DOA-based endoscopy capsule localization and orientation estimation via unscented kalman filter
Document Type
Article
Publication Date
11-1-2014
Abstract
© 2014 IEEE. The endoscopy capsule is a medical device capable of capturing images inside human's digestion system, specifically the small and big intestine. For medical diagnostics and surgery, it is required to know the position and direction of the image taken inside digestion system. This paper considers an alternative method of presurgery gastroscopy and colonoscopy monitoring procedure that allows the patient to freely move inside the medical ward. The direction-of-arrival (DOA) and inertial measurement unit (IMU) measurements are integrated to track the movement of capsule with respect to patient's body reference frame. The DOA is estimated via antenna arrays installed within a medical ward and the IMU is installed on the capsule endoscopy. The IMU sends the position information wirelessly to the antenna arrays in medical ward. Additional beacons are attached to the patient to allow body orientation and absolute position estimation due to the free movement. The nonhomogeneous nature of human body refracts the signal transmitted by the capsule, which leads to a highly nonlinear DOA function. This paper implements the unscented Kalman filter (UKF) to track the capsule by fusing the measurements made by DOA, IMU, and additional beacons attached to the patient. Simulations are conducted to investigate the capsule tracking and orientation estimation performance with respect to DOA resolution and beacons localization accuracy. Results confirm that compared with the DOA resolution, the beacons localization accuracy has a higher impact on the capsule orientation estimation performance. Furthermore, this paper investigates the impact of the number of available antenna arrays on multiplication required by UKF.
Publication Title
IEEE Sensors Journal
Recommended Citation
Goh, S.,
Zekavat, S.,
&
Pahlavan, K.
(2014).
DOA-based endoscopy capsule localization and orientation estimation via unscented kalman filter.
IEEE Sensors Journal,
14(11), 3819-3829.
http://doi.org/10.1109/JSEN.2014.2342720
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/10728