Integral-Free Spatial Orientation Estimation Method and Wearable Rotation Measurement Device for Robot-Assisted Catheter Intervention

Hooshiar, Amir; Sayadi, Amir; Dargahi, Javad; Najarian, Siamak

doi:10.1109/tmech.2021.3071295

Cited by 17 publications

(4 citation statements)

References 41 publications

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“…To derive an absolute upper error bound for the MKCL, we consider the scalar case of (6). In this scenario,…”

Section: B Estimation Of a Constant Vectormentioning

confidence: 99%

“…IMUs have several advantages over optical motion capture systems, including their low cost, small size, and ability to function outside of indoor environments. Recently, IMUs have been utilized in health monitoring [4], joint angle estimation [5], and robot control [6]. These applications require IMUs to maintain their precision in a disturbed environment and expect a faster execution frequency and longer work time.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Kernel Maximum Correntropy Kalman Filter for Orientation Estimation

Li,

Shi

et al. 2022

IEEE Robot. Autom. Lett.

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This paper presents two computationally efficient algorithms for the orientation estimation of inertial measurement units (IMUs): the correntropy-based gradient descent (CGD) and the correntropy-based decoupled orientation estimation (CDOE). Traditional methods, such as gradient descent (GD) and decoupled orientation estimation (DOE), rely on the mean squared error (MSE) criterion, making them vulnerable to external acceleration and magnetic interference. To address this issue, we demonstrate that the multi-kernel correntropy loss (MKCL) is an optimal objective function for maximum likelihood estimation (MLE) when the noise follows a type of heavytailed distribution. In certain situations, the estimation error of the MKCL is bounded even in the presence of arbitrarily large outliers. By replacing the standard MSE cost function with MKCL, we develop the CGD and CDOE algorithms. We evaluate the effectiveness of our proposed methods by comparing them with existing algorithms in various situations. Experimental results indicate that our proposed methods (CGD and CDOE) outperform their conventional counterparts (GD and DOE), especially when faced with external acceleration and magnetic disturbances. Furthermore, the new algorithms demonstrate significantly lower computational complexity than Kalman filterbased approaches, making them suitable for applications with low-cost microprocessors.

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“…To derive an absolute upper error bound for the MKCL, we consider the scalar case of (6). In this scenario,…”

Section: B Estimation Of a Constant Vectormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Kernel Maximum Correntropy Kalman Filter for Orientation Estimation

Li,

Shi

et al. 2022

IEEE Robot. Autom. Lett.

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“…Thus, we propose to leverage 4 sensors to avoid the inconvenience of wearing multiple sensors, decrease cost and facility requirements, and achieve human motion tracking with good usability [46]. Considering that IMUs are easily affected by electromagnetic interference [11] and less flexible as they require a drift-avoidance configuration that prevents position deviation and inaccurate direction measurement [2,33], we employ four flexible sensors to track human motion.…”

Section: Introductionmentioning

confidence: 99%

Full-body Human Motion Reconstruction with Sparse Joint Tracking Using Flexible Sensors

Chen

Jiang

Zhan

et al. 2023

ACM Trans. Multimedia Comput. Commun. Appl.

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Human motion tracking is a fundamental building block for various applications including computer animation, human-computer interaction, healthcare, etc. To reduce the burden of wearing multiple sensors, human motion prediction from sparse sensor inputs has become a hot topic in human motion tracking. However, such predictions are non-trivial as i) the widely adopted data-driven approaches can easily collapse to average poses. ii) the predicted motions contain unnatural jitters. In this work, we address the aforementioned issues by proposing a novel framework which can accurately predict the human joint moving angles from the signals of only four flexible sensors, thereby achieving the tracking of human joints in multi-degrees of freedom. Specifically, we mitigate the collapse to average poses by implementing the model with a Bi-LSTM neural network that makes full use of short-time sequence information; we reduce jitters by adding a median pooling layer to the network, which smooths consecutive motions. Although being bio-compatible and ideal for improving the wearing experience, the flexible sensors are prone to aging which increases prediction errors. Observing that the aging of flexible sensors usually results in drifts of their resistance ranges, we further propose a novel dynamic calibration technique to rescale sensor ranges, which further improves the prediction accuracy. Experimental results show that our method achieves a low and stable tracking error of 4.51 degrees across different motion types with only four sensors.

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“…More specifically for TAVI and despite its advantages over AVR, its main technical limitation is the loss of natural tactile sensing. Especially in robot-assisted surgeries, e.g., with the system proposed in [ 6 ], the loss of tactile feedback is shown to adversely affect the ability of surgeons in estimation of tool–tissue interaction force or tissue distinction [ 7 , 8 ]. Consequently, surgeons are less situationally aware of the surgical site during TAVI procedures, which may result in risky surgical maneuvers and irreversible or catastrophic trauma to the patient.…”

Section: Introductionmentioning

confidence: 99%

Optical Fiber Array Sensor for Force Estimation and Localization in TAVI Procedure: Design, Modeling, Analysis and Validation

Bandari

Dargahi

Packirisamy

2021

Sensors

Self Cite

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Transcatheter aortic valve implantation has shown superior clinical outcomes compared to open aortic valve replacement surgery. The loss of the natural sense of touch, inherited from its minimally invasive nature, could lead to misplacement of the valve in the aortic annulus. In this study, a cylindrical optical fiber sensor is proposed to be integrated with valve delivery catheters. The proposed sensor works based on intensity modulation principle and is capable of measuring and localizing lateral force. The proposed sensor was constituted of an array of optical fibers embedded on a rigid substrate and covered by a flexible shell. The optical fibers were modeled as Euler–Bernoulli beams with both-end fixed boundary conditions. To study the sensing principle, a parametric finite element model of the sensor with lateral point loads was developed and the deflection of the optical fibers, as the determinant of light intensity modulation was analyzed. Moreover, the sensor was fabricated, and a set of experiments were performed to study the performance of the sensor in lateral force measurement and localization. The results showed that the transmitted light intensity decreased up to 24% for an external force of 1 N. Additionally, the results showed the same trend between the simulation predictions and experimental results. The proposed sensor was sensitive to the magnitude and position of the external force which shows its capability for lateral force measurement and localization.

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Integral-Free Spatial Orientation Estimation Method and Wearable Rotation Measurement Device for Robot-Assisted Catheter Intervention

Cited by 17 publications

References 41 publications

Multi-Kernel Maximum Correntropy Kalman Filter for Orientation Estimation

Multi-Kernel Maximum Correntropy Kalman Filter for Orientation Estimation

Full-body Human Motion Reconstruction with Sparse Joint Tracking Using Flexible Sensors

Optical Fiber Array Sensor for Force Estimation and Localization in TAVI Procedure: Design, Modeling, Analysis and Validation

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