Force Sensitive Resistors-Based Real-Time Posture Detection System Using Machine Learning Algorithms

To detect the improper sitting posture of a person sitting on a chair, a posture detection system using machine learning classification has been proposed in this work. The addressed problem correlates to the third Sustainable Development Goal (SDG), ensuring healthy lives and promoting well-being for all ages, as specified by the World Health Organization (WHO). An improper sitting position can be fatal if one sits for a long time in the wrong position, and it can be dangerous for ulcers and lower spine discomfort. This novel study includes a practical implementation of a cushion consisting of a grid of 3 × 3 force-sensitive resistors (FSR) embedded to read the pressure of the person sitting on it. Additionally, the Body Mass Index (BMI) has been included to increase the resilience of the system across individual physical variances and to identify the incorrect postures (backward, front, left, and right-leaning) based on the five machine learning algorithms: ensemble boosted trees, ensemble bagged trees, ensemble subspace K-Nearest Neighbors (KNN), ensemble subspace discriminant, and ensemble RUSBoosted trees. The proposed arrangement is novel as existing works have only provided simulations without practical implementation, whereas we have implemented the proposed design in Simulink. The results validate the proposed sensor placements, and the machine learning (ML) model reaches a maximum accuracy of 99.99%, which considerably outperforms the existing works. The proposed concept is valuable as it makes it easier for people in workplaces or even at individual household levels to work for long periods without suffering from severe harmful effects from poor posture.