Development of Raspberry Pi–Based Image Segmentation System for Automated Wound Area Measurement and Temperature Monitoring
DOI:
https://doi.org/10.15662/IJEETR.2026.0802115Keywords:
Wound healing, Image segmentation, Temperature sensor, Patient monitoring, CSV data storage, Infection detection, Healing percentageAbstract
This project proposes a wound healing monitoring system. It's efficient in checking how a wound is healing. It also finds infections that may happen during healing. The system uses an ID for each patient. This helps store the patients data on wound healing. The data is stored as CSV files for easy analysis. The system takes high-resolution images of the wound. It then accurately identifies the wound area. The system also calculates the wound area. It uses images from the start and current stages of healing. This helps calculate the healing percentage. So it gives a measure of wound healing. The system also has a temperature sensor. This sensor finds infections. So it helps evaluate wound healing
References
1. Yu-Hsien Lu, Meng-Hsuan Wu, Yu-Zheng Chen, Po- Liang Ou, Kuo-Shu Hung, Yi-Syuan Shin, Yuan-Yu Hsueh, Peng- Ting Chen, Chih-Lung Lin, "Multispectral Imaging for Preliminary Burn Depth Evaluation in Mice with Tissue Section Analysis", 2024 IEEE SENSORS, pp.1-4, 2024.
2. Yogesh Jadhav, K. H. Swetha, Omprakash Das, M. P. Sunil, "17513 Experimental evaluation and validation of different variants of PCA on octonionoctonion multispectral imagingmultispectral imaging", Octonion Sparse-Based Image Processing, pp.175, 2025.
3. Murugeshwari, B., Sudharson, K., Panimalar, S. P., Shanmugapriya, M., & Abinaya, M. (2020). SAFE–Secure Authentication in Federated Environment using CEG Key code.
4. Sugumar, R. (2025). Cyber-Secure Cloud Architecture Integrating Network and API Controls for Risk-Aware SAP Healthcare Data Platforms. International Journal of Humanities and Information Technology, 7(4), 53-60.
5. Sharma, K. P., Kumar, I., Singh, P. P., Anbazhagan, K., Albarakati, H. M., Bhatt, M. W., ... & Rana, A. (2024). Advancing spacecraft rendezvous and docking through safety reinforcement learning and ubiquitous learning principles. Computers in Human Behavior, 153, 108110.
6. Yi-Syuan Shin, Kuo-Shu Hung, Chung-Te Tsai, Meng-Hsuan Wu, Chih-Lung Lin, Yuan-Yu Hsueh, "Validation of multispectral imaging–based tissue oxygen saturation detecting system for wound healing recognition on open wounds", Journal of Biomedical Optics, vol.29, no.08, 2024.
7. C. -L. Lin et al., "Multispectral Imaging-Based System for Detecting Tissue Oxygen Saturation With Wound Segmentation for Monitoring Wound Healing," in IEEE Journal of Translational Engineering in Health and Medicine, vol. 12, pp. 468-479, 2024, doi: 10.1109/JTEHM.2024.3399232
8. R. Gupta et al., "Towards an AI-Based Objective Prognostic Model for Quantifying Wound Healing," in IEEE Journal of Biomedical and Health Informatics, vol. 28, no. 2, pp. 666-677, Feb. 2024, doi: 10.1109/JBHI.2023.3251901.
9. D. R. Seshadri, N. D. Bianco, A. N. Radwan, C. A. Zorman and K. M. Bogie, "An Absorbent, Flexible, Transparent, and Scalable Substrate for Wound Dressings," in IEEE Journal of Translational Engineering in Health and Medicine, vol. 10, pp. 1-9, 2022, Art no. 4900909.
10. Z. Ye, M. Yang, M. Farhat, M. M. . -C. Cheng and P. -Y. Chen, "Multimodal Wireless Wound Sensors via Higher-Order Parity-Time Symmetry," in IEEE Sensors Journal, vol. 24, no. 1, pp. 741-749, 1 Jan.1, 2024.
11. Y. Cao and Y. Wang, "The Impact of Artificial Intelligence and Deep Learning-Based Family-Centered Care Interventions on the Healing of Chronic Lower Limb Wounds in Children," in IEEE Access, vol. 12, pp. 125557-125570, 2024.
12. A. Mwangi, L. Navarro-Hilfiker, L. Brewka, M. Gryning, E. Fumagalli and M. Gibescu, "A Threshold-Triggered Deep Q-Network-Based Framework for Self-Healing in Autonomic Software-Defined IIoT-Edge Networks," in IEEE Transactions on Network and Service Management, vol. 23, pp. 1297-1311, 2026.
13. Agrippina Mwangi, León Navarro-Hilfiker, Lukasz Brewka, Mikkel Gryning, Elena Fumagalli, Madeleine Gibescu, "A Threshold-Triggered Deep Q-Network-Based Framework for Self-Healing in Autonomic Software-Defined IIoT-Edge Networks", IEEE Transactions on Network and Service Management, vol.23, pp.1297-1311, 2026.
14. A. Lazaro, M. Rodrigo Cujilema, R. Villarino and D. Girbau, "Smart Bandage for Wireless Pressure and Wound State Sensing Based on LC Sensor," in IEEE Sensors Journal, vol. 26, no. 1, pp. 1232-1247, 1 Jan.1, 2026.
15. D. Vital et al., "SkinAid: A Wirelessly Powered Smart Dressing Solution for Continuous Wound-Tracking Using Textile-Based Frequency Modulation," in IEEE Transactions on Biomedical Circuits and Systems, vol. 17, no. 5, pp. 985-998, Oct. 2023.
16. R. S. Fard et al., "Multimodal AI for Home Wound Patient Referral Decisions From Images With Specialist Annotations," in IEEE Journal of Translational Engineering in Health and Medicine, vol. 13, pp. 341-353, 2025.
17. C. Zeng, C. Li, M. Li, C. Chen, C. Liu and S. Han, "Wearable Organic Electrochemical Transistor System for Multiplexed Chronic-Wound Biosensing," in IEEE Sensors Journal, vol. 26, no. 3, pp. 5098-5105, 1 Feb.1, 2026.
18. C. -L. Lin et al., "Multispectral Imaging-Based System for Detecting Tissue Oxygen Saturation With Wound Segmentation for Monitoring Wound Healing," in IEEE Journal of Translational Engineering in Health and Medicine, vol. 12, pp. 468-479, 2024.
19. B. Pandey, D. Joshi, A. S. Arora, N. Upadhyay and H. S. Chhabra, "A Deep Learning Approach for Automated Detection and Segmentation of Pressure Ulcers Using Infrared-Based Thermal Imaging," in IEEE Sensors Journal, vol. 22, no. 15, pp. 14762-14768, 1 Aug.1, 2022..
20. B. K. S. Kumar, K. C. Anandakrishan, M. Sumant and S. Jayaraman, "Wound Care: Wound Management System," in IEEE Access, vol. 11, pp. 45301-45312, 2023.
21. D. M. Anisuzzaman, Y. Patel, J. A. Niezgoda, S. Gopalakrishnan, and Z. Yu, “A mobile app for wound localization using deep learning,” IEEE Access, vol. 10, pp. 61398–61409, 2022.
22. S. Sarp, M. Kuzlu, M. Pipattanasomporn, and O. Guler, “Simultaneous wound border segmentation and tissue classification using a conditional generative adversarial network,” J. Eng., vol. 2021, no. 3, pp. 125–134, Mar. 2021.
23. C.Nagarajan and M.Madheswaran - ‘Stability Analysis of Series Parallel Resonant Converter with Fuzzy Logic Controller Using State Space Techniques’- Taylor &Francis, Electric Power Components and Systems, Vol.39 (8), pp.780-793, May 2011. DOI: 10.1080/15325008.2010.541746
24. C.Nagarajan and M.Madheswaran - ‘Experimental verification and stability state space analysis of CLL-T Series Parallel Resonant Converter’ - Journal of Electrical Engineering, Vol.63 (6), pp.365-372, Dec.2012. DOI: 10.2478/v10187-012-0054-2
25. C.Nagarajan and M.Madheswaran - ‘Performance Analysis of LCL-T Resonant Converter with Fuzzy/PID Using State Space Analysis’- Springer, Electrical Engineering, Vol.93 (3), pp.167-178, September 2011. DOI 10.1007/s00202-011-0203-9
26. S.Tamilselvi, R.Prakash, C.Nagarajan,“Solar System Integrated Smart Grid Utilizing Hybrid Coot-Genetic Algorithm Optimized ANN Controller” Iranian Journal Of Science And Technology-Transactions Of Electrical Engineering, DOI10.1007/s40998-025-00917-z,2025
27. S.Tamilselvi, R.Prakash, C.Nagarajan,“ Adaptive sliding mode control of multilevel grid-connected inverters using reinforcement learning for enhanced LVRT performance” Electric Power Systems Research 253 (2026) 112428, doi.org/10.1016/j.epsr.2025.112428
28. S.Thirunavukkarasu, C. Nagarajan, 2024, “Performance Investigation on OCF and SCF study in BLDC machine using FTANN Controller," Journal of Electrical Engineering And Technology, Volume 20, pages 2675–2688, (2025), doi.org/10.1007/s42835-024-02126-w
29. C. Nagarajan, M.Madheswaran and D.Ramasubramanian- ‘Development of DSP based Robust Control Method for General Resonant Converter Topologies using Transfer Function Model’- Acta Electrotechnica et Informatica Journal , Vol.13 (2), pp.18-31,April-June.2013, DOI: 10.2478/aeei-2013-0025.
30. C.Nagarajan and M.Madheswaran - ‘DSP Based Fuzzy Controller for Series Parallel Resonant converter’- Springer, Frontiers of Electrical and Electronic Engineering, Vol. 7(4), pp. 438-446, Dec.12. DOI 10.1007/s11460-012-0212-0.
31. C.Nagarajan and M.Madheswaran - ‘Experimental Study and steady state stability analysis of CLL-T Series Parallel Resonant Converter with Fuzzy controller using State Space Analysis’- Iranian Journal of Electrical & Electronic Engineering, Vol.8 (3), pp.259-267, September 2012.
32. C.Nagarajan and M.Madheswaran, “Analysis and Simulation of LCL Series Resonant Full Bridge Converter Using PWM Technique with Load Independent Operation” has been presented in ICTES’08, a IEEE / IET International Conference organized by M.G.R.University, Chennai.Vol.no.1, pp.190-195, Dec.2007
33. Suganthi Mullainathan, Ramesh Natarajan, “An SPSS and CNN modelling based quality assessment using ceramic materials and membrane filtration techniques”, Revista Materia (Rio J.) Vol. 30, 2025, DOI: https://doi.org/10.1590/1517-7076-RMAT-2024-0721
34. M Suganthi, N Ramesh, “Treatment of water using natural zeolite as membrane filter”, Journal of Environmental Protection and Ecology, Volume 23, Issue 2, pp: 520-530,2022
35. M. Goyal, N. D. Reeves, A. K. Davison, S. Rajbhandari, J. Spragg, and M. H. Yap, “DFUNet: Convolutional neural networks for diabetic foot ulcer classification,” IEEE Trans. Emerg. Topics Comput. Intell., vol. 4, no. 5, pp. 728–739, Oct. 2020.
