Treffer: Automated health monitoring system using YOLOv8 for real-time device parameter detection.
Title:
Automated health monitoring system using YOLOv8 for real-time device parameter detection.
Authors:
Mahmood MS; Department of Electrical and Electronic Engineering, Chittagong University of Engineering and Technology, Chattogram, Bangladesh., Shoyaeb M; Department of Electrical and Electronic Engineering, Chittagong University of Engineering and Technology, Chattogram, Bangladesh., Chowdhury A; Department of Electrical and Electronic Engineering, Chittagong University of Engineering and Technology, Chattogram, Bangladesh. aditta.eee@cuet.ac.bd., Chowdhury MH; Department of Electrical and Electronic Engineering, Chittagong University of Engineering and Technology, Chattogram, Bangladesh.
Source:
Physical and engineering sciences in medicine [Phys Eng Sci Med] 2026 Mar; Vol. 49 (1), pp. 397-406. Date of Electronic Publication: 2025 Nov 17.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Springer Country of Publication: Switzerland NLM ID: 101760671 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2662-4737 (Electronic) Linking ISSN: 26624729 NLM ISO Abbreviation: Phys Eng Sci Med Subsets: MEDLINE
Imprint Name(s):
Original Publication: Switzerland : Springer, [2020]-
MeSH Terms:
References:
Saha Tchinda B, Tchiotsop D (2025) A lightweight 1D convolutional neural network model for arrhythmia diagnosis from electrocardiogram signal. Phys Eng Sci Med 48:577–589. https://doi.org/10.1007/s13246-025-01525-1. (PMID: 10.1007/s13246-025-01525-139998757)
Shaikh T-A, Rasool T, Verma P (2023) Machine intelligence and medical cyber-physical system architectures for smart healthcare: taxonomy, challenges, opportunities, and possible solutions. Artif Intell Med 146:102692. https://doi.org/10.1016/j.artmed.2023.102692. (PMID: 10.1016/j.artmed.2023.10269238042609)
Lam NFD, Cai J, Ng KH (2025) Artificial intelligence and its potential integration with the clinical practice of diagnostic imaging medical physicists: a review. Phys Eng Sci Med 48:529–544. https://doi.org/10.1007/s13246-025-01535-z. (PMID: 10.1007/s13246-025-01535-z40126762)
Li G, Liu J, Li X, Wei R (2014) A multiple biomedical signals synchronous acquisition circuit based on over-sampling and shaped signal for the application of the ubiquitous health care. Circuits Syst Signal Process 33:3003–3017. https://doi.org/10.1007/s00034-014-9794-5. (PMID: 10.1007/s00034-014-9794-5)
Xu H, Yu W, Griffith D, Golmie N-A (2018) Survey on industrial internet of things: a cyber-physical systems perspective. IEEE Access 6:78238–78259. https://doi.org/10.1109/ACCESS.2018.2884906. (PMID: 10.1109/ACCESS.2018.2884906)
Bhamare D, Zolanvari M, Erbad A, Jain R, Khan K, Meskin N (2020) Cybersecurity for industrial control systems: a survey. Comput Secur 89:101677. https://doi.org/10.1016/j.cose.2019.101677. (PMID: 10.1016/j.cose.2019.101677)
Vidyashree L (2022) Suresha: Mitigation of cyber attacks assuring security with conglomerate edict based intrusion detection system in IoT. Sadhana Acad Proc Eng Sci. https://doi.org/10.1007/s12046-022-01818-1. (PMID: 10.1007/s12046-022-01818-1)
Slavin O-A, Arlazarov V-L (2023) Algorithms of the tiger and cuneiform optical character recognition software. Pattern Recognit Image Anal 33(4):669–684. https://doi.org/10.1134/S1054661823040442. (PMID: 10.1134/S1054661823040442)
Li N, Zhang W, Liu Z, Liu K, Wang J, Zhang F (2024) Pdse-yolov8: a lightweight detection method for internal defects in asphalt roads. Signal Image Video Process. https://doi.org/10.1007/s11760-024-03518-1. (PMID: 10.1007/s11760-024-03518-1)
Nayak R, Pati U-C, Das S-K, Sahoo G-K (2024) YOLO-GTWDNet: a lightweight YOLOv8 network with GhostNet backbone and transformer neck to detect handheld weapons for smart city applications. Signal Image Video Process 18(11):8159–8167. https://doi.org/10.1007/s11760-024-03458-w. (PMID: 10.1007/s11760-024-03458-w)
Skorobogatjko A, Romanovs A, Kunicina N (2014) State of the art in the healthcare cyber-physical systems. Inf Technol Manag Sci. https://doi.org/10.1515/itms-2014-0019.1. (PMID: 10.1515/itms-2014-0019.1)
Gatouillat A, Badr Y, Massot B, Sejdic E (2018) Internet of medical things: a review of recent contributions dealing with cyber-physical systems in medicine. IEEE Internet Things J 5(5):3810–3822. https://doi.org/10.1109/JIOT.2018.2849014. (PMID: 10.1109/JIOT.2018.2849014)
Henry C, Ahn S-Y, Lee S-W (2020) Multi- national license plate recognition using generalized character sequence detection. IEEE Access 8:35185–35199. https://doi.org/10.1109/ACCESS.2020.2974973. (PMID: 10.1109/ACCESS.2020.2974973)
Finnegan E, Villarroel M, Velardo C, Tarassenko L (2019) Automated method for detecting and reading seven-segment digits from images of blood glucose metres and blood pressure monitors. J Med Eng Technol 43(6):341–355. https://doi.org/10.1080/03091902.2019.1673844. (PMID: 10.1080/03091902.2019.167384431679409)
Castro-Zunti R-D, Yépez J, Ko S-B (2020) License plate segmentation and recognition system using deep learning and OpenVINO. IET Intell Transp Syst 14(2):119–126. https://doi.org/10.1049/iet-its.2019.0481. (PMID: 10.1049/iet-its.2019.0481)
Rajesh M, Rajan B-K, Roy A, Thomas K-A, Thomas A, Tharakan T-B, Dinesh C (2017) Text recognition and face detection aid for visually impaired person using Raspberry PI. In: 2017 international conference on circuit, power and computing technologies (ICCPCT), pp 1–5 (2017). https://doi.org/10.1109/ICCPCT.2017.8074355.
Kanagarathinam K, Sekar K (2019) Text detection and recognition in raw image dataset of seven segment digital energy meter display. Energy Rep 5:842–852. https://doi.org/10.1016/j.egyr.2019.07.004. (PMID: 10.1016/j.egyr.2019.07.004)
Drobac S, Lindén K (2020) Optical character recognition with neural networks and post-correction with finite state methods. Int J Doc Anal Recognit 23(4):279–295. https://doi.org/10.1007/s10032-020-00359-9. (PMID: 10.1007/s10032-020-00359-9)
Balaha H-M, Ali H-A, Saraya M, Badawy M (2021) A new Arabic handwritten character recognition deep learning system (AHCR-DLS). Neural Comput Appl. https://doi.org/10.1007/s00521-020-05397-2. (PMID: 10.1007/s00521-020-05397-2)
Elrefaei L-A, Bajaber A, Natheir S, Abu-Sanab N, Bazi M (2015) Automatic electricity meter reading based on image processing. In: 2015 IEEE Jordan conference on applied electrical engineering and computing technologies (AEECT), pp 1–5 (2015). https://doi.org/10.1109/AEECT.2015.7360571.
Sathiya S, Balasubramanian M, Priya D-V (2014) Real time recognition of traffic light and their signal count-down timings. In: International conference on information communication and embedded systems (ICICES2014), pp 1–6. https://doi.org/10.1109/ICICES.2014.7033965.
Boonsim N, Kanjaruek S (2023) An integrated technique for detecting seven-segment digits on medical devices. In: 27th international computer science and engineering conference 2023, ICSEC, pp 65–68 (2023). https://doi.org/10.1109/IC-SEC59635.2023.10329763 .
Ghugardare R-P, Narote S-P, Mukherji P, Kulkarni P-M (2019) Optical character recognition system for seven segment display images of measuring instruments. In: TENCON 2009 -2009 IEEE Region 10 Conference, pp 1–6. https://doi.org/10.1109/TENCON.2009.5395994.
Liu C (2016) Digits recognition on medical device. https://ir.lib.uwo.ca/etdhttps://ir.lib.uwo.ca/etd/3683.
Moreira L-P (2022) Automated medical device display reading using deep learning object detection. https://arxiv.org/abs/2210.01325.
Gunawan T-S, Mohammad H-R, Yaacob M, Azmin N-F-M, Ismail N, Ismail M-Y, Kartiwi M, Rahman A-A (2022) Development of seven segment display recognition using tensorflow on raspberry Pi. In: 2022 IEEE 8th international conference on computing, engineering and design (ICCED), pp 1–6. https://doi.org/10.1109/ICCED56140.2022.10010449.
Tan L, Huangfu T, Wu L, Chen W (2021) Comparison of YOLO v3, Faster R-CNN, and SSD for Real-Time Pill Identification. https://doi.org/10.21203/rs.3.rs-668895/v1.
Shaikh T-A, Rasool T, Verma P (2023) Machine intelligence and medical cyber-physical system architectures for smart healthcare: taxonomy, challenges, opportunities, and possible solutions. Artif Intell Med 146:102692. https://doi.org/10.1016/j.artmed.2023.102692. (PMID: 10.1016/j.artmed.2023.10269238042609)
Lam NFD, Cai J, Ng KH (2025) Artificial intelligence and its potential integration with the clinical practice of diagnostic imaging medical physicists: a review. Phys Eng Sci Med 48:529–544. https://doi.org/10.1007/s13246-025-01535-z. (PMID: 10.1007/s13246-025-01535-z40126762)
Li G, Liu J, Li X, Wei R (2014) A multiple biomedical signals synchronous acquisition circuit based on over-sampling and shaped signal for the application of the ubiquitous health care. Circuits Syst Signal Process 33:3003–3017. https://doi.org/10.1007/s00034-014-9794-5. (PMID: 10.1007/s00034-014-9794-5)
Xu H, Yu W, Griffith D, Golmie N-A (2018) Survey on industrial internet of things: a cyber-physical systems perspective. IEEE Access 6:78238–78259. https://doi.org/10.1109/ACCESS.2018.2884906. (PMID: 10.1109/ACCESS.2018.2884906)
Bhamare D, Zolanvari M, Erbad A, Jain R, Khan K, Meskin N (2020) Cybersecurity for industrial control systems: a survey. Comput Secur 89:101677. https://doi.org/10.1016/j.cose.2019.101677. (PMID: 10.1016/j.cose.2019.101677)
Vidyashree L (2022) Suresha: Mitigation of cyber attacks assuring security with conglomerate edict based intrusion detection system in IoT. Sadhana Acad Proc Eng Sci. https://doi.org/10.1007/s12046-022-01818-1. (PMID: 10.1007/s12046-022-01818-1)
Slavin O-A, Arlazarov V-L (2023) Algorithms of the tiger and cuneiform optical character recognition software. Pattern Recognit Image Anal 33(4):669–684. https://doi.org/10.1134/S1054661823040442. (PMID: 10.1134/S1054661823040442)
Li N, Zhang W, Liu Z, Liu K, Wang J, Zhang F (2024) Pdse-yolov8: a lightweight detection method for internal defects in asphalt roads. Signal Image Video Process. https://doi.org/10.1007/s11760-024-03518-1. (PMID: 10.1007/s11760-024-03518-1)
Nayak R, Pati U-C, Das S-K, Sahoo G-K (2024) YOLO-GTWDNet: a lightweight YOLOv8 network with GhostNet backbone and transformer neck to detect handheld weapons for smart city applications. Signal Image Video Process 18(11):8159–8167. https://doi.org/10.1007/s11760-024-03458-w. (PMID: 10.1007/s11760-024-03458-w)
Skorobogatjko A, Romanovs A, Kunicina N (2014) State of the art in the healthcare cyber-physical systems. Inf Technol Manag Sci. https://doi.org/10.1515/itms-2014-0019.1. (PMID: 10.1515/itms-2014-0019.1)
Gatouillat A, Badr Y, Massot B, Sejdic E (2018) Internet of medical things: a review of recent contributions dealing with cyber-physical systems in medicine. IEEE Internet Things J 5(5):3810–3822. https://doi.org/10.1109/JIOT.2018.2849014. (PMID: 10.1109/JIOT.2018.2849014)
Henry C, Ahn S-Y, Lee S-W (2020) Multi- national license plate recognition using generalized character sequence detection. IEEE Access 8:35185–35199. https://doi.org/10.1109/ACCESS.2020.2974973. (PMID: 10.1109/ACCESS.2020.2974973)
Finnegan E, Villarroel M, Velardo C, Tarassenko L (2019) Automated method for detecting and reading seven-segment digits from images of blood glucose metres and blood pressure monitors. J Med Eng Technol 43(6):341–355. https://doi.org/10.1080/03091902.2019.1673844. (PMID: 10.1080/03091902.2019.167384431679409)
Castro-Zunti R-D, Yépez J, Ko S-B (2020) License plate segmentation and recognition system using deep learning and OpenVINO. IET Intell Transp Syst 14(2):119–126. https://doi.org/10.1049/iet-its.2019.0481. (PMID: 10.1049/iet-its.2019.0481)
Rajesh M, Rajan B-K, Roy A, Thomas K-A, Thomas A, Tharakan T-B, Dinesh C (2017) Text recognition and face detection aid for visually impaired person using Raspberry PI. In: 2017 international conference on circuit, power and computing technologies (ICCPCT), pp 1–5 (2017). https://doi.org/10.1109/ICCPCT.2017.8074355.
Kanagarathinam K, Sekar K (2019) Text detection and recognition in raw image dataset of seven segment digital energy meter display. Energy Rep 5:842–852. https://doi.org/10.1016/j.egyr.2019.07.004. (PMID: 10.1016/j.egyr.2019.07.004)
Drobac S, Lindén K (2020) Optical character recognition with neural networks and post-correction with finite state methods. Int J Doc Anal Recognit 23(4):279–295. https://doi.org/10.1007/s10032-020-00359-9. (PMID: 10.1007/s10032-020-00359-9)
Balaha H-M, Ali H-A, Saraya M, Badawy M (2021) A new Arabic handwritten character recognition deep learning system (AHCR-DLS). Neural Comput Appl. https://doi.org/10.1007/s00521-020-05397-2. (PMID: 10.1007/s00521-020-05397-2)
Elrefaei L-A, Bajaber A, Natheir S, Abu-Sanab N, Bazi M (2015) Automatic electricity meter reading based on image processing. In: 2015 IEEE Jordan conference on applied electrical engineering and computing technologies (AEECT), pp 1–5 (2015). https://doi.org/10.1109/AEECT.2015.7360571.
Sathiya S, Balasubramanian M, Priya D-V (2014) Real time recognition of traffic light and their signal count-down timings. In: International conference on information communication and embedded systems (ICICES2014), pp 1–6. https://doi.org/10.1109/ICICES.2014.7033965.
Boonsim N, Kanjaruek S (2023) An integrated technique for detecting seven-segment digits on medical devices. In: 27th international computer science and engineering conference 2023, ICSEC, pp 65–68 (2023). https://doi.org/10.1109/IC-SEC59635.2023.10329763 .
Ghugardare R-P, Narote S-P, Mukherji P, Kulkarni P-M (2019) Optical character recognition system for seven segment display images of measuring instruments. In: TENCON 2009 -2009 IEEE Region 10 Conference, pp 1–6. https://doi.org/10.1109/TENCON.2009.5395994.
Liu C (2016) Digits recognition on medical device. https://ir.lib.uwo.ca/etdhttps://ir.lib.uwo.ca/etd/3683.
Moreira L-P (2022) Automated medical device display reading using deep learning object detection. https://arxiv.org/abs/2210.01325.
Gunawan T-S, Mohammad H-R, Yaacob M, Azmin N-F-M, Ismail N, Ismail M-Y, Kartiwi M, Rahman A-A (2022) Development of seven segment display recognition using tensorflow on raspberry Pi. In: 2022 IEEE 8th international conference on computing, engineering and design (ICCED), pp 1–6. https://doi.org/10.1109/ICCED56140.2022.10010449.
Tan L, Huangfu T, Wu L, Chen W (2021) Comparison of YOLO v3, Faster R-CNN, and SSD for Real-Time Pill Identification. https://doi.org/10.21203/rs.3.rs-668895/v1.
Contributed Indexing:
Keywords: Convolutional neural network (CNN); Optical character recognition (OCR); Seven segment digit (SSD); You only look once (YOLO)
Entry Date(s):
Date Created: 20251117 Date Completed: 20260314 Latest Revision: 20260314
Update Code:
20260314
DOI:
10.1007/s13246-025-01673-4
PMID:
41247607
Database:
MEDLINE
Weitere Informationen
Nowadays, monitoring the health of elderly people at home or patients at the hospital on a regular basis is becoming necessary. Unfortunately, peer-to-peer treatment may require a longer time based on the availability of the doctors. In addition, it is practically impossible to go to hospitals for health checkups almost every day of the week. Hence, this research proposes an idea that can automate these processes without decreasing efficiency and reducing manual labor by integrating a healthcare system with the cyber layer to execute the automation processes. Previous text and image recognition studies used different machine learning and deep learning algorithms. However, in this study, an optical character recognition method ‛YOLO V8' is used, which provides a faster detection speed than other methods. The target was to retrofit biomedical devices such as blood pressure monitoring machines, digital thermometers, etc. using image processing techniques. To train the'YOLOv8' model, we have utilized two distinct image datasets that we have developed. The model showed an accuracy of 99.5% in detecting areas of concern on medical devices. Later, for recognition of values of different parameters from those devices a Convolutional Neural Network model is used, which confirms real-time validation employing 1000 images from different medical equipment. An accuracy of 99.7% has been achieved using this method. In the future, other medical devices such as heart rate monitors, pulse oximeters, etc. can be included in this system.
(© 2025. Australasian College of Physical Scientists and Engineers in Medicine.)
Declarations. Conflict of interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Ethical approval: This article contains no studies with human participants or animals performed by authors.