Publications by authors named "Keyvan Hashtrudi-Zaad"

In this paper, we propose three methods to compute low-latency analog position where two of them fuse encoder and rate gyro signals. While one method is based on gyro with bias correction using encoder information, the other one is encoder-referenced combined with a resettable integrator to minimize the staircase form of encoder signals. Experiments on a one degree-of-freedom haptic simulation system have shown that a low-latency analog position with an accuracy over 98% compared to the sampled encoder signal can be obtained.

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Interest in home-based stroke rehabilitation mechatronics, which includes both robots and sensor mechanisms, has increased over the past 12 years. The COVID-19 pandemic has exacerbated the existing lack of access to rehabilitation for stroke survivors post-discharge. Home-based stroke rehabilitation devices could improve access to rehabilitation for stroke survivors, but the home environment presents unique challenges compared to clinics.

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Introduction: This study investigated the needs of stroke survivors and therapists, and how they may contrast, for the design of robots for at-home post stroke rehabilitation therapy, in the Ontario, Canada, context.

Methods: Individual interviews were conducted with stroke survivors ( = 10) and therapists ( = 6). The transcripts were coded using thematic analysis inspired by the WHO International Classification of Functioning, Disability, and Health.

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The stability of haptic simulation systems has been studied for a safer interaction with virtual environments. In this work, the passivity, uncoupled stability, and fidelity of such systems are analyzed when a viscoelastic virtual environment is implemented using a general discretization method that can also represent methods such as backward difference, Tustin, and zero-order-hold. Dimensionless parametrization and rational delay are considered for device independent analysis.

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Human-robot interactions help in various industries and enhance the user experience in different ways. However, constant safety monitoring is needed in environments where human users are at risk, such as rehabilitation therapy, space exploration, or mining. One way to improve safety and performance in robotic tasks is to include biological information of the user in the control system.

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Absolute stability analysis of bilateral teleoperation systems are typically model-based. Under borderline conditions of absolute stability, depending on the degree of uncertainty in the dynamic model of the teleoperator and existing noise, the system may behave as potentially unstable when the model-based analysis predicts otherwise. In this article, we propose a methodology to experimentally verify the absolute stability of master-slave teleoperation systems.

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Haptic simulation systems, which typically implement virtual environments in the discrete-time domain, present an inherent trade-off between stability, sampling frequency, and the range of implementable environment dynamics. Previous research has demonstrated the potential of analog feedback for expanding the range of environment dynamics that result in a stable haptic interaction. In this paper, the effect of various system parameters on the environment dynamic range is analytically and experimentally investigated in the sense of uncoupled stability.

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There is a discontinuity in published electromechanical delays (EMD) in upper-limb muscles and the state-of-the-art in modelling end-point force from electromyographic signals collected from one or more muscles. Published values are typically in the range of 10 to 30ms, depending on the nature of the contraction. In published literature where the EMG-force relationship is modelled, generally a delay of 100ms or more is induced during linear enveloping to match the EMD.

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An important quality of upper limb force estimation is the repeatability and worst-case performance of the estimator. The following paper proposes a methodology using an ensemble learning technique coupled with the fast orthogonal search (FOS) algorithm to reliably predict varying isometric contractions of the right arm. This method leverages the rapid and precise modelling offered by FOS combined with a univariate outlier detection algorithm to dynamically combine the output of numerous FOS models.

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Electromagnetic (EM) tracking systems are highly susceptible to field distortion. The interference can cause measurement errors up to a few centimeters in clinical environments, which limits the reliability of these systems. Unless corrected for, this measurement error imperils the success of clinical procedures.

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Background: In clinical environments, field distortion can cause significant electromagnetic tracking errors. Therefore, dynamic calibration of electromagnetic tracking systems is essential to compensate for measurement errors.

Methods: It is proposed to integrate the motion model of the tracked instrument with redundant EM sensor observations and to apply a simultaneous localization and mapping algorithm in order to accurately estimate the pose of the instrument and create a map of the field distortion in real-time.

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Purpose: The purpose of this study is to evaluate the uncertainties of an electromagnetic (EM) tracking system and to improve both the trueness and the precision of the EM tracker.

Methods: For evaluating errors, we introduce an optical (OP) tracking system and consider its measurement as "ground truth". In the experiment, static data sets and dynamic profiles are collected in both relatively less-metallic environments.

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We present the research advances on the development of 50-200 mJ energy range diode-pumped Yb:CaF2- based multipass amplifiers operating at relatively high repetition rates. These laser amplifiers are based on diverse innovative geometries. All these innovations aim to design compact, stable and reliable amplifiers adapted to our application that consists in pumping ultrashort-pulse OPCPA (optical parametric chirped pulse amplifier) systems in the frame of the Apollon 10 PW laser project.

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To accurately estimate muscle forces using electromyogram (EMG) signals, precise EMG amplitude estimation, and a modeling scheme capable of coping with the nonlinearities and dynamics of the EMG-force relationship are needed. In this work, angle-based EMG amplitude calibration and parallel cascade identification (PCI) modeling are combined for EMG-based force estimation in dynamic contractions, including concentric and eccentric contractions of the biceps brachii and triceps brachii muscles. Angle-based calibration has been shown to improve surface EMG (SEMG) based force estimation during isometric contractions through minimization of the effects of joint angle related factors, and PCI modeling captures both the nonlinear and dynamic properties of the process.

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Purpose: The performance of a fusion-based needle deflection estimation method was experimentally evaluated using prostate brachytherapy phantoms. The accuracy of the needle deflection estimation was determined. The robustness of the approach with variations in needle insertion speed and soft tissue biomechanical properties was investigated.

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We present a needle deflection estimation method to anticipate needle bending during insertion into deformable tissue. Using limited additional sensory information, our approach reduces the estimation error caused by uncertainties inherent in the conventional needle deflection estimation methods. We use Kalman filters to combine a kinematic needle deflection model with the position measurements of the base and the tip of the needle taken by electromagnetic (EM) trackers.

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A modification method based on integrated contact pressure and surface electromyogram (SEMG) recordings over the biceps brachii muscle is presented. Multi-site sEMGs are modified by pressure signals recorded at the same locations for isometric contractions. The resulting pressure times SEMG signals are significantly more correlated to the force induced at the wrist (FW), yielding SEMG-force models with superior performance in force estimation.

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In this paper, a calibration method to compensate for changes in SEMG amplitude with joint angle is introduced. Calibration factors were derived from constant amplitude surface electromyogram (SEMG) recordings from the biceps brachii (during elbow flexion) and the triceps brachii (during elbow extension) across seven elbow joint angles. SEMG data were then recorded from the elbow flexors (biceps brachii and brachioradialis) and extensors (triceps brachii) during isometric, constant force flexion and extension contractions at the same joint angles.

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This paper presents the prototype for an augmented reality haptic simulation system with potential for spinal needle insertion training. The proposed system is composed of a torso mannequin, a MicronTracker2 optical tracking system, a PHANToM haptic device, and a graphical user interface to provide visual feedback. The system allows users to perform simulated needle insertions on a physical mannequin overlaid with an augmented reality cutaway of patient anatomy.

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Measuring force production in muscles is important for many applications such as gait analysis, medical rehabilitation, and human-machine interaction. Substantial research has focused on finding signal processing and modeling techniques which give accurate estimates of muscle force from the surface-recorded electromyogram (EMG). The proposed methods often do not capture both the nonlinearities and dynamic components of the EMG-force relation.

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In this paper, we develop a mechanics-based dynamic model for bevel-tip flexible needle insertion into soft tissues. We use Newton-Euler formulation to account for the effect of actuation, friction, tissue interactions, and bevel-tip forces on the needle. The soft tissue deformation is modeled by finite element analysis, whereas the mechanics-based model is used to predict needle deflections due to bevel-tip asymmetry.

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A calibration method is proposed to compensate for the changes in the surface electromyogram (SEMG) amplitude level of the biceps brachii at different joint angles due to the movement of the muscle bulk under the EMG electrodes for a constant force level. To this end, an experiment was designed, and SEMG and force measurements were collected from 5 subjects. The fast orthogonal search (FOS) method was used to find a mapping between SEMG from the biceps and force recorded at the wrist.

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We propose a haptic-based simulator for ultrasound-guided percutaneous spinal interventions. The system is composed of a haptic device to provide force feedback, a camera system to display video and augmented computed tomography (CT) overlay, a finite element model for tissue deformation and US simulation from a CT volume. The proposed system is able to run a large finite element model at the required haptic rate for smooth force feedback, and uses haptic device position measurements for a steady response.

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In many applications that include direct human involvement such as control of prosthetic arms, athletic training, and studying muscle physiology, hand force is needed for control, modeling and monitoring purposes. The use of inexpensive and easily portable active electromyography (EMG) electrodes and position sensors would be advantageous in these applications compared to the use of force sensors which are often very expensive and require bulky frames. Among non-model-based estimation methods, Multilayer Perceptron Artificial Neural Networks (MLPANN) has widely been used to estimate muscle force or joint torque from different anatomical features in humans or animals.

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Soft tissue needle guidance and steering for clinical applications has been an active topic of research in the past decade. Although dynamic feedback control of needle insertion systems is expected to provide more accurate target tracking, it has received little attention due to the fact that most available models for needle-tissue interaction do not incorporate the dynamics of motions. In this paper, we study the controllability of rigid or flexible needles inside soft tissues using mechanical-based dynamic models.

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