Development of a Novel Handheld Device for Active Compensation of Physiological Tremor

In microsurgery, the human hand imposes certain limitations in accurately positioning the tip of a device such as scalpel. 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. This is particularly true in the case of vitreoretinal microsurgery. The most familiar source of involuntary motion is physiological tremor. Real-time compensation of tremor is, therefore, necessary to assist surgeons to precisely position and manipulate the tool-tip to accurately perform a microsurgery. In this thesis, a novel handheld device (AID) is described for compensation of physiological tremor in the hand. MEMS-based accelerometers and gyroscopes have been used for sensing the motion of the hand in six degrees of freedom (DOF). An augmented state complementary Kalman filter is used to calculate 2 DOF orientation. An adaptive filtering algorithm, band-limited Multiple Fourier linear combiner (BMFLC), is used to calculate the tremor component in the hand in real-time. Ionic Polymer Metallic Composites (IPMCs) have been used as actuators for deflecting the tool-tip to compensate for the tremor

Drift-Free Position Estimation of Periodic or Quasi-Periodic Motion Using Inertial Sensors

Position sensing with inertial sensors such as accelerometers and gyroscopes usually requires other aided sensors or prior knowledge of motion characteristics to remove position drift resulting from integration of acceleration or velocity so as to obtain accurate position estimation. A method based on analytical integration has previously been developed to obtain accurate position estimate of periodic or quasi-periodic motion from inertial sensors using prior knowledge of the motion but without using aided sensors. In this paper, a new method is proposed which employs linear filtering stage coupled with adaptive filtering stage to remove drift and attenuation. The prior knowledge of the motion the proposed method requires is only approximate band of frequencies of the motion. Existing adaptive filtering methods based on Fourier series such as weighted-frequency Fourier linear combiner (WFLC), and band-limited multiple Fourier linear combiner (BMFLC) are modified to combine with the proposed method. To validate and compare the performance of the proposed method with the method based on analytical integration, simulation study is performed using periodic signals as well as real physiological tremor data, and real-time experiments are conducted using an ADXL-203 accelerometer. Results demonstrate that the performance of the proposed method outperforms the existing analytical integration method

Empowering and assisting natural human mobility: The simbiosis walker

This paper presents the complete development of the Simbiosis Smart Walker. The device is equipped with a set of sensor subsystems to acquire user-machine interaction forces and the temporal evolution of user's feet during gait. The authors present an adaptive filtering technique used for the identification and separation of different components found on the human-machine interaction forces. This technique allowed isolating the components related with the navigational commands and developing a Fuzzy logic controller to guide the device. The Smart Walker was clinically validated at the Spinal Cord Injury Hospital of Toledo - Spain, presenting great acceptability by spinal chord injury patients and clinical staf

Estimation of Physiological Tremor from Accelerometers for Real-Time Applications

Accurate filtering of physiological tremor is extremely important in robotics assisted surgical instruments and procedures. This paper focuses on developing single stage robust algorithms for accurate tremor filtering with accelerometers for real-time applications. Existing methods rely on estimating the tremor under the assumption that it has a single dominant frequency. Our time-frequency analysis on physiological tremor data revealed that tremor contains multiple dominant frequencies over the entire duration rather than a single dominant frequency. In this paper, the existing methods for tremor filtering are reviewed and two improved algorithms are presented. A comparative study is conducted on all the estimation methods with tremor data from microsurgeons and novice subjects under different conditions. Our results showed that the new improved algorithms performed better than the existing algorithms for tremor estimation. A procedure to separate the intended motion/drift from the tremor component is formulated

Real-Time Estimation of Pathological Tremor Parameters from Gyroscope Data

This paper presents a two stage algorithm for real-time estimation of instantaneous tremor parameters from gyroscope recordings. Gyroscopes possess the advantage of providing directly joint rotational speed, overcoming the limitations of traditional tremor recording based on accelerometers. The proposed algorithm first extracts tremor patterns from raw angular data, and afterwards estimates its instantaneous amplitude and frequency. Real-time separation of voluntary and tremorous motion relies on their different frequency contents, whereas tremor modelling is based on an adaptive LMS algorithm and a Kalman filter. Tremor parameters will be employed to drive a neuroprosthesis for tremor suppression based on biomechanical loading

Automatic real-time monitoring and assessment of tremor parameters in the upper limb from orientation data

Upper limb tremor is the most prevalent movement disorder and, unfortunately, it is not effectively managed in a large proportion of the patients. Neuroprostheses that stimulate the sensorimotor pathways are one of the most promising alternatives although they are still under development. To enrich the interpretation of data recorded during long-term tremor monitoring and to increase the intelligence of tremor suppression neuroprostheses we need to be aware of the context. Context awareness is a major challenge for neuroprostheses and would allow these devices to react more quickly and appropriately to the changing demands of the user and/or task. Traditionally kinematic features are used to extract context information, with most recently the use of joint angles as highly potential features. In this paper we present two algorithms that enable the robust extraction of joint angle and related features to enable long-term continuous monitoring of tremor with context awareness. First, we describe a novel relative sensor placement identification technique based on orientation data. We focus on relative rather than absolute sensor location, because in many medical applications magnetic and inertial measurement units (MIMU) are used in a chain stretching over adjacent segments, or are always placed on a fixed set of locations. Subsequently we demonstrate how tremor parameters can be extracted from orientation data using an adaptive estimation algorithm. Relative sensor location was detected with an accuracy of 94.12% for the 4 MIMU configuration, and 100% for the 3 MIMU configurations. Kinematic tracking error values with an average deviation of 8% demonstrate our ability to estimate tremor from orientation data. The methods presented in this study constitute an important step toward more user-friendly and context-aware neuroprostheses for tremor suppression and monitoring

Automatic real-time monitoring and assessment of tremor parameters in the upper limb from orientation data

Upper limb tremor is the most prevalent movement disorder and, unfortunately, it is not effectively managed in a large proportion of the patients. Neuroprostheses that stimulate the sensorimotor pathways are one of the most promising alternatives although they are still under development. To enrich the interpretation of data recorded during long-term tremor monitoring and to increase the intelligence of tremor suppression neuroprostheses we need to be aware of the context. Context awareness is a major challenge for neuroprostheses and would allow these devices to react more quickly and appropriately to the changing demands of the user and/or task. Traditionally kinematic features are used to extract context information, with most recently the use of joint angles as highly potential features. In this paper we present two algorithms that enable the robust extraction of joint angle and related features to enable long-term continuous monitoring of tremor with context awareness. First, we describe a novel relative sensor placement identification technique based on orientation data. We focus on relative rather than absolute sensor location, because in many medical applications magnetic and inertial measurement units (MIMU) are used in a chain stretching over adjacent segments, or are always placed on a fixed set of locations. Subsequently we demonstrate how tremor parameters can be extracted from orientation data using an adaptive estimation algorithm. Relative sensor location was detected with an accuracy of 94.12% for the 4 MIMU configuration, and 100% for the 3 MIMU configurations. Kinematic tracking error values with an average deviation of 8% demonstrate our ability to estimate tremor from orientation data. The methods presented in this study constitute an important step toward more user-friendly and context-aware neuroprostheses for tremor suppression and monitoring. © 2014 Lambrecht, Gallego, Rocon and Pons.This work has been funded by the European project NeuroTremor(ICT-2011.5.1-287739)andt he Spanish Consolider project HYPER (CSD2009-00067).Peer Reviewe