This paper presents a novel real-time posture monitoring system using signal processing and computer vision techniques to provide accurate feedback on body posture. By measuring key angles between the head-shoulder and shoulder-hip regions, the system identifies deviations from ideal posture. A Butterworth low-pass filter is employed to smooth the posture data, significantly reducing noise and misclassification of sudden movements as poor posture. The proposed system’s novelty lies in the integration of signal processing to enhance data interpretation, ensuring that momentary shifts are filtered out, resulting in more reliable classification and feedback. The system was tested in real-world scenarios, demonstrating its ability to offer immediate, high-accuracy posture feedback. Unlike conventional systems that rely solely on raw data, our approach uses smoothed, noise-free data to provide a clearer understanding of posture, making it suitable for deployment in workplaces, home offices, and rehabilitation centers. Future work will focus on multi-joint analysis, duration-based feedback mechanisms for sustained posture deviations, and the impact of camera angle on measurement accuracy. Overall, the system provides a cost-effective and efficient solution for continuous posture monitoring, aiming to improve health and ergonomics across various settings.