Enhancing Laboratory Safety with AI: PPE Detection and Non-Compliant Activity Monitoring Using Object Detection and Pose Estimation


Authors : Aro Praveen; Nahin Shaikh; Mohammad Annus; Gayathri; Bharani Kumar Depuru

Volume/Issue : Volume 10 - 2025, Issue 3 - March

Google Scholar : https://tinyurl.com/mtk2e3rw

Scribd : https://tinyurl.com/4kzm5svn

DOI : https://doi.org/10.38124/ijisrt/25mar1274

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Abstract : Ensuring workplace safety and adhering to regulatory standards in pharmaceutical manufacturing is vital. However, traditional manual monitoring methods are inefficient, prone to errors, and labor-intensive, resulting in potential safety risks and non-compliance penalties. This research introduces an automated deep learning framework that employs video analytics for real-time compliance monitoring, providing a scalable alternative to manual inspection processes. The system integrates YOLOv11n for detecting Personal Protective Equipment (PPE), such as gloves, masks, and goggles, identifying violations where PPE is either missing or improperly worn. Additionally, YOLOv8n-Pose is utilized to assess non-compliant postures, including actions like bending, hand-raising, and face-touching. A logging system tracks violations with precise timestamps, enabling efficient documentation for audits and regulatory purposes. A curated video dataset was developed and annotated using Roboflow, featuring both compliant and non-compliant actions. To enhance the model's robustness, preprocessing techniques such as resizing, contrast enhancement, and data augmentation were applied. The system’s performance, evaluated using metrics like mean Average Precision (mAP), F1- score, and precision, demonstrated an impressive 90% accuracy, with a mAP@50 of 92.1% and a processing speed of 25 frames per second (FPS), fulfilling the real-time monitoring criteria. This solution offers a scalable, real-time alternative to manual inspections, reducing human intervention, improving workplace safety, ensuring compliance with regulations, and automating the documentation process. Future developments aim to integrate IoT devices, employ edge computing, and incorporate cloud-based analytics to further enhance safety monitoring and compliance.

References :

  1. S. Liu, Y. Yin, and S. Ostadabbas, “In-bed pose estimation: Deep learning with shallow dataset,” IEEE Journal of Translational Engineering in Health and Medicine, vol. 7, pp. 4900112, 2019. DOI: 10.1109/JTEHM.2019.2892970.
  2. R. M. Butler, E. Frassini, T. S. Vijfvinkel, S. van Riel, C. Bachvarov, J. Constandse, M. van der Elst, J. J. van den Dobbelsteen, and B. H. W. Hendriks, “Benchmarking 2D human pose estimators and trackers for workflow analysis in the cardiac catheterization laboratory,” Medical Engineering and Physics, vol. 136, 2025, Art. no. 104289. DOI: 10.1016/j.medengphy.2025.104289.
  3. V. R. Kumar, P. Waghmare, S. Bukya, B. K. Depuru, and I. Kaliamoorthy, “Forecasting drug demand for optimal medical inventory management: A data-driven approach with advanced machine learning techniques,” International Journal of Innovative Science and Research Technology, vol. 8, no. 9, pp. 221–229, Sep. 2023. DOI: 10.38124/IJISRT20AUG257
  4. Bhat, A. Dhadd, B. S. Patil, and B. K. Depuru, “Enhancing automobile manufacturing efficiency using machine learning: Sequence tracking and clamping monitoring with machine learning video analytics and laser light alert system,” International Journal of Innovative Science and Research Technology, vol. 8, no. 8, pp. 1884–1896, Aug. 2023. DOI: 10.1080/00207543.2022.2152897.
  5. Vinod, D. C. Mohanty, A. John, and B. K. Depuru, “Application of artificial intelligence in poultry farming - Advancing efficiency in poultry farming by automating the egg counting using computer vision system,” Research Square, Aug. 18, 2023. DOI: 10.21203/rs.3.rs-3266412/v1.
  6. M. Shahin, F. F. Chen, A. Hosseinzadeh, H. K. Koodiani, and H. Bouzary, “Enhanced safety implementation in 5S+1 via object detection algorithms,” The International Journal of Advanced Manufacturing Technology, vol. 126, 2023. DOI: 10.1007/s00170-023-10970-9.
  7. S. Ludwika and A. P. Rifai, “Deep learning for detection of proper utilization and adequacy of personal protective equipment in manufacturing teaching laboratories,” Safety, vol. 10, no. 1, p. 26, Mar. 2024. DOI: 10.3390/safety10010026.
  8. Y. Duan, Z. Li, and B. Shi, “Multi-Target Irregular Behavior Recognition of Chemical Laboratory Personnel Based on Improved DeepSORT Method,” Processes, vol. 12, no. 2796, Dec. 2024. DOI: 10.3390/pr12122796.
  9. L. Ali, F. Alnajjar, M. M. A. Parambil, M. I. Younes, Z. I. Abdelhalim, and H. Aljassmi, “Development of YOLOv5-based real-time smart monitoring system for increasing lab safety awareness in educational institutions,” Sensors, vol. 22, no. 8820, pp. 1–15, Nov. 2022. DOI: 10.3390/s22228820.
  10. S. Kaur, H. K. Shukla, R. K. Pal, N. Yadav, and S. Singh, “Human activity recognition,” International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), vol. 9, no. 3, pp. 161–166, May–Jun. 2022. DOI: 10.32628/IJSRSET229342.

Ensuring workplace safety and adhering to regulatory standards in pharmaceutical manufacturing is vital. However, traditional manual monitoring methods are inefficient, prone to errors, and labor-intensive, resulting in potential safety risks and non-compliance penalties. This research introduces an automated deep learning framework that employs video analytics for real-time compliance monitoring, providing a scalable alternative to manual inspection processes. The system integrates YOLOv11n for detecting Personal Protective Equipment (PPE), such as gloves, masks, and goggles, identifying violations where PPE is either missing or improperly worn. Additionally, YOLOv8n-Pose is utilized to assess non-compliant postures, including actions like bending, hand-raising, and face-touching. A logging system tracks violations with precise timestamps, enabling efficient documentation for audits and regulatory purposes. A curated video dataset was developed and annotated using Roboflow, featuring both compliant and non-compliant actions. To enhance the model's robustness, preprocessing techniques such as resizing, contrast enhancement, and data augmentation were applied. The system’s performance, evaluated using metrics like mean Average Precision (mAP), F1- score, and precision, demonstrated an impressive 90% accuracy, with a mAP@50 of 92.1% and a processing speed of 25 frames per second (FPS), fulfilling the real-time monitoring criteria. This solution offers a scalable, real-time alternative to manual inspections, reducing human intervention, improving workplace safety, ensuring compliance with regulations, and automating the documentation process. Future developments aim to integrate IoT devices, employ edge computing, and incorporate cloud-based analytics to further enhance safety monitoring and compliance.

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