Secure BioGauard


EOI: 10.11242/viva-tech.01.06.005

Download Full Text here



Citation

Anuradha Dhumwad, Kashish Chauhan, Chaitanya Pawar, Prof. Reshma Chaudhari, "A Review on Hospital Seat Detection System ", VIVA-IJRI Volume 1, Issue 7, Article COMP_07, pp. 1-10, 2024. Published by Computer Engineering Department, VIVA Institute of Technology, Virar, India.

Abstract

In the dynamic landscape of contemporary healthcare, efficient resource management is paramount, particularly in bustling hospital environments. This surveillance system introduces an innovative solution to address challenges in hospital crowd management. Leveraging advanced surveillance technology and real-time data processing, the system enhances patient experience by notifying individuals about unoccupied seats in waiting areas, optimizing resource utilization and minimizing inconvenience. Prioritizing patient well-being, the system contributes to judicious healthcare resource allocation. Providing healthcare personnel with real-time insights into seat occupancy, the system enables proactive responses, ensuring organized and streamlined patient flow. It represents a pioneering approach to hospital crowd management, featuring real-time oversight and communication capabilities. Through sophisticated image processing and machine learning, the system continually monitors waiting area occupancy, promptly notifying waiting patients of vacant seats, reducing wait times, and enhancing overall satisfaction. Dynamic resource allocation ensures patients are directed efficiently, benefiting both patients and healthcare facilities. This healthcare resource management system, with its comprehensive features, addresses immediate crowd management challenges and establishes itself as a holistic solution for elevating patient experiences and overall healthcare operational effectiveness.

Keywords

crowd management, patient-centric, real-time insights, seat occupancy, surveillance technology

References

  1. O. C. Daniel, V. Ramsurrun and A. K. Seeam, "Smart Library Seat, Occupant and Occupancy Information System, using Pressure and RFID Sensors," 2019 Conference on Next Generation Computing Applications (NextComp), Mauritius, 2019, pp. 1-5, doi: 10.1109/NEXTCOMP.2019.8883610.
  2. M. Devy, A. Giralt and A. Marin-Hernandez, "Detection and classification of passenger seat occupancy using stereovision," Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No.00TH8511), Dearborn, MI, USA, 2000, pp. 714-719, doi: 10.1109/IVS.2000.898433.
  3. B. George, H. Zangl, T. Bretterklieber and G. Brasseur, "Seat Occupancy Detection Based on Capacitive Sensing," in IEEE Transactions on Instrumentation and Measurement, vol. 58, no. 5, pp. 1487-1494, May 2009, doi: 10.1109/TIM.2009.2009411.
  4. X. Wu, G. Liang, K. K. Lee and Y. Xu, "Crowd Density Estimation Using Texture Analysis and Learning," 2006 IEEE International Conference on Robotics and Biomimetics, Kunming, China, 2006, pp. 214-219, doi: 10.1109/ROBIO.2006.340379.
  5. B. Xu, P. Paul, Y. Artan and F. Perronnin, "A machine learning approach to vehicle occupancy detection," 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), Qingdao, China, 2014, pp. 1232-1237, doi: 10.1109/ITSC.2014.6957856.
  6. Y. Jing, L. Mao and J. Xu, "Research on a library seat management system," 2019 2nd International Conference on Information Systems and Computer Aided Education (ICISCAE), Dalian, China, 2019, pp. 297-301, doi: 10.1109/ICISCAE48440.2019.221639.
  7. S. Poovizhi, M. Premalatha and C. Nivetha, "Automatic water level monitoring and seat availability details in train using wireless sensor network," 2017 International Conference on Computation of Power, Energy Information and Communication (ICCPEIC), Melmaruvathur, India, 2017, pp. 321-324, doi: 10.1109/ICCPEIC.2017.8290385.
  8. Y. Artan, P. Paul, F. Perronin and A. Burry, "Comparison of face detection and image classification for detecting front seat passengers in vehicles," IEEE Winter Conference on Applications of Computer Vision, Steamboat Springs, CO, USA, 2014, pp. 1006-1012, doi: 10.1109/WACV.2014.6835994.
  9. S. Wang, R. Li, X. Lv, X. Zhang, J. Zhu and J. Dong, "People Counting Based on Head Detection and Reidentification in Overlapping Cameras System," 2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC), Jinan, China, 2018, pp. 47-51, doi: 10.1109/SPAC46244.2018.8965468.
  10. Liang, Hongyu & Wu, Jinchen & Huang, Kaiqi. (2012). “People in Seats Counting via Seat Detection for Meeting Surveillance”. 202-210. 10.1007/978-3-642-33506-8_26.
  11. D. Beymer, "Person counting using stereo," Proceedings Workshop on Human Motion, Austin, TX, USA, 2000, pp. 127-133, doi: 10.1109/HUMO.2000.897382.
  12. Tao Zhao and R. Nevatia, "Bayesian human segmentation in crowded situations," 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings., Madison, WI, USA, 2003, pp. II-459, doi: 10.1109/CVPR.2003.1211503.
  13. C. S. Manikandababu, J. M, K. B and N. S, "Smart Crowd Monitoring System using Image Processing Technique During Covid-19," 2022 3rd International Conference on Smart Electronics and Communication (ICOSEC), Trichy, India, 2022, pp. 1472-1475, doi: 10.1109/ICOSEC54921.2022.9951874.
  14. M. Li, Z. Zhang, K. Huang and T. Tan, "Estimating the number of people in crowded scenes by MID based foreground segmentation and head-shoulder detection," 2008 19th International Conference on Pattern Recognition, Tampa, FL, USA, 2008, pp. 1-4, doi: 10.1109/ICPR.2008.4761705.
  15. X. Hao, H. Chen and J. Li, "An Automatic Vehicle Occupant Counting Algorithm Based on Face Detection," 2006 8th international Conference on Signal Processing, Guilin, China, 2006, pp., doi: 10.1109/ICOSP.2006.345797.