Smart Cushion System Based on Machine Learning and Pressure Sensing

Prolonged poor sitting posture increases the risk of musculoskeletal disorders and chronic diseases. We developed a smart cushion system that integrated pressure sensing and machine learning for posture recognition. Nine FSR406 sensors were used to measure pressure distribution on the system. A calibration and normalization process improves data consistency, and a heatmap visualizes the result. Among the five machine learning models evaluated, the narrow neural network achieved the best performance, with a validation accuracy of 97.63% and a test accuracy of 91.73%. When body mass index (BMI) was included as an additional input feature, the test accuracy improved to 95.49%, indicating that BMI positively impacts recognition performance.