scholarly journals Real-Time Nonlinear Characterization of Soft Tissue Mechanical Properties

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Jaehyun Shin ◽  
Yongmin Zhong ◽  
Chengfan Gu
Keyword(s):  
Soft Tissue ◽  
Adaptive Filtering ◽  
Tissue Characterization ◽  
Haptic Feedback ◽  
Unscented Kalman Filter ◽  
Weighting Factor ◽  
Measurement Noise ◽  
Robotic Assisted ◽  
Robotic Assisted Surgery ◽  
Noise Statistics

Online soft tissue characterization is important for robotic-assisted minimally invasive surgery to achieve precise and stable robotic control with haptic feedback. This paper presents a new nonlinear recursive adaptive filtering methodology for online nonlinear soft tissue characterization. An adaptive unscented Kalman filter is developed based on the Hunt-Crossley model by windowing approximation to online estimate system and measurement noise covariances. To improve the accuracy of noise covariance estimations, a recursive formulation is subsequently developed for estimation of system and measurement noise covariances by introducing a weighting factor. This weighting factor is further modified to accommodate noise statistics of large variation which could be caused by rupture events and geometric discontinuities in robotic-assisted surgery. Simulations, experiments, and comparison analyses demonstrate that the proposed nonlinear recursive adaptive filtering methodology can characterize soft tissue parameters in the presence of system or measurement noise statistics in both small and large variations for robotic-assisted surgery. The proposed methodology can effectively estimate soft tissue parameters under system and measurement noises in both small and large variations, leading to improved filtering accuracy and robustness in comparison with UKF.

ADAPTIVE UNSCENTED KALMAN FILTER FOR ONLINE SOFT TISSUES CHARACTERIZATION

2017 ◽  
Vol 17 (07) ◽  
pp. 1740014
Author(s):  
JAEHYUN SHIN ◽  
YONGMIN ZHONG ◽  
JULIAN SMITH ◽  
CHENGFAN GU
Keyword(s):  
Kalman Filter ◽  
Soft Tissue ◽  
Tissue Characterization ◽  
Haptic Feedback ◽  
Unscented Kalman Filter ◽  
Recursive Estimation ◽  
Robotic Assisted ◽  
System Noise ◽  
Adaptive Unscented Kalman Filter ◽  
Noise Covariance

Online soft tissue characterization is important for robotic-assisted minimally invasive surgery to achieve precise and stable robotic control with haptic feedback. This paper presents a new adaptive unscented Kalman filter based on the nonlinear Hunt–Crossley model for online soft tissue characterization without requiring the characteristics of system noise. This filter incorporates the concept of Sage windowing in the traditional unscented Kalman filter to adaptively estimate system noise covariance using predicted residuals within a time window. In order to account for the inherent relationship between the current and previous states of soft tissue deformation involved in robotic-assisted surgery and improve the estimation performance, a recursive estimation of system noise covariance is further constructed by introducing a fading scaling factor to control the contributions between noise covariance estimations at current and previous time points. The proposed adaptive unscented Kalman filter overcomes the limitation of the traditional unscented Kalman filter in requiring the characteristics of system noise. Simulations and comparisons show the efficacy of the suggested nonlinear adaptive unscented Kalman filter for online soft tissue characterization.

A NEW PARAMETER ESTIMATION METHOD FOR ONLINE SOFT TISSUE CHARACTERIZATION

2016 ◽  
Vol 16 (08) ◽  
pp. 1640019 ◽  
Author(s):  
JAEHYUN SHIN ◽  
YONGMIN ZHONG ◽  
JULIAN SMITH ◽  
CHENGFAN GU
Keyword(s):  
Soft Tissue ◽  
Linear Regression ◽  
Tissue Characterization ◽  
Soft Tissues ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Regression Method ◽  
Linear Regression Method ◽  
Non Linear

Dynamic soft tissue characterization is of importance to robotic-assisted minimally invasive surgery. The traditional linear regression method is unsuited to handle the non-linear Hunt–Crossley (HC) model and its linearization process involves a linearization error. This paper presents a new non-linear estimation method for dynamic characterization of mechanical properties of soft tissues. In order to deal with non-linear and dynamic conditions involved in soft tissue characterization, this method improves the non-linearity and dynamics of the HC model by treating parameter [Formula: see text] as independent variable. Based on this, an unscented Kalman filter is developed for online estimation of soft tissue parameters. Simulations and comparison analysis demonstrate that the proposed method is able to estimate mechanical parameters for both homogeneous tissues and heterogeneous and multi-layer tissues, and the achieved performance is much better than that of the linear regression method.

Recursive Calibration of Industrial Manipulators by Adaptive Filtering

1995 ◽  
Vol 117 (3) ◽  
pp. 406-411
Author(s):  
Y. L. Yao ◽  
S. M. Wu
Keyword(s):  
Adaptive Filtering ◽  
Estimation Error ◽  
Laser Interferometry ◽  
Measurement Noise ◽  
Error Covariance ◽  
Noise Statistics ◽  
Three Degree Of Freedom ◽  
The Given ◽  
Gantry Robot ◽  
Kinematic Errors

The calibration scheme of robot forward kinematics presented in this paper has a number of features. Firstly, robot kinematic errors are modeled in a recursive format and as such, the number of measurements that need to be taken for calibration can be determined by studying the rate of convergence of estimation error covariance. Secondly, a simplified adaptive filtering algorithm is used to deal with unknown measurement noise statistics and unknown robot motion repeatability characteristics in estimating the kinematic errors. Thirdly, a laser interferometry system is used to measure positions of a robot end-effector in world coordinates. The measurement system was implemented in experiments involving a three degree-of-freedom gantry robot. The adaptive filtering of the experimental data identified 0.5 to 1.5 percent errors in representative kinematic parameters of the given robot by taking into account measurement noise and robot repeatability.

scholarly journals Random Weighting, Strong Tracking, and Unscented Kalman Filter for Soft Tissue Characterization

Sensors ◽  
2018 ◽  
Vol 18 (5) ◽  
pp. 1650 ◽  
Author(s):  
Jaehyun Shin ◽  
Yongmin Zhong ◽  
Denny Oetomo ◽  
Chengfan Gu
Keyword(s):  
Kalman Filter ◽  
Soft Tissue ◽  
Tissue Characterization ◽  
Unscented Kalman Filter ◽  
Random Weighting

scholarly journals Detailed cost of robotic-assisted surgery in the Australian public health sector: from implementation to a multi-specialty caseload

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Kate McBride ◽  
Daniel Steffens ◽  
Christina Stanislaus ◽  
Michael Solomon ◽  
Teresa Anderson ◽  
...  
Keyword(s):  
Public Sector ◽  
Health Sector ◽  
Public Hospitals ◽  
Fixed Costs ◽  
Tertiary Referral Hospital ◽  
Robotic Assisted ◽  
The Public ◽  
Robotic Assisted Surgery ◽  
Specific Implementation ◽  
Theatre Utilisation

Abstract Background A barrier to the uptake of robotic-assisted surgery (RAS) continues to be the perceived high costs. A lack of detailed costing information has made it difficult for public hospitals in particular to determine whether use of the technology is justified. This study aims to provide a detailed description of the patient episode costs and the contribution of RAS specific costs for multiple specialties in the public sector. Methods A retrospective descriptive costing review of all RAS cases undertaken at a large public tertiary referral hospital in Sydney, Australia from August 2016 to December 2018 was completed. This included RAS cases within benign gynaecology, cardiothoracic, colorectal and urology, with the total costs described utilizing various inpatient costing data, and RAS specific implementation, maintenance and consumable costs. Results Of 211 RAS patients, substantial variation was found between specialties with the overall median cost per patient being $19,269 (Interquartile range (IQR): $15,445 to $32,199). The RAS specific costs were $8828 (46%) made up of fixed costs including $4691 (24%) implementation and $2290 (12%) maintenance, both of which are volume dependent; and $1848 (10%) RAS consumable costs. This was in the context of 37% robotic theatre utilisation. Conclusions There is considerable variation across surgical specialties for the cost of RAS. It is important to highlight the different cost components and drivers associated with a RAS program including its dependence on volume and how it fits within funding systems in the public sector.

scholarly journals Robotic natural orifice specimen extraction surgery versus traditional robotic-assisted surgery (NOTR) for patients with colorectal cancer: a study protocol for a randomized controlled trial

Trials ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Rui Luo ◽  
Fangfang Zheng ◽  
Haobo Zhang ◽  
Weiquan Zhu ◽  
Penghui He ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Randomized Controlled Trial ◽  
Controlled Trial ◽  
Natural Orifice ◽  
Specimen Extraction ◽  
Natural Orifice Specimen Extraction ◽  
Robotic Assisted ◽  
Randomized Controlled ◽  
Robotic Assisted Surgery ◽  
Secondary Outcomes

Abstract Background Natural orifice specimen extraction surgery for colorectal cancer has been introduced in order to reduce the abdominal incision, demonstrating major development potential in minimally invasive surgery. We are conducting this randomized controlled trial to assess whether robotic NOSES is non-inferior to traditional robotic-assisted surgery for patients with colorectal cancer in terms of primary and secondary outcomes. Method/design Accordingly, a prospective, open-label, randomized controlled, parallel-group, multicenter, and non-inferiority trial will be conducted to discuss the safety and efficacy of robotic natural orifice extraction surgery compared to traditional robotic-assisted surgery. Here, 550 estimated participants will be enrolled to have 80% power to detect differences with a one-sided significance level of 0.025 in consideration of the non-inferiority margin of 10%. The primary outcome is the incidence of surgical complications, which will be classified using the Clavien-Dindo system. Discussion This trial is expected to reveal whether robotic NOSES is non-inferior to traditional robotic-assisted surgery, which is of great significance in regard to the development of robotic NOSES for patients with colorectal cancer in the minimally invasive era. Furthermore, robotic NOSES is expected to exhibit superiority to traditional robotic-assisted surgery in terms of both primary and secondary outcomes. Trial registration ClinicalTrials.govNCT04230772. Registered on January 15, 2020.

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