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>

Evolutionary Algorithm Based Feature Optimization for Multi-Channel EEG Classification

Wang, Yubo,Veluvolu, Kalyana C. Frontiers Media S.A. 2017 Frontiers in neuroscience Vol.11 No.-

<P>The most BCI systems that rely on EEG signals employ Fourier based methods for time-frequency decomposition for feature extraction. The band-limited multiple Fourier linear combiner is well-suited for such band-limited signals due to its real-time applicability. Despite the improved performance of these techniques in two channel settings, its application in multiple-channel EEG is not straightforward and challenging. As more channels are available, a spatial filter will be required to eliminate the noise and preserve the required useful information. Moreover, multiple-channel EEG also adds the high dimensionality to the frequency feature space. Feature selection will be required to stabilize the performance of the classifier. In this paper, we develop a new method based on Evolutionary Algorithm (EA) to solve these two problems simultaneously. The real-valued EA encodes both the spatial filter estimates and the feature selection into its solution and optimizes it with respect to the classification error. Three Fourier based designs are tested in this paper. Our results show that the combination of Fourier based method with covariance matrix adaptation evolution strategy (CMA-ES) has the best overall performance.</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>

A Motion Planning Framework with Connectivity Management for Multiple Cooperative Robots

Defoort, Michael,Veluvolu, Kalyana C. Springer-Verlag 2014 JOURNAL OF INTELLIGENT AND ROBOTIC SYSTEMS Vol.75 No.2

Active Control of Nonlinear Suspension System Using Modified Adaptive Supertwisting Controller

Rath, Jagat J.,Veluvolu, Kalyana C.,Defoort, Michael Hindawi Limited 2015 Discrete dynamics in nature and society Vol.2015 No.-

<P>The suspension system is faced with nonlinearities from the spring, damper, and external excitations from the road surface. The objective of any control action provided to the suspension is to improve ride comfort while ensuring road holding for the vehicle. In this work, a robust higher order sliding mode algorithm combining the merits of the modified supertwisting algorithm and the adaptive supertwisting algorithm has been proposed for the nonlinear active suspension system. The proposed controller is robust to linearly growing perturbations and bounded uncertainties. Simulations have been performed for different classes of road excitations and the results are presented.</P>

Higher-Order Sliding Mode Observer for Speed and Position Estimation in PMSM

Kommuri, Suneel K.,Veluvolu, Kalyana C.,Defoort, M.,Soh, Yeng C. Hindawi Limited 2014 Mathematical problems in engineering Vol.2014 No.-

<P>This paper presents a speed and position estimation method for the permanent magnet synchronous motor (PMSM) based on higher-order sliding mode (HOSM) observer. The back electromotive forces (EMFs) in the PMSM are treated as unknown inputs and are estimated with the HOSM observer without the need of low-pass filter and phase compensation modules. With the estimation of back EMFs, an accurate estimation of speed and rotor position can be obtained. Further, the proposed method completely eliminates chattering. Experimental results with a 26 W three-phase PMSM demonstrate the effectiveness of the proposed method.</P>

Adaptive Estimation of bandlimited Physiological Signals in Real-time

Fan Zhe,Wang Yubo,Kalyana C. Veluvolu 제어로봇시스템학회 2011 제어로봇시스템학회 국제학술대회 논문집 Vol.2011 No.10

Accurate estimation of physiological bandlimited signal is extremely important in various biomedical applications. This paper focuses on developing a single stage robust algorithm for accurate bandlimited signal estimation in real-time applications. In this paper, the existing method bandlimited multiple Fourier linear combiner(BMFLC) with least-mean square(LMS) is improved by replacing LMS with recursive leat square (RLS). A comparative study is conducted on physiological tremor data and EEG data from subjects. Our results showed that the BMFLC-RLS performed better than the existing algorithms for tremor and EEG signal estimation.

Differential Evolution with Population and Strategy Parameter Adaptation

Gonuguntla, V.,Mallipeddi, R.,Veluvolu, Kalyana C. Hindawi Limited 2015 Mathematical problems in engineering Vol.2015 No.-

<P>Differential evolution (DE) is simple and effective in solving numerous real-world global optimization problems. However, its effectiveness critically depends on the appropriate setting of population size and strategy parameters. Therefore, to obtain optimal performance the time-consuming preliminary tuning of parameters is needed. Recently, different strategy parameter adaptation techniques, which can automatically update the parameters to appropriate values to suit the characteristics of optimization problems, have been proposed. However, most of the works do not control the adaptation of the population size. In addition, they try to adapt each strategy parameters individually but do not take into account the interaction between the parameters that are being adapted. In this paper, we introduce a DE algorithm where both strategy parameters are self-adapted taking into account the parameter dependencies by means of a multivariate probabilistic technique based on Gaussian Adaptation working on the parameter space. In addition, the proposed DE algorithm starts by sampling a huge number of sample solutions in the search space and in each generation a constant number of individuals from huge sample set are adaptively selected to form the population that evolves. The proposed algorithm is evaluated on 14 benchmark problems of CEC 2005 with different dimensionality.</P>

Event-Related Functional Network Identification: Application to EEG Classification

Gonuguntla, Venkateswarlu,Yubo Wang,Veluvolu, Kalyana C. IEEE 2016 IEEE journal of selected topics in signal processi Vol.10 No.7

<P>Recent works on brain functional analysis have accentuated the importance of distributed functional networks and synchronized activity between networks in mediating cognitive functions. The network perspective is important to relate mechanisms of brain functions and the basis for classifying brain states. In this work, the network patterns related to neural tasks based on synchronization measure phase-locking value (PLV) in an electroencephalogram (EEG) are analyzed. Based on network dissimilarities between the rest and motor imagery tasks, important nodes and channel pairs corresponding to motor tasks are identified. A framework is developed to identify these most reactive channel pairs that form the subject-specific functional network. The identified functional network corresponding to tasks demonstrate significant PLV variation in line with the experiment protocol. With the selection of subject-specific reactive band, these channel pairs provide even higher variation corresponding to tasks. To demonstrate the potential of the developed framework to brain-computer interface, identified network patterns are employed as features for classification of tasks. Analysis performed with the two classes (left-hand and right-hand motor imagery EEG data) showed that the proposed approach yielded better classification results compared to earlier band-power based approaches for single trial analysis.</P>

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>