Multidimensional Modeling of Physiological Tremor for Active Compensation in Handheld Surgical Robotics

Tatinati, Sivanagaraja,Nazarpour, Kianoush,Ang, Wei Tech,Veluvolu, Kalyana C. IEEE 2017 IEEE transactions on industrial electronics Vol.64 No.2

<P>Precision, robustness, dexterity, and intelligence are the design indices for current generation surgical robotics. To augment the required precision and dexterity into normal microsurgical work-flow, handheld robotic instruments are developed to compensate physiological tremor in real time. The hardware (sensors and actuators) and software (causal linear filters) employed for tremor identification and filtering introduces time-varying unknown phase delay that adversely affects the device performance. The current techniques that focus on three-dimensions (3-D) tip position control involves modeling and canceling the tremor in three axes (x-, y-, and z-axes) separately. Our analysis with the tremor recorded from surgeons and novice subjects shows that there exists significant correlation in tremor across the dimensions. Based on this, a new multidimensional modeling approach based on extreme learning machines is proposed in this paper to correct the phase delay and to accurately model 3-D tremor simultaneously. Proposed method is evaluated through both simulations and experiments. Comparison with the state-of-the art techniques highlight the suitability and better performance of the proposed approach for tremor compensation in handheld surgical robotics.</P>

Multistep Prediction of Physiological Tremor Based on Machine Learning for Robotics Assisted Microsurgery

Tatinati, Sivanagaraja,Veluvolu, Kalyana C.,Wei Tech Ang IEEE 2015 IEEE transactions on cybernetics Vol.45 No.2

<P>For effective tremor compensation in robotics assisted hand-held device, accurate filtering of tremulous motion is necessary. The time-varying unknown phase delay that arises due to both software (filtering) and hardware (sensors) in these robotics instruments adversely affects the device performance. In this paper, moving window-based least squares support vector machines approach is formulated for multistep prediction of tremor to overcome the time-varying delay. This approach relies on the kernel-learning technique and does not require the knowledge of prediction horizon compared to the existing methods that require the delay to be known as a priori. The proposed method is evaluated through simulations and experiments with the tremor data recorded from surgeons and novice subjects. Comparison with the state-of-the-art techniques highlights the suitability and better performance of the proposed method.</P>

Autoregressive model with Kalman filter for Estimation of Physiological Tremor in Surgical Robotic Applications

Sivanagaraja Tatinati,K. C. Veluvolu,W. T. Ang 제어로봇시스템학회 2011 제어로봇시스템학회 국제학술대회 논문집 Vol.2011 No.10

In real-time implementation computational complexity plays vital role. This paper focuses on adaptive signal processing of physiological hand tremor for tremor cancellation in robotic devices. The physiological tremor is modelled with AR(3) process that has less computational complexity compared to other model based existing methods. In this paper, filter coefficients are updated with Kalman filter to improve the performance. The existing method AR-LMS and the improved method AR-Kalman are implemented in real-time for tremor compensation. A comparative study is conducted on the algorithms with the tremor data from microsurgeons and novice subjects. Experimental results shows that the proposed method AR with Kalman filter improves the accuracy by at least 10% in real-time compared to AR with LMS.

A Quaternion Weighted Fourier Linear Combiner for Modeling Physiological Tremor

Adhikari, Kabita,Tatinati, Sivanagaraja,Ang, Wei Tech,Veluvolu, Kalyana C.,Nazarpour, Kianoush IEEE 2016 IEEE Transactions on Biomedical Engineering Vol.63 No.11

<P>Goal: This paper offers a new approach to model physiological tremor aiming at attenuating undesired vibrations of the tip of microsurgical instruments. Method: Several tremor modeling algorithms, such as the weighted Fourier linear combiner (wFLC), have proved effective. They, however, treat the three-dimensional (3-D) tremor signal as three independent 1-D signals in the x-, y-, and z-axes. In addition, the force f by which a surgeon holds the instrument has never been taken into account in modeling. Hence, conventional algorithms are inherently blind to any potential multidimensional couplings. Results: We first show that there exists statistically significant subject-and task-dependent coherence between data in the x-, y-, z-, and f-axes. We hypothesize that a filter that models the tremor in 4-D (x, y, z, and f) yields a more accurate model of tremor. We, therefore, developed a quaternion version of the wFLC algorithm and termed it QwFLC. We tested the proposed QwFLC algorithm with real physiological tremor data that were recorded from five novice subjects and four experienced microsurgeons. Although compared to wFLC, QwFLC requires six times larger computational resources, we showed that QwFLC can improve the modeling by up to 67% and that the improvement is proportional to the total coherence between the tremor in xyz and the force signal. Conclusion: By taking into account interactions of the 3-D tremor and the force data, the tremor modeling performance enhances significantly.</P>

Multistep Prediction of Physiological Tremor for Surgical Robotics Applications

Veluvolu, Kalyana C.,Tatinati, Sivanagaraja,Hong, Sun-Mog,Ang, Wei Tech IEEE 2013 IEEE Transactions on Biomedical Engineering Vol.60 No.11

<P>Accurate canceling of physiological tremor is extremely important in robotics-assisted surgical instruments/procedures. The performance of robotics-based hand-held surgical devices degrades in real time due to the presence of phase delay in sensors (hardware) and filtering (software) processes. Effective tremor compensation requires zero-phase lag in filtering process so that the filtered tremor signal can be used to regenerate an opposing motion in real time. Delay as small as 20 ms degrades the performance of human-machine interference. To overcome this phase delay, we employ multistep prediction in this paper. Combined with the existing tremor estimation methods, the procedure improves the overall accuracy by 60% for tremor estimation compared to single-step prediction methods in the presence of phase delay. Experimental results with developed methods for 1-DOF tremor estimation highlight the improvement.</P>

Performance Comparison of Sliding Mode Observers for Back EMFs based Speed Estimation in PMSM

Suneel K. Kommuri,Jagat J. Rath,Kalyana C. Veluvolu,M. Defoort,S. Tatinati 제어로봇시스템학회 2015 제어로봇시스템학회 국제학술대회 논문집 Vol.2015 No.10

This paper proposes a second-order high-speed sliding mode (SHSM) observer and compares the performance with the recent higher-order sliding mode (HOSM) observer for the problem of sensorless speed estimation in the permanent magnet synchronous motor (PMSM). In which, a sigmoid function is substituted for the signum function with a variable boundary layer. A SHSM observer is proposed to provide the estimation of back electro motive forces (EMFs) that are treated as unknown inputs in the PMSM model. An accurate speed estimate of PMSM can be algebraically computed with the estimated back EMFs. The chattering phenomenon, that is commonly found in the sliding mode observers is well-reduced by replacing signum function with the sigmoid function, in comparison to the HOSM observer. Simulation results show the effectiveness of the proposed SHSM speed estimation method in comparison to the earlier HOSM observer in terms of good accuracy and chattering phenomenon.

An adaptive modified super-twisting sliding mode controller

J. J. Rath,Suneel K. Kommuri,Kalyana C. Veluvolu,M. Defoort,S. Tatinati 제어로봇시스템학회 2015 제어로봇시스템학회 국제학술대회 논문집 Vol.2015 No.10

A robust higher order sliding mode algorithm combining the merits of the modified super-twisting algorithm and the adaptive super-twisting algorithm has been proposed for a class of nonlinear uncertain systems in this article. For a class of linearly growing perturbations whose upper bounds are not known, the convergence of the sliding dynamics in finite time is proven. To illustrate the effectiveness of the proposed approach, an adaptive robust controller based on the proposed algorithm is developed for the nonlinear active suspension system faced with perturbations from the road surface. Simulation results provided demonstrate the effectiveness of the proposed approach.