PostureGuard: AI-Powered Posture and Drowsiness Monitoring System

Long-term use of computers is associated with musculoskeletal strain, visual fatigue and reduced productivity. PostureGuard is a real-time desktop monitoring system designed to reduce posture-related risk and drowsiness during extended screen work by integrating live camera analysis, ergonomics-oriented feedback, and session analytics. The system integrates pose landmarks utilizing an upper-body posture analysis with facial landmarks to assess drowsiness, and provides timely multimodal alerts through on-screen notifications and audio warnings. A hybrid data architecture implements local SQLite storage for offline reliability, while optional MongoDB synchronisation enables cloud-based continuity and multi-device analytics. The proposed design emphasises practical deployment on commodity hardware, low-latency inference, and user-centred intervention-frequency control.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-06
DOI
https://doi.org/10.5281/zenodo.22522716
Primary Topic
Ergonomics and Musculoskeletal Disorders
Type
preprint
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preprint

PostureGuard: AI-Powered Posture and Drowsiness Monitoring System

Affan Khan, Ahmed Khan
Zenodo (CERN European Organization for Nuclear Research)
Ergonomics and Musculoskeletal Disorders
preprint

PostureGuard: AI-Powered Posture and Drowsiness Monitoring System

Affan Khan, Ahmed Khan
preprint en

Abstract

Long-term use of computers is associated with musculoskeletal strain, visual fatigue and reduced productivity. PostureGuard is a real-time desktop monitoring system designed to reduce posture-related risk and drowsiness during extended screen work by integrating live camera analysis, ergonomics-oriented feedback, and session analytics. The system integrates pose landmarks utilizing an upper-body posture analysis with facial landmarks to assess drowsiness, and provides timely multimodal alerts through on-screen notifications and audio warnings. A hybrid data architecture implements local SQLite storage for offline reliability, while optional MongoDB synchronisation enables cloud-based continuity and multi-device analytics. The proposed design emphasises practical deployment on commodity hardware, low-latency inference, and user-centred intervention-frequency control.

Zenodo (CERN European Organization for Nuclear Research)
Ergonomics and Musculoskeletal Disorders
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