitoring the physical data is truly possible by using the suggested device. This innovative system combines Mach–Zehnder Interferometer (MZI) technology to detect micro-movements caused by heartbeats, posture shifts, and other physiological signals in the sitting position. This optical fiber sensor is covered with soft cushions to capture the exact health data of students. On the other hand, the data are then collected by an advanced dual mode generative adversarial network (D2GAN) and continuously processed and transformed using (URLLC) 6G wireless network technology. This approach improves the quality of analysis by generating synthetic data for more accurate predictions and anomaly detection tasks. The study simulation is inspired by existing papers based on the procedures by which we assess our proposed device. The outcomes show effective performance with a high accuracy ratio of 99.89%, a prediction ratio of about 97.25%, and an interaction ratio of about 96.12%. Additionally, the efficiency ratio is 95.32%. This outcome proves the proposed device’s effectiveness in detecting the physical data. It achieves a 20.5% error reduction rate, highlighting its adaptability in real-time prediction knowledge about students’ physical conditions. Overall, this smart chair approach to taking wearable technology to the next level is undoubtedly confirmed by this study.