6,193 research outputs found

    CLEAR: Covariant LEAst-square Re-fitting with applications to image restoration

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    In this paper, we propose a new framework to remove parts of the systematic errors affecting popular restoration algorithms, with a special focus for image processing tasks. Generalizing ideas that emerged for 1\ell_1 regularization, we develop an approach re-fitting the results of standard methods towards the input data. Total variation regularizations and non-local means are special cases of interest. We identify important covariant information that should be preserved by the re-fitting method, and emphasize the importance of preserving the Jacobian (w.r.t. the observed signal) of the original estimator. Then, we provide an approach that has a "twicing" flavor and allows re-fitting the restored signal by adding back a local affine transformation of the residual term. We illustrate the benefits of our method on numerical simulations for image restoration tasks

    The use of surface electromyography within equine performance analysis

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    Equine athletes participate in a wide range of equestrian disciplines. Performance analysis in sport is the collection and subsequent analysis of data, or key information sets, related to facets of training and / or competition, to accelerate and improve athletic performance. Equine performance analysis research aims to optimise the potential competition success of the horse whilst concurrently promoting health and welfare and increasing career longevity. Despite the benefits associated with performance analysis, its application is limited in equine sport.Surface electromyography (sEMG) is a non-invasive technique which illustrates recruitment patterns of superficial skeletal muscle and can provide quantitative data on the activity within muscle during dynamic motion. sEMG has the potential to contribute to equine performance analysis particularly via assessment of muscle recruitment, activity and adaptation within training regimens and during competition. The critical commentary demonstrates the potential of surface electromyography (sEMG) as an effective performance analysis tool that could be used to assess the physiological response of muscle during field-based exercise in the horse and provides examples of how sEMG data obtained could guide improvements in the efficacy of training regimens for the equine athlete. Critical reflection on four peer-reviewed evidence sources was conducted to establish their contribution to equine performance research and to facilitate debate of future research directions for equine sEMG. The research demonstrates the validity of telemetric sEMG as an emerging technology that could be used to analyse muscle performance in the equine athlete for defined events, for example jumping a fence, and to assess performance over time, for example monitoring muscle activity during interval training. Opportunities also exist to determine the efficacy of muscle-related clinical and therapeutic interventions such as prophylactic dentistry or physiotherapy. The preliminary research presented suggests the use of equine sEMG as a performance analysis tool has most value to assess and compare muscle performance during exercise within individual horses. However further research is required to substantiate this. Future studies integrating larger sample sizes, horses selected from specific equestrian disciplines and breeds, and further exploration of the impact of coat length and sEMG sensor placement on data obtained would be worthwhile to standardise and validate the protocols employed here. Equine performance is a complex area; future work needs to focus on the individual characteristics that contribute to desired performance goals, but should also evaluate performance as a holistic entity. It is essential for progression in the performance field that research undertaken is shared with the equine industry to enable practical implementation. The use of sEMG in the equine athlete has the potential to increase understanding of how muscle responds to exercise and could help create an evidence-base to inform individual and discipline-specific training regimens. Increased efficacy in training should promote success, enhancing performance and extending career longevity for the equine athlete, whilst indirectly benefiting the horse’s health and welfare through improved management practices and injury reduction

    Experimental Investigations of EMG-Torque Modeling for the Human Upper Limb

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    The electrical activity of skeletal muscle—the electromyogram (EMG)—is of value to many different application areas, including ergonomics, clinical biomechanics and prosthesis control. For many applications, the EMG is related to muscular tension, joint torque and/or applied forces. In these cases, a goal is for an EMG-torque model to emulate the natural relationship between the central nervous system (as evidenced in the surface EMG) and peripheral joints and muscles. This thesis work concentrated on experimental investigations of EMG-torque modeling. My contributions include: 1) continuing to evaluate the advantage of advanced EMG amplitude estimators, 2) studying system identification techniques (regularizing the least squares fit and increasing training data duration) to improve EMG-torque model performance, and 3) investigating the influence of joint angle on EMG-torque modeling. Results show that the advanced EMG amplitude estimator reduced the model error by 21%—71% compared to conventional estimators. Use of the regularized least squares fit with 52 seconds of training data reduced the model error by 20% compared to the least squares fit without regulation when using 26 seconds of training data. It is also demonstrated that the influence of joint angle can be modeled as a multiplicative factor in slowly force-varying and force-varying contractions at various, fixed angles. The performance of the models that account for the joint angle are not statistically different from a model that was trained at each angle separately and thus does not interpolate across angles. The EMG-torque models that account for joint angle and utilize advanced EMG amplitude estimation and system identification techniques achieved an error of 4.06±1.2% MVCF90 (i.e., error referenced to maximum voluntary contraction at 90° flexion), while models without using these advanced techniques and only accounting for a joint angle of 90° generated an error of 19.15±11.2% MVCF90. This thesis also summarizes other collaborative research contributions performed as part of this thesis. (1) EMG-force modeling at the finger tips was studied with the purpose of assessing the ability to determine two or more independent, continuous degrees of freedom of control from the muscles of the forearm [with WPI and Sherbrooke University]. (2) Investigation of EMG bandwidth requirements for whitening for real-time applications of EMG whitening techniques [with WPI colleagues]. (3) Investigation of the ability of surface EMG to estimate joint torque at future times [with WPI colleagues]. (4) Decomposition of needle EMG data was performed as part of a study to characterize motor unit behavior in patients with amyotrophic lateral sclerosis (ALS) [with Spaulding Rehabilitation Hospital, Boston, MA]

    Electromyogram (EMG) Signal Analysis: Extraction of a Novel EMG Feature and Optimal Root Difference of Squares (RDS) Processing in Additive Noise

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    Electromyogram signals generated by human muscles can be measured on the surface of the skin and then processed for use in applications such as prostheses control, kinesiology and diagnostic medicine. Most EMG applications extract an estimate of the EMG amplitude, defined as the time-varying standard deviation of EMG, EMGσ. To improve the quality of EMGσ, additional signal processing techniques, such as whitening, noise reduction and additional signal features can be incorporated into the EMGσ processing. Implementation of these additional processing techniques improve the quality of the processed signal but at the cost of increased computational complexity and required calibration contractions. Whitening filters are employed to temporally decorrelate data so that the samples are statistically independent. Different types of whitening filters, linear and adaptive, and their performance have been previously studied in (Clancy and Hogan) and (Clancy and Farry). The linear filter fails at low effort levels and the adaptive filter requires a calibration every time electrodes are removed and reapplied. With the goal of avoiding the disadvantages of the previous whitening filter approaches, the first signal processing technique studied herein developed a universal fixed whitening filter using the ensemble mean of the power spectrum density of EMG recordings from the 64 subjects available in an existing data set. Performance of the EMG to torque model with the universal fixed whitening filter was computed to be 4.8% maximum voluntary contraction (MVC); this is comparable to the 4.84 %MVC error computed for the adaptive whitening filter. The universal fixed whitening filter preserves the performance of the adaptive filter but need not be calibrated for each electrode. To optimize noise reduction, the second signal processing technique studied derived analytical models using the resting EMG data. The probability density function of the rest contractions was observed to be very close to a Gaussian distribution, showing only a 1.6% difference when compared to a Gaussian distribution. Once the models were developed, they were used to prove that the optimal subtraction of the noise variance is to compute the root of the difference between the signal squared and noise variance (RDS). If this result would lead to a negative value, it must be set to zero; EMGσ cannot contain negative components. Once the RDS was proven to be the optimal noise subtraction, it was implemented on 0% MVC and 50% MVC data. The RDS processing has a considerable impact on lower level contractions (0% MVC), but not on higher level contractions (50% MVC), as expected. The third signal processing technique involved the creation of a new EMG feature from four individual signal features. Different techniques were used to combine EMGσ, zero crossings (ZC), slope sign changes (SSC) and waveform length (WL) into a single new EMG feature that would be used in an end application, such as the modeling of torque about the elbow or prosthesis control. The new EMG feature was developed to reduce the variance of the traditional EMGσ only feature and to eliminate the need for calibration contractions. Five different methods of combination were attempted, but none of the new EMG features improved performance in EMG to torque model

    Content-based Image Classification via Visual Learning

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    Commercial articulated collaborative in situ 3D bioprinter for skin wound healing

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    In situ bioprinting is one of the most clinically relevant techniques in the emerging bioprinting technology because it could be performed directly on the human body in the operating room and it does not require bioreactors for post-printing tissue maturation. However, commercial in situ bioprinters are still not available on the market. In this study, we demonstrated the benefit of the originally developed first commercial articulated collaborative in situ bioprinter for the treatment of full-thickness wounds in rat and porcine models. We used an articulated and collaborative robotic arm from company KUKA and developed original printhead and correspondence software enabling in situ bioprinting on curve and moving surfaces. The results of in vitro and in vivo experiments show that in situ bioprinting of bioink induces a strong hydrogel adhesion and enables printing on curved surfaces of wet tissues with a high level of fidelity. The in situ bioprinter was convenient to use in the operating room. Additional in vitro experiments (in vitro collagen contraction assay and in vitro 3D angiogenesis assay) and histological analyses demonstrated that in situ bioprinting improves the quality of wound healing in rat and porcine skin wounds. The absence of interference with the normal process of wound healing and even certain improvement in the dynamics of this process strongly suggests that in situ bioprinting could be used as a novel therapeutic modality in wound healing.publishersversionPeer reviewe
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