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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. The analog position signals are then utilized to produce analog viscoelastic virtual environments to assess and benchmark the proposed methods through uncoupled stability and perceived fidelity tests. The results have shown that a virtual stiffness range larger than 400% can be obtained with enhanced fidelity compared to common digital implementations.

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. The present study undertakes a scoping review of designs for at-home upper limb stroke rehabilitation mechatronic devices to identify important design principles and areas for improvement. Online databases were used to identify papers published 2010-2021 describing novel rehabilitation device designs, from which 59 publications were selected describing 38 unique designs. The devices were categorized and listed according to their target anatomy, possible therapy tasks, structure, and features. Twenty-two devices targeted proximal (shoulder and elbow) anatomy, 13 targeted distal (wrist and hand) anatomy, and three targeted the whole arm and hand. Devices with a greater number of actuators in the design were more expensive, with a small number of devices using a mix of actuated and unactuated degrees of freedom to target more complex anatomy while reducing the cost. Twenty-six of the device designs did not specify their target users' function or impairment, nor did they specify a target therapy activity, task, or exercise. Twenty-three of the devices were capable of reaching tasks, 6 of which included grasping capabilities. Compliant structures were the most common approach of including safety features in the design. Only three devices were designed to detect compensation, or undesirable posture, during therapy activities. Six of the 38 device designs mention consulting stakeholders during the design process, only two of which consulted patients specifically. Without stakeholder involvement, these designs risk being disconnected from user needs and rehabilitation best practices. Devices that combine actuated and unactuated degrees of freedom allow a greater variety and complexity of tasks while not significantly increasing their cost. Future home-based upper limb stroke rehabilitation mechatronic designs should provide information on patient posture during task execution, design with specific patient capabilities and needs in mind, and clearly link the features of the design to users' needs., (© 2023. The Author(s).)

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 ( n = 10) and therapists ( n = 6). The transcripts were coded using thematic analysis inspired by the WHO International Classification of Functioning, Disability, and Health., Results: Design recommendations, potential features, and barriers were identified from the interviews. Stroke survivors and therapists agreed on many of the needs for at-home robotic rehabilitation; however, stroke survivors had more insights into their home environment, barriers, and needs relating to technology, while therapists had more insights into therapy methodology and patient safety and interaction. Both groups felt a one-size-fits-all approach to rehabilitation robot design is inappropriate. Designs could address a broader range of impairments by incorporating household items and breaking activities down into their component motions. Designs should incorporate hand and wrist supports and activities. Designs should monitor trunk and shoulder motion and consider incorporating group activities., Conclusion: While therapists can provide insight in the early stages of design of rehabilitation technology, stroke survivors' perspectives are crucial to designing for the home environment., Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2023.)

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. Aiming at expanding the virtual environment dynamic range, equations to find optimum damping values for maximize stiffness are derived and it is shown that by tuning the parameters for a customized discretization method, the virtual environment dynamic range will supersede the ranges offered by methods such as backward difference, Tustin and zero-order-hold. It is also shown that minimum time delay is required for stable Tustin implementation and that specific delay ranges must be avoided. The proposed discretization method is numerically and experimentally evaluated.

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. This can help regulate the energy that is delivered to the user. In this work, we estimate the energy absorbing capabilities of the human arm, using the metric Excess of Passivity (EOP). EOP data from healthy subjects were obtained based on Forcemyography of the subjects' arm, to expand the sources of biological information and improve estimations.Clinical relevance- This protocol can help determine the ability of rehabilitation patients to withstand robotic stimulation with high amplitudes of therapeutic forces, as needed in assistive therapy.

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. Since absolute stability demands bounds of all possible environments, we achieve this by conducting only three experiments that are often experienced in teleoperation: free slave, mass-carrying slave and locked slave (rigid environment). We will validate and compare our proposed method with the benchmark Llewellyns absolute stability criterion. Furthermore, we will examine the robustness of the proposed method and will provide guidelines for choosing the mass for the mass-carrying load condition.

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. In addition, Multilayer Decomposition, which enables a simple analog PD controller to implement nonhomogeneous or multilayer virtual environment dynamics, will be presented and evaluated.

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. This is performed using high-density surface electromyography (HD-SEMG) obtained from three upper-arm muscles, the biceps brachii, triceps brachii and brachioradialis. This method offers improved performance over other HD-SEMG and SEMG based force estimators, with a substantial reduction in the number of channels required.

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. The implications of EMD on end-point force prediction were considered using inter-session end-point force modelling with a support-vector-regression model. The delays were estimated using the first-order cross-correlation and the force and EMG signal were temporally aligned. The results show the delays vary by 20ms or more but did produce a notable trend based on elbow joint angle. We conclude that for upper-limb biomechanics modelling, the best practice is to align the force and EMG signals based on the induced delay during linear enveloping.

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. It is therefore fundamental to dynamically calibrate EM tracking systems and compensate for measurement error caused by field distorting objects commonly present in clinical environments. We propose to combine a motion model with observations of redundant EM sensors and compensate for field distortions in real time. We employ a simultaneous localization and mapping technique to accurately estimate the pose of the tracked instrument while creating the field distortion map. We conducted experiments with six degrees-of-freedom motions in the presence of field distorting objects in research and clinical environments. We applied our approach to improve the EM tracking accuracy and compared our results to a conventional sensor fusion technique. Using our approach, the maximum tracking error was reduced by 67% for position measurements and by 64% for orientation measurements. Currently, clinical applications of EM trackers are hampered by the adverse distortion effects. Our approach introduces a novel method for dynamic field distortion compensation, independent from preoperative calibrations or external tracking devices, and enables reliable EM navigation for potential applications.

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. Experiments were conducted in the presence of ferromagnetic and electrically-conductive field distorting objects and results compared with those of a conventional sensor fusion approach., Results: The proposed method reduced the tracking error from 3.94±1.61 mm to 1.82±0.62 mm in the presence of steel, and from 0.31±0.22 mm to 0.11±0.14 mm in the presence of aluminum., Conclusions: With reduced tracking error and independence from external tracking devices or pre-operative calibrations, the approach is promising for reliable EM navigation in various clinical procedures. Copyright © 2015 John Wiley & Sons, Ltd., (Copyright © 2015 John Wiley & Sons, Ltd.)

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. SEMG data recorded during constant force, constant velocity, and varying force, varying velocity flexion and extension trials are calibrated. The calibration values are obtained at specific elbow joint angles and interpolated to cover a continuous range of joint angles. The calibrated data are used in PCI models to estimate the force induced at the wrist. The experimental results show the effectiveness of the calibration scheme, combined with PCI modeling. For the constant force, constant velocity trials, minimum %RMSE of 8.3% is achieved for concentric contractions, 10.3% for eccentric contractions and 33.3% for fully dynamic contractions. Force estimation accuracy is superior in concentric contractions in comparison to eccentric contractions , which may be indicative of more nonlinearity in the eccentric SEMG-force relationship.

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., Methods: A needle deflection estimation method was developed to determine the amount of needle bending during insertion into deformable tissue by combining a kinematic deflection model with measurements taken from two electromagnetic trackers placed at the tip and the base of the needle. Experimental verification of this method for use in prostate brachytherapy needle insertion procedures was performed. A total of 21 beveled tip, 18 ga, 200 mm needles were manually inserted at various speeds through a template and toward different targets distributed within 3 soft tissue mimicking polyvinyl chloride prostate phantoms of varying stiffness. The tracked positions of both the needle tip and base were recorded, and Kalman filters were applied to fuse the sensory information. The estimation results were validated using ground truth obtained from fluoroscopy images., Results: The manual insertion speed ranged from 8 to 34 mm/s, needle deflection ranged from 5 to 8 mm at an insertion depth of 76 mm, and the elastic modulus of the soft tissue ranged from 50 to 150 kPa. The accuracy and robustness of the estimation method were verified within these ranges. When compared to purely model-based estimation, we observed a reduction in needle tip position estimation error by [Formula: see text] % (mean [Formula: see text] SD) and the cumulative deflection error by [Formula: see text] %., Conclusions: Fusion of electromagnetic sensors demonstrated significant improvement in estimating needle deflection compared to model-based methods. The method has potential clinical applicability in the guidance of needle placement medical interventions, particularly prostate brachytherapy.

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. The targeted repetition rate is in the range of 20-100 Hz with energies of few tens of mJ for the first stages up to 1 J for the final stage. An analysis of the specificities of Yb:CaF2 is done to explain the different options we chose to fulfil these specifications. The critical points and limitations of the multipass Yb:CaF2-based amplifiers are subsequently discussed. To overcome the encountered problems, different issues are investigated such as crystal optimisation, laser head geometry, thermo-optical dynamics or coherent combining techniques. Experimental results for different multipass configurations are demonstrated and discussed.

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. Static data sets are for error modeling, and dynamic ones are for testing. To improve the trueness and precision of the EM tracker, tracker calibration based on polynomial fitting and smooth filters, such as the Kalman filter, the moving average filter and the local regression filter, are deployed., Results: From the experimental data analysis, as the distance between the transmitter and the sensor of the EM tracking system increases, the trueness and precision tend to decrease. The system's trueness and jitter errors can be modeled as the 3(rd) order polynomial error equations. After minimizing the positional error and applying smoothing filters, the mean value of error reduction is 36.9%., Conclusion: Our method can effectively reduce both positional systematic error and jitter error caused by EM field distortion. The method is successfully applied to calibrate an EM tracked surgical cautery tool.

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. A tissue model based on a finite-element model provides force during the insertion. The system allows for training without the need for the presence of a trained clinician or access to live patients or cadavers. A pilot user study demonstrates the potential and functionality of the system.

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. One EM tracker is installed on the needle base and estimates the needle tip position indirectly using the kinematic needle deflection model. Another EM tracker is installed on the needle tip and estimates the needle tip position through direct, but noisy measurements. Kalman filters are then employed to fuse these two estimates in real time and provide a reliable estimate of the needle tip position, with reduced variance in the estimation error. We implemented this method to compensate for needle deflection during simulated needle insertions and performed sensitivity analysis for various conditions. At an insertion depth of 150 mm, we observed needle tip estimation error reductions in the range of 28% (from 1.8 to 1.3 mm) to 74% (from 4.8 to 1.2 mm), which demonstrates the effectiveness of our method, offering a clinically practical solution.

In this paper, we propose an approach to the control of linear teleoperation systems under time delays. Unlike traditional delay-robust control systems that guarantee passive communication channel through the transmission of wave variables, the new approach uses the concept of absolute stability for the physically expressive Lawrence's four-channel structure for transmitting the standard power variables, i.e., force and position. By incorporating kinesthetic performance requirements, we derive an absolutely stable four-channel controller that is transparent when time delay is negligible. Experimentally, the study evaluates and compares the performance of the proposed controller with that of a benchmark wave variable-based controller. The results indicate contact stability for large delays, a lack of position drift, and improved position and force tracking in both the free motion and rigid contact regimes for small delays.

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. The resulting force at the wrist was measured. The fast orthogonal search method was used to find a mapping between the system inputs - estimated SEMG amplitudes and joint angle - and the system output - measured force, for both calibrated and non-calibrated SEMG data. Models developed with calibrated data yielded a statistically significant improvement in force estimation compared to models developed with non-calibrated data, suggesting that the calibration method can compensate for changes in the SEMG-force relationship with changing joint angle. It was also found that the number of non-linear, joint angle-dependent terms used in the SEMG-force model was reduced with calibration. Additionally, initial inter-session analysis performed for four subjects suggests that calibration values can be used for subsequent recording sessions, and different output force levels., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

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. This paper investigates the use of Radial Basis Function (RBF) ANN and MLPANN for force estimation and experimentally compares the performance of the two methodologies for the same human anatomy, ie, hand force estimation, under an ensemble of operational conditions. In this unified study, the EMG signal readings from upper-arm muscles involved in elbow joint movement and elbow angular position and velocity are utilized as inputs to the ANNs. In addition, the use of the elbow angular acceleration signal as an input for the ANNs is also investigated.

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. A sensor patch, combining six SEMG and six contact pressure sensors was designed and built. SEMG, and contact pressure data over the biceps brachii and induced wrist force data were collected from 5 subjects. Polynomial fitting was used to find a mapping between biceps SEMG and wrist force. Comparison between evaluation values from models trained with modified and non-modified SEMG signals revealed a statistically significant superiority of models trained with the modified SEMG.

We present a needle deflection estimation method to compensate for needle bending during insertion into deformable tissue. We combine a kinematic needle deflection estimation model, electromagnetic (EM) trackers, and a Kalman filter (KF). We reduce the impact of error from the needle deflection estimation model by using the fusion of two EM trackers to report the approximate needle tip position in real-time. One reliable EM tracker is installed on the needle base, and estimates the needle tip position using the kinematic needle deflection model. A smaller but much less reliable EM tracker is installed on the needle tip, and estimates the needle tip position through direct noisy measurements. Using a KF, the sensory information from both EM trackers is fused to provide a reliable estimate of the needle tip position with much reduced variance in the estimation error. We then implement this method to compensate for needle deflection during simulated prostate cancer brachytherapy needle insertion. At a typical maximum insertion depth of 15 cm, needle tip mean estimation error was reduced from 2.39 mm to 0.31 mm, which demonstrates the effectiveness of our method, offering a clinically practical solution.

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. The simulated US images from CT closely resemble the vertebrae images captured in vivo. This is the first report of a system that provides a training environment to couple haptic feedback with a tracked mannequin, and a CT volume overlaid on a visual feed of the mannequin.

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. Comparison between evaluation values from models trained with calibrated and non-calibrated SEMG signals revealed a statistically significant superiority of models trained with the calibrated SEMG.