Physiological Organ Motion Prediction and Compensation Based on Multirate, Delayed, and Unregistered Measurements in Robot-Assisted Surgery and Therapy

Bowthorpe, Meaghan; Tavakoli, Mahdi

doi:10.1109/tmech.2015.2482391

Cited by 16 publications

(3 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Before discussing robot controllers, a vital issue is to measure the moving organ position in real time. To this end, many image-based sensors have been widely used in research such as high-speed camera/laparoscopy (Nakajima et al, 2014 ), X-ray fluoroscopy (Ma et al, 2020 ), computed tomography (CT) (Su et al, 2013 ), magnetic resonance imaging (MRI) (Yang et al, 2014 ), positron emission tomography (PET) (Bettinardi et al, 2013 ), and ultrasound imaging (US) (Bowthorpe and Tavakoli, 2016a , b ; Diodato et al, 2018 ). To get performance, hybrid imaging systems are also developed to measure precise organ motion including MRI/US imaging (Celicanin et al, 2018 ), MRI/CT imaging (Neumann et al, 2017 ), PET/CT imaging (Bettinardi et al, 2013 ; Pepin et al, 2014 ), and PET/MRI (Kolbitsch et al, 2018 ).…”

Section: Teleoperation For Organ Motion Compensationmentioning

confidence: 99%

“…Predictive feedback controllers not only need the organ's current position but also take the tracking error into account. By considering the physiological organ motion as periodic disturbances, controllers such as model predictive controllers (MPCs) (Gangloff et al, 2006 ; Vrooijink et al, 2017 ), Smith predictor-based controllers (Bowthorpe et al, 2013 ; Bowthorpe and Tavakoli, 2016a ), generalized predictive controllers (GPCs) (Bowthorpe and Tavakoli, 2016b ), and repetitive-GPC (R-GPC) (Ginhoux et al, 2005 ) were used. As these methods rely on the known organ motion model, the robustness of the system to irregular organ motion is challenging.…”

Section: Teleoperation For Organ Motion Compensationmentioning

confidence: 99%

See 1 more Smart Citation

COVID-19 Pandemic Spurs Medical Telerobotic Systems: A Survey of Applications Requiring Physiological Organ Motion Compensation

Cheng

Tavakoli

2020

Front. Robot. AI

Self Cite

View full text Add to dashboard Cite

The coronavirus disease 2019 (COVID-19) pandemic has resulted in public health interventions such as physical distancing restrictions to limit the spread and transmission of the novel coronavirus, causing significant effects on the delivery of physical healthcare procedures worldwide. The unprecedented pandemic spurs strong demand for intelligent robotic systems in healthcare. In particular, medical telerobotic systems can play a positive role in the provision of telemedicine to both COVID-19 and non-COVID-19 patients. Different from typical studies on medical teleoperation that consider problems such as time delay and information loss in long-distance communication, this survey addresses the consequences of physiological organ motion when using teleoperation systems to create physical distancing between clinicians and patients in the COVID-19 era. We focus on the control-theoretic approaches that have been developed to address inherent robot control issues associated with organ motion. The state-of-the-art telerobotic systems and their applications in COVID-19 healthcare delivery are reviewed, and possible future directions are outlined.

show abstract

Section: Teleoperation For Organ Motion Compensationmentioning

confidence: 99%

Section: Teleoperation For Organ Motion Compensationmentioning

confidence: 99%

COVID-19 Pandemic Spurs Medical Telerobotic Systems: A Survey of Applications Requiring Physiological Organ Motion Compensation

Cheng

Tavakoli

2020

Front. Robot. AI

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, these controllers are inadequate in the performance due to their phase lag caused by the feedback measurement [ 2 ]. In the literature [ 8 , 12 – 18 ], researchers proposed higher effective model predictive control approaches by importing the prediction of the future heart motion in the feed-forward loop as in [ 2 , 12 , 16 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Beating Heart Motion Accurate Prediction Method Based on Interactive Multiple Model: An Information Fusion Approach

Xie

2017

BioMed Research International

View full text Add to dashboard Cite

Robot-assisted motion compensated beating heart surgery has the advantage over the conventional Coronary Artery Bypass Graft (CABG) in terms of reduced trauma to the surrounding structures that leads to shortened recovery time. The severe nonlinear and diverse nature of irregular heart rhythm causes enormous difficulty for the robot to realize the clinic requirements, especially under arrhythmias. In this paper, we propose a fusion prediction framework based on Interactive Multiple Model (IMM) estimator, allowing each model to cover a distinguishing feature of the heart motion in underlying dynamics. We find that, at normal state, the nonlinearity of the heart motion with slow time-variant changing dominates the beating process. When an arrhythmia occurs, the irregularity mode, the fast uncertainties with random patterns become the leading factor of the heart motion. We deal with prediction problem in the case of arrhythmias by estimating the state with two behavior modes which can adaptively “switch” from one to the other. Also, we employed the signal quality index to adaptively determine the switch transition probability in the framework of IMM. We conduct comparative experiments to evaluate the proposed approach with four distinguished datasets. The test results indicate that the new proposed approach reduces prediction errors significantly.

show abstract

State observation and feedback control in robotic systems for therapy and surgery

Fallahi

Cheng

Tavakoli

2020

Control Systems Design of Bio-Robotics and Bio-Mechatronics With Advanced Applications

View full text Add to dashboard Cite

Physiological Organ Motion Prediction and Compensation Based on Multirate, Delayed, and Unregistered Measurements in Robot-Assisted Surgery and Therapy

Cited by 16 publications

References 28 publications

COVID-19 Pandemic Spurs Medical Telerobotic Systems: A Survey of Applications Requiring Physiological Organ Motion Compensation

COVID-19 Pandemic Spurs Medical Telerobotic Systems: A Survey of Applications Requiring Physiological Organ Motion Compensation

Beating Heart Motion Accurate Prediction Method Based on Interactive Multiple Model: An Information Fusion Approach

State observation and feedback control in robotic systems for therapy and surgery

Contact Info

Product

Resources

About