Sitting Pressure Distribution Monitoring System Using K-Nearest Neighbor and Rule-Based Logic

An embedded sitting-pressure monitoring system is developed to identify asymmetric pressure distribution and detect prolonged static sitting. Four FSR402 sensors connected to a 12-bit ADC on an ESP32 measure seatpressure, using Left_percent, Right_percent, and P_ratio as classification features. A K-Nearest Neighbor (KNN) classifier (K = 5) is implemented directly on the ESP32 to classify Balanced, Lean Left, and Lean Right postures. Classification results are combined with duration-based rules to determine NORMAL, WARNING, and HIGH RISK conditions, supported by vibration feedback, Firebase Realtime Database, and a Flutter application. The results show an average relative pressure measurement error of 1.05% (98.95% accuracy), with pressure shifts of 17.32–18.29%. KNN achieves 98.89% mean cross-validation accuracy and 97.78% test accuracy, with a macro F1-score of 0.9776, while end-to-end posture detection achieves 90% accuracy. Haptic feedback delays range from 0.784 to 1.127 seconds. These findings demonstrate the feasibility of implementing KNN-based posture classification directly on an ESP32 and integrating it with duration-based rules for real-time monitoring of asymmetric and prolonged static sitting.