PosePal: An AI-Powered Human Posture Tracking and Real-Time Alert System

Musculoskeletal discomfort caused by prolonged sitting and unmonitored poor posture habits has become a serious occupational health concern among university employees. Without any real-time corrective mechanism, postural deviations accumulate silently into chronic back pain, neck strain, and shoulder tension — conditions widely reported among faculty and staff at Pangasinan State University – Alaminos City Campus (PSU-ACC). Survey results further confirmed that 95% of employees experience posture-related discomfort, yet none had previously used a posture monitoring device, and 55% had no specific method to manage their sitting habits. This study developed PosePal: An AI-Powered Human Posture Tracking and Real-Time Alert System to address these persistent ergonomic challenges in an academic workplace setting. The system was built using Python, Dart, and JavaScript, integrating YOLOv11-Pose for real-time human detection and key point-based posture estimation, supported by Firebase as the cloud database. IP cameras serve as the primary input devices, continuously capturing body alignment for AI-based analysis without requiring any wearable equipment. To enhance accuracy, the system integrates multi-object tracking, face recognition, and a rule-based posture classifier with temporal validation. PosePal automatically detects improper sitting positions and instantly alerts users to correct their posture, while providing posture scoring, analytics reports, personalized recommendations, face recognition for secure identification, and an admin dashboard for institutional monitoring and management. The system was developed following Agile methodology and evaluated by 21 respondents comprising faculty, non-teaching staff, and an IT expert. Assessed against ISO/IEC 25010 quality standards, PosePal achieved an overall weighted mean of 4.19 (Excellent), confirming its effectiveness, reliability, and usability in promoting posture awareness and improving workplace wellness. The study concludes with recommendations to refine posture detection and alert mechanisms, continuously evaluate system accuracy and user satisfaction, and conduct further studies to expand system features and ensure PosePal remains adaptive and reliable across diverse real-world settings.

Artificial Intelligence, Computer Vision, Posture Estimation, Posture Detection, YOLO

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