ALIGN: An AI-Driven IoT Framework for Real-Time Sitting Posture Detection

Posture, defined as the body’s alignment relative to gravity, plays a vital role in musculoskeletal health by influencing muscle efficiency, joint integrity, and overall balance. The global shift to remote and sedentary work environments during the COVID-19 pandemic has amplified concerns regarding posture-related disorders and long-term ergonomic risks. This study introduces ALIGN, an IoT-enabled intelligent system for real-time sitting posture detection that integrates both machine learning and deep learning methodologies. Implemented on a single-board computer, the system processes live video streams to classify user posture as correct or incorrect and provides alert-based notifications when sustained improper posture is detected, thereby supporting real-time posture awareness without issuing corrective instructions. Among conventional classifiers, K-Nearest Neighbors (KNN), Support Vector Classifiers (SVC), and Multi-Layer Perceptrons (MLP) achieved accuracies of 98.74%, 96.64%, and 97.17%, respectively, while in the deep learning category, ResNet52 reached a test accuracy of 94.37%, outperforming DenseNet121 (81.53%). By enabling intelligent real-time detection and monitoring, ALIGN offers a scalable and cost-effective solution for ergonomic risk awareness and preventive digital health support.