Journals / An International Journal of Optimization and Control: Theories & Applications (IJOCTA) / 2022 / Cilt: 12 - Sayı: 2

Localization of an ultra wide band wireless endoscopy capsule inside the human body using received signal strength and centroid algorithm

Pages
151–159
Publication Date
2022-10-11
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results show that a precise position detection is possible with fewer sensors positioned correctly.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Wireless capsule endoscopy (WCE) is used for imaging and diagnosing diseases in the gastrointestinal (GI) system.
📊
Key Findings & Data:In this study, RSS-based centroid algorithm has been used in order to accurately predict the capsule position on a sample data set.
💡
Conclusion & Impact:The results show that a precise position detection is possible with fewer sensors positioned correctly.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

Wireless capsule endoscopy (WCE) is used for imaging and diagnosing diseases in the gastrointestinal (GI) system. The location of the disease detected by WCE is still an important problem. Location information is very important for the surgical or drug treatment of the detected disease. In this study, RSS-based centroid algorithm has been used in order to accurately predict the capsule position on a sample data set. The effect of different parameters such as number of sensors used on the proposed mathematical model, location of sensors on positioning is analyzed in detail. The results show that a precise position detection is possible with fewer sensors positioned correctly. As a result, the positioning error with the correctly selected sensors is reduced by approximately 55%. In addition, the performance of the proposed method was compared with the classical centroid algorithm and more than 50% improvement was achieved

🔖

Cite This Article

Suveren, M., Akay, R., Kanaan, M. (2022). Localization of an ultra wide band wireless endoscopy capsule inside the human body using received signal strength and centroid algorithm. An International Journal of Optimization and Control: Theories & Applications (IJOCTA), Cilt: 12 - Sayı: 2, 151–159. https://ajindex.com/articles/440517

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (13)

  1. Mateen, H., Basar, R., Ahmed, A. U., & Ahmad, M. Y. (2017). Localization of wireless capsule endoscope: A systematic review. IEEE Sensors Journal, 17(5), 1197-1206
  2. Kumar, R., Zhao, Q., Seshamani, S., Mullin, G., Hager, G., & Dassopoulos, T. (2012). Assessment of crohn's disease lesions in wireless capsule endoscopy images. IEEE Transactions on Biomedical Engineering, 59(2), 355-362
  3. Woods, S.P., & Constandinou, T.G. (2013). Wireless capsule endoscope for targeted drug delivery: Mechanics and design considerations. IEEE Transactions on Biomedical Engineering 60(4), 945-953
  4. Davenport, D., Hernandez, M., Lewis, D., Li, H. B., McPartland, R., Omeni, O., & Astrin, A. (2011). IEEE 802.15. 6 Tutorial. IEEE P802, 15
  5. Yuce, M.R., Keong, H.C., & Chae, M.S. (2009). Wideband communication for implantable and wearable systems. IEEE transactions on microwave theory and techniques 57(10), 2597-2604
  6. Khaleghi, A., Chavez-Santiago, R., & Balasingham, I. (2010). Ultra-wideband pulse-based data communications for medical implants. IET communications 4(15), 1889-1897
  7. Alam, M. W., Vedaei, S. S., & Wahid, K. A. (2020). A fluorescence-based wireless capsule endoscopy system for detecting colorectal cancer. Cancers, 12(4), 890
  8. Khan, A. H., Sohag, M. H. A., Vedaei, S. S., Mohebbian, M. R., & Wahid, K. A. (2020). Automatic detection of intestinal bleeding using an optical sensor for wireless capsule endoscopy. In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 4345-4348). IEEE
  9. Deeba, F., Bui, F. M., & Wahid, K. A. (2020). Computer-aided polyp detection based on image enhancement and saliency-based selection. Biomedical signal processing and control, 55, 101530
  10. Wang, M., Shi, Q., Song, S., Hu, C., & Meng, M. Q. H. (2019). A novel relative position estimation method for capsule robot moving in gastrointestinal tract. Sensors, 19(12), 2746
  11. Shao, G., Tang, Y., Tang, L., Dai, Q. & Guo, Y. (2019). A Novel Passive Magnetic Localization Wearable System for Wireless Capsule Endoscopy, IEEE Sensors Journal, 19(9), 3462-3472
  12. Than, T.D., Alici, G., Zhou, & H., Li, W. (2012). A review of localization systems for robotic endoscopic capsules. IEEE Transactions on Biomedical Engineering 59(9), 2387-2399
  13. Pahlavan, K., Geng, Y., Cave, D. R., Bao, G., Mi, L., Agu, E.,. & Tarokh, V. (2015). A novel cyber physical system for 3-D imaging of the small intestine in vivo. IEEE Ac