Tremor acquisition and reduction for robotic surgical applications

Pragya Gopal*, Sanjeev Kumar, Sudesh Bachhal, Amod Kumar

CSIR-Central Scientific Instruments Organization, Chandigarh-16003, India. *e-mail: pragyagopal9@gmail.com

Abstract: Tremor is the major cause for human imprecision during microsurgery. It corrupts accurate voluntary motion causing an unwanted disturbance or noise. Even though there have been many developments made for tremor measurements, still the challenges remain to make these measurements robust and accurate in real-time working environment. Hence, need is to design a system and to develop an algorithm that can track and nullify these tremors while still responding to the movements/commands of the surgeon's hands. In the research work interest lies in the area of signal acquisition which includes design of the device to acquire the hand motion, differentiate between the voluntary motion and the motion incurred due to physiological tremor. Signal processing is done with focus on filtering of tremor signal for tremor compensation in robotic surgical applications. The present work deals with the issues to measure the hand tremor at the surgeon handle and to reduce at the far end at tool tip. Keywords: Tremor, real-time, signal processing, signal acquisition, tremor compensation, microsurgery, robotic surgical applications, physiological tremor.

I. INTRODUCTION Robotic Surgery is a type of surgery in which the surgeon operates the patient by manipulating the arms of a robotic system. Robotic arms not only mimic the surgeon’s hand movements, but also scale down the movement, allowing the surgeon to easily make precise and small cuts. The whole system is a computerized one with motorized manipulators capable of interacting with the environment. It contains sensors and associated processing to provide feedback data on the robot’s current position based on which next action of the arm would be planned. A key advantage of robotic surgery includes small incision, high accuracy and ability to repeat identical motions. In surgical procedures particularly in case of microsurgery, the human hand imposes certain limitations in accurately positioning the tip of surgical instrumentation. Any errors in the motion of the hand make microsurgical procedures difficult and involuntary motions such as hand tremors can make some procedures significantly difficult to perform. The most familiar source of involuntary motion is physiological tremor. Real-time compensation of physiological tremor is, therefore, necessary to assist surgeons to precisely position and manipulate the tool-tip to accurately perform a microsurgery. The situation of tremor can be avoided by measuring the hand tremor of the surgeon and reduced at the tool side. Tremor is an involuntary, approximately rhythmic, oscillatory movement produced when muscles repeatedly contract or relax [1]. It has relatively fixed frequency and amplitude. Tremor usually ranges between 3Hz to 18Hz. Quantification of tremor amplitude and frequency are fundamental in robotic surgery where tremor is produced in surgeon’s hand due to the stress or fatigue. Need is to

design a system which can detect and filter out any tremors present in the surgeon's hand movements, so that they are not duplicated robotically and later reduce or suppress it at the tool end. Some of the cases of surgeries which last for hours, the surgeon's arms get tired resulting in tremors due to fatigue which results in false cuts. Hence, an accurate filtering of physiological hand tremor is extremely important for compensation in robotics assisted microsurgical instruments/procedures. It is a technical challenge to cancel or supress the tremor signals in real-time. Thus the emphasis has been given to design a system to measure the hand tremor at the surgeon handle and to develop the algorithms to cancel or suppress the tremor signals at the far end at tool tip.

I. MATERIALS AND METHODS Aim was to develop a system that can acquire tremor signals at surgeon’s handle for analysis and processing of tremor signals. Two small experimental set up were made. One comprising of a joystick kind handle for obtaining the three direction motion over which accelerometer was placed as shown in Figure 1(a) and another comprising of multi-turn sensitive potentiometer on which accelerometer was mounted as shown in Figure 1(b). Both these set up were used to acquire stationary motion of tremor and tremor with voluntary motion respectively. Acquisition of tremor signals was done in Labview environment and processed in Matlab.

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Figure 1 (a): Experimental set up (stationary test)

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2013 International Conference on Advanced Electronic Systems (ICAES)

I. SIGNAL ACQUISITION AND PROCESSING

Signal Acquisition: In this part, the tremor data has been acquired from the subjects using accelerometer with the designed experimental set ups and the output were fed to the Data Acquisition Card (DAQ) which was interfaced with Labview software (Figure 2). Sampling frequency selected for recording data was 100Hz. Recorded signal values were stored in excel sheet and later were imported to MATALB for parameters analysis and the filtering of tremor motion from the voluntary motion. GUI was developed in Labview environment for acquisition purpose of the tremor signal in real time. Tremor data was recorded for 5 volunteers for all 3 axes(x, y, z). Acquired tremor signal for these three axes were recorded and displayed in the designed GUI as shown in Figure 4. The last waveform in the GUI depicts the voluntary motion with tremor superimposed over it which was obtained by using the tracking experimental set up made as mentioned earlier. The values of corresponding tremor data were stored in excel sheet which was later imported to MATLAB. Based on these data further analysis were performed. Also corresponding 10 epoch signals of each axis were obtained for which various parameters were calculated. Signal Processing: Identification and computation of various parameters of tremor signal in time and frequency domain along with the analysis of individual parameters were performed. Also processing of acquired tremor was achieved efficiently in MATLAB. The compensation of tremor was achieved which involves reduction or cancellation of tremors using standardized algorithms (Figure 3). The acquired signal of all x, y, z axes were imported to MATLAB and then 10 epochs of 5 seconds were made for each axis of tremor signal and its parameters were calculated which includes power spectrum, frequency and amplitude calculation, spectral edge frequency and band powers. These parameters were studied and analysed to understand the characteristics and behaviour of tremor signal. In other case, tremor data and voluntary motion were recorded using potentiometer experimental set up. Since the frequency range of tremor is different from the intended motion, hence different filters were designed and implemented to compensate the tremor motion in the voluntary motion.

I. RESULTS Graphical user Interface (GUI) was developed in Labview for acquisition of the tremor signal in real time environment as shown in Figure 4 where tremor data and voluntary motion of the subjects were recorded and displayed.

Calculation of parameters showed that there were several typically dominant tremor frequencies present in power spectrum of x-axis tremor signal in healthy subjects. This could occur due to variation of more than one frequency components in tremor and different causes or sources of tremor. Presence of 10 Hz peak frequency in y and z axes in all 5 subjects observed were correlated to firing rates of motor unit which explained the increase in tremor amplitudes which was observed during stressed or fatigued conditions. Band powers calculated showed that in x and y axes, the power amplitude always lie in lower magnitude range for the female subjects as compared to male subjects. This signified that gender of a person could also be one of the factors in tremor characterization. Figure 5(a) shows the tremor signal obtained from accelerometer of x-axis and Figure 5(b) is the recorded voluntary motion with tremor. Then using zero-phase lowpass filtering tremor is compensated as shown in Figure 5(c).Since the tremor frequency range is known, using highpass filter tremor signal was extracted from voluntary motion shown in Figure 5(d). Figure 5(e) shows the result of the processing of tremor signal.

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Figure 5(a): Tremor in x-axis

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II. CONCLUSION AND DISCUSSIONS

The developed system was tested and can solve the need for an effective and simple tremor measuring instrument fit for wide distribution. Standardized algorithms were implemented and validated for real-time tremor compensation which has its application like in microsurgery. The main objective was to develop tremor acquisition and cancellation system that would distinguish between undesired and intended motion, and compensates the undesired motion accurately in real-time for robotics applications. The project aims at developing a real-time tremor filtering methods/technology that would achieve greater efficiency for tremor cancellation in handheld instruments. The focus was on designing algorithms for estimation/filtering of physiological tremor for surgical robotics applications. The basic idea behind the tremor removing algorithms is to predict the frequency and amplitude of human hand tremors. Given these predictions the algorithm can remove the displacements caused by the tremors, hence increases the accuracy.

ACKNOWLEDGEMENT The author is thankful to Council of Scientific and Industrial Research (CSIR), India for sponsoring the project.

REFERENCES

[1] K.C. Veluvolu ,“Double adaptive bandlimited multiple Fourier linear combiner for real-time estimation/filtering of physiological tremor”, Biomedical Signal Processing and Control Journal, June 2009.

[2] Abhijit Saxena , “An Active Handheld Tremor Cancellation Device”, ECE Grad Symp, UWO, June 2011.

[3] Kalyana C. Veluvolu, “Estimation of Physiological Tremor from Accelerometers for Real-Time Applications”, Sensors, 11, 3020-3036, 2011.

[4] Wie tech Ang, Pradeep K.Khosla,”An intelligent hand-held microsurgical instrument for improved accuracy”, Proceedings of Third international Conference on medical computing and computer assisted intervention, Pittsburgh, Pa., October 11-14, 2000.

[5] M. Gomez-Blanco, C. Riviere, and P.hosla, “Sensing hand tremor in a vitreoretinal microsurgical instrument,” Tech. Rep. CMU-RI-TR-99-39, Robotics Institute, Pittsburgh, PA, December 1999.

[6] C. N. Riviere, R. S. Rader, and N. V. Thakor, “Adaptive cancelling of physiological tremor for improved precision in microsurgery,” IEEE Trans. Biomed. Eng., vol. 45, no. 7, pp. 839–846, 1998.57

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3122013 International Conference on Advanced Electronic Systems