The work conducted in this dissertation focuses on issues that are related to the accurate and real-time separation of physiological tremor components from the sensed motion. This dissertation mainly focused on developing new algorithms and techniques for accurate modeling and prediction of physiological tremor components ( the filtering and modeling system) for the hand-held instruments. The methods developed in the course of this dissertation were validated with the physiological tremor database collected from micro-surgeons and novice subjects. Further, the methods were experimentally validated with the bench tests conducted on hand-held instrument, iTrem.
Precision, robustness, dexterity, and intelligence are the design indices for current generation surgical robotics. In microsurgeries, physiological tremor – an intrinsic hand motion with amplitude of 100µm – is a major impediment for surgeons’ to perform delicate and fine motor tasks in sub-millimeter space. To augment the required precision and dexterity into normal microsurgical workflow by compensating the tremor in real-time, hand-held robotic instruments are developed. The working principle of a typical handheld instrument involves subsequent execution of three steps 1) sensing its own motion with inertial sensors, 2) filtering the involuntary motion from the sensed motion, and 3) actuate the surgical end-effectors (instrument tip) based on the filtered involuntary motions to compensate the tremor motion.
Generally, digital filters are employed to filter out the noise components and subsequently extract the tremor motion from the whole motion. As a result, a time-varying and unknown delay in the range of 20 to 200ms (depends on the variant of the filter) is introduced into the tremor compensation proceedings, which in turn, adversely affects the tremor compensation performance. Ideally, zero phase lag between the actual tremor and extracted tremor motion is essential for hand-held instruments. This motivates development of new and innovative signal processing solutions, which can enhance the performance of hand-held instruments, in practice. We believe the key to achieve this goal is to go beyond the paradigm of conventional linear modelling techniques, which is limited to least squares solutions. This book proposes several solutions to overcome the existing issues and propose new solutions based on machine learning techniques for correction of phase delay.
Table of Contents
1 Introduction
1.1 Context
1.2 Microsurgery and it’s Technical Challenges
1.3 Hand-held Robotic Instruments
1.4 Motivations
1.5 Contributions
1.6 Organization
2 Physiological Tremor Modeling: A Review
2.1 Physiological Tremor Genesis
2.2 Characteristics of Physiological Tremor
2.2.1 Time-domain Characteristics
2.2.2 Frequency domain Characteristics
2.3 Existing methods for Tremor Modeling
2.3.1 Amplitude-Domain Methods
2.3.2 Frequency-Domain Methods
2.4 Quantification of Physiological Tremor Modeling Methods
2.4.1 Physiological Tremor Database
2.4.2 Experimental Setup with Hand-held Instrument
3 Modeling of Physiological Tremor With Autoregressive (AR) Model
3.1 Introduction
3.2 Methodology
3.2.1 Autoregressive Modeling
3.2.2 Autoregressive Model with Least Mean Squares (LMS)
3.2.3 Autoregressive Model with Kalman Filter
3.2.4 Computational Complexity
3.3 Results
3.3.1 Optimal Initialization of AR model Hyper-parameters
3.3.2 Comparison Analysis
3.4 Experimental Evaluation
3.5 Discussion
3.6 Summary
4 Multi-step Prediction of Physiological Tremor
4.1 Introduction
4.2 Latency in Hand-held Instruments
4.3 Methodology
4.3.1 Multi-step prediction with BMFLC (MS-BMFLC)
4.3.2 Multi-step prediction with AR model (MS-AR)
4.3.3 Computational Complexity
4.4 Results
4.4.1 Optimal Initialization of Parameters
4.4.2 Comparison Analysis
4.5 Experimental Evaluation
4.6 Discussions
4.7 Summary
5 Machine Learning Techniques Based Multi-step Prediction of Physiological Tremor
5.1 Introduction
5.2 Methodology
5.2.1 Conventional LS-SVM
5.2.2 Moving window LS-SVM (MWLS-SVM)
5.2.3 Tremor prediction with MWLS-SVM
5.3 Results
5.3.1 Parameter selection
5.3.2 Simulation results
5.3.3 Experimental validation
5.4 Discussions
5.5 Summary
6 Machine Learning Techniques based Characterization of Pathological Tremor For FES based Applications
6.1 Introduction
6.2 Pathological Tremor Suppression with FES
6.3 Methodology
6.3.1 Proposed Approach For Pathological Tremor Filtering
6.3.2 Proposed Approach For Pathological Tremor Prediction
6.4 Pathological Tremor Database
6.5 Results
6.5.1 Optimal Initialization of mwLS-SVM Parameters
6.5.2 Pathological Tremor Filtering with mwLS-SVM
6.5.3 Pathological Tremor Prediction with mwLS-SVM
6.6 Summary
7 Conclusions and Future Directions
7.1 Conclusions
7.2 Future Directions
Research Objectives and Focus Areas
The primary research objective of this work is to develop robust, real-time signal processing and machine learning algorithms for the accurate modeling and multi-step prediction of physiological and pathological tremors in hand-held surgical instruments, specifically to mitigate the performance-degrading effects of phase delays inherent in conventional linear filtering.
- Real-time separation of surgeon's intended motion from involuntary tremor motion.
- Compensation of time-varying phase delays caused by hardware and digital filters.
- Development of horizon-free prediction methods using machine learning (LS-SVM/mwLS-SVM).
- Experimental validation using hand-held robotic instrumentation (iTrem) and tremor databases.
Excerpt from the Book
1.1 Context
Anthropomorphic design constrains humans to accomplish micro-manipulation tasks that demand dexterity and precise spatial resolution in upper limb maneuvers. The primary factor that limits the precise spatial resolution of maneuvers in humans is small-magnitude rhythmic involuntary movements. These movements are concomitant with all sorts of voluntary muscle contractions, and are named as Physiological tremor [2, 3, 4, 5].
Physiological tremor has been extensively investigated over the last five decades and is attributed as a ubiquitous property of humans [2, 5]. It is believed that physiological tremor originates from both mechanical and neuromuscular sources [3, 4].
However, a lucid view of its origin is still lacking. The amplitude of physiological tremor is usually in millimeter range and barely visible to the naked eye [2, 3, 4, 5]. In frequency domain, the dominant peak of physiological tremor lies in the range of 6 to 12 Hz [3, 4, 6, 7]. By virtue of its biological origins, physiological tremor is non-stationary in nature [5]. Experimental studies conducted on physiological tremor have determined that it is a linear stochastic process [5, 8]. Further, it’s modulation with voluntary motion in time-domain is additive, the magnitude of tremulous motion is superimposed on the magnitude of voluntary motion [5, 8, 6].
The effect of small-magnitude physiological tremor on normal daily living activities is benign. However, for the activities where the magnitude of voluntary motion is same as that of involuntary motion and for the activities that deal with a workspace in millimeter range, the effect of physiological tremor is adverse [9] . For example, in the arena of microsurgery where surgeons perform complex maneuvers in sub-millimeter-sized anatomy, physiological tremor has deleterious effects [9, 10].
Summary of Chapters
1 Introduction: Provides the context of micro-manipulation tasks, defines physiological tremor, and outlines the technical challenges and motivations for robotic compensation.
2 Physiological Tremor Modeling: A Review: Reviews the biological origins, time-domain and frequency-domain characteristics, and existing modeling/compensation methods for physiological tremor.
3 Modeling of Physiological Tremor With Autoregressive (AR) Model: Details the use of AR models updated with LMS and Kalman filters for tremor estimation, including parameter initialization and computational complexity analysis.
4 Multi-step Prediction of Physiological Tremor: Analyzes the latency in hand-held instruments and proposes multi-step prediction approaches using BMFLC and AR models to mitigate phase delay.
5 Machine Learning Techniques Based Multi-step Prediction of Physiological Tremor: Introduces a horizon-free prediction method using moving window Least Squares Support Vector Machines (mwLS-SVM) for adaptive tremor prediction.
6 Machine Learning Techniques based Characterization of Pathological Tremor For FES based Applications: Applies the developed mwLS-SVM methodology to the filtering and prediction of pathological tremor for Functional Electrical Stimulation (FES) applications.
7 Conclusions and Future Directions: Summarizes the thesis findings regarding tremor modeling and prediction, and suggests future research directions, including 3D tremor prediction and motion discrimination.
Keywords
Physiological tremor, Microsurgery, Hand-held robotic instruments, Signal processing, Autoregressive (AR) model, Kalman filter, Phase delay, Multi-step prediction, Machine learning, Least Squares Support Vector Machines (LS-SVM), mwLS-SVM, Pathological tremor, Functional Electrical Stimulation (FES), Tremor compensation, Non-stationary signal
Frequently Asked Questions
What is the primary motivation for this research?
The primary motivation is to enhance the precision of micro-surgical procedures by developing robotic hand-held instruments capable of real-time compensation of physiological tremors, which currently impede surgeons in delicate, sub-millimeter tasks.
What are the main thematic fields of the work?
The work covers robotic surgery, biomedical signal processing, control systems for medical robotics, machine learning (specifically support vector machines), and tremor analysis in neurology.
What is the core research question or objective?
The core objective is to develop algorithms that can accurately separate and predict involuntary tremor motion from a surgeon's intended hand motion in real-time, specifically addressing and correcting the performance-degrading phase delay introduced by standard digital filters.
Which scientific methods are primarily employed?
The research employs time-domain and frequency-domain signal analysis, Autoregressive (AR) modeling, adaptive filtering (Kalman and LMS), and machine learning techniques, specifically Least Squares Support Vector Machines (LS-SVM) with a moving window approach.
What is covered in the main section of the dissertation?
The main sections focus on the modeling of physiological tremors using AR models, the implementation of multi-step prediction to compensate for filtering-induced phase lags, and the application of machine learning (mwLS-SVM) for horizon-free tremor prediction in both physiological and pathological tremor cases.
Which keywords best characterize this research?
Key terms include physiological tremor, microsurgery, hand-held robotics, phase delay, multi-step prediction, Kalman filtering, and mwLS-SVM.
How does the proposed mwLS-SVM improve upon existing methods?
Unlike previous methods that require prior knowledge of the tremor's prediction horizon or assume stationarity, the proposed mwLS-SVM is horizon-free and adapts dynamically to the non-stationary nature of tremors using an incremental/decremental moving window training approach.
What significance do the experimental results have?
The experimental results demonstrate that the proposed multi-step prediction and machine learning techniques significantly improve tremor estimation and compensation accuracy compared to conventional linear filtering and earlier adaptive prediction methods.
- Quote paper
- Kalyana Veluvolu (Author), Sivanagaraja Tatinati (Author), 2020, Real-time Filtering of Physiological Tremor for Microsurgery. Physiological Tremor Robotic Compensation, Munich, GRIN Verlag, https://www.grin.com/document/948843
