478 research outputs found
Entropy in Image Analysis II
Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas
Evaluating footwear “in the wild”: Examining wrap and lace trail shoe closures during trail running
Trail running participation has grown over the last two decades. As a result, there have been an increasing number of studies examining the sport. Despite these increases, there is a lack of understanding regarding the effects of footwear on trail running biomechanics in ecologically valid conditions. The purpose of our study was to evaluate how a Wrap vs. Lace closure (on the same shoe) impacts running biomechanics on a trail. Thirty subjects ran a trail loop in each shoe while wearing a global positioning system (GPS) watch, heart rate monitor, inertial measurement units (IMUs), and plantar pressure insoles. The Wrap closure reduced peak foot eversion velocity (measured via IMU), which has been associated with fit. The Wrap closure also increased heel contact area, which is also associated with fit. This increase may be associated with the subjective preference for the Wrap. Lastly, runners had a small but significant increase in running speed in the Wrap shoe with no differences in heart rate nor subjective exertion. In total, the Wrap closure fit better than the Lace closure on a variety of terrain. This study demonstrates the feasibility of detecting meaningful biomechanical differences between footwear features in the wild using statistical tools and study design. Evaluating footwear in ecologically valid environments often creates additional variance in the data. This variance should not be treated as noise; instead, it is critical to capture this additional variance and challenges of ecologically valid terrain if we hope to use biomechanics to impact the development of new products
Biomechanical Spectrum of Human Sport Performance
Writing or managing a scientific book, as it is known today, depends on a series of major activities, such as regrouping researchers, reviewing chapters, informing and exchanging with contributors, and at the very least, motivating them to achieve the objective of publication. The idea of this book arose from many years of work in biomechanics, health disease, and rehabilitation. Through exchanges with authors from several countries, we learned much from each other, and we decided with the publisher to transfer this knowledge to readers interested in the current understanding of the impact of biomechanics in the analysis of movement and its optimization. The main objective is to provide some interesting articles that show the scope of biomechanical analysis and technologies in human behavior tasks. Engineers, researchers, and students from biomedical engineering and health sciences, as well as industrial professionals, can benefit from this compendium of knowledge about biomechanics applied to the human body
Modélisation de signaux électromyographiques par des processus de renouvellement - Filtre bayésien pour l'estimation séquentielle de paramètres à destination de la commande d'une prothèse d'avant-bras
soumise aux rapporteurs le 25/10/13We deal with intramuscular electromyographical signals (iEMG signals) taken in the muscles of a human upper limb. iEMG signal are an image of the control of the central nervous system on the muscles. They are made of a superimposition of wavelet trains, each wavelet codes a group of muscle fibers and its discharge rate codes the force developed by the group. The objective is to extract sequentially pieces of information from the signal iEMG. We believe that this information may be used to control an upper limb prosthesis. In the first place, we model a spike train as a Markov chain and we present mass functions to model the inter-spike intervals. The discrete Weibull mass function holds our attention: we realize an online estimation of its parameters. Secondly, we model the iEMG signal with a hidden Markov model based on the above model of spike train. We are able to propagate sequentially Bayes estimator of the parameters of the hidden Markov model with a Bayes filter, particularly the shapes of the wavelets and their discharge rates. Finally, we propose a method to estimate the number of wavelet trains, a discrete parameter of the model. We confirm the proposed methods and algorithms on simulated signals and iEMG signals.Nous traitons des signaux électromyographiques intramusculaires (signaux iEMG) relevés dans les muscles de l'avant-bras. Les signaux iEMG représentent une image de la commande du système nerveux central vers les muscles. Ils se composent d'une superposition de trains d'ondelettes, chaque ondelette code un groupe de fibres musculaires et son taux de mise à feu code l'effort produit par ce groupe. L'objectif est d'extraire de façon séquentielle des informations du signal iEMG. Nous espérons que ces informations se révèleront utiles pour la commande d'une prothèse d'avant-bras. En premier lieu, nous modélisons un train d'impulsions comme une chaîne de Markov et nous discutons des lois pouvant caractériser le temps entre deux impulsions. La loi de Weibull discrète a retenu notre attention. Nous avons mis en place une méthode d'estimation en ligne de ses paramètres. En second lieu, nous modélisons le signal iEMG par un modèle de Markov caché s'appuyant sur le modèle de train d'impulsions ci-dessus. La mise en place d'un filtre bayésien nous permet de propager séquentiellement une estimation bayésienne des paramètres du modèle de Markov caché, en particulier la forme des ondelettes et leur taux de mise à feu. Nous proposons finalement une méthode d'estimation du nombre de trains d'ondelettes, un paramètre discret du modèle. Nous validons les méthodes et algorithmes proposés sur des signaux simulés et des signaux iEMG
Measuring Behavior 2018 Conference Proceedings
These proceedings contain the papers presented at Measuring Behavior 2018, the 11th International Conference on Methods and Techniques in Behavioral Research. The conference was organised by Manchester Metropolitan University, in collaboration with Noldus Information Technology. The conference was held during June 5th – 8th, 2018 in Manchester, UK. Building on the format that has emerged from previous meetings, we hosted a fascinating program about a wide variety of methodological aspects of the behavioral sciences. We had scientific presentations scheduled into seven general oral sessions and fifteen symposia, which covered a topical spread from rodent to human behavior. We had fourteen demonstrations, in which academics and companies demonstrated their latest prototypes. The scientific program also contained three workshops, one tutorial and a number of scientific discussion sessions. We also had scientific tours of our facilities at Manchester Metropolitan Univeristy, and the nearby British Cycling Velodrome. We hope this proceedings caters for many of your interests and we look forward to seeing and hearing more of your contributions
Brain-Inspired Computing
This open access book constitutes revised selected papers from the 4th International Workshop on Brain-Inspired Computing, BrainComp 2019, held in Cetraro, Italy, in July 2019. The 11 papers presented in this volume were carefully reviewed and selected for inclusion in this book. They deal with research on brain atlasing, multi-scale models and simulation, HPC and data infra-structures for neuroscience as well as artificial and natural neural architectures
Innovations in the Integrated Management of Breast Cancer
Breast cancer is acknowledged as an international priority in healthcare. It is currently the most common cancer in women worldwide, with demographic trends indicating a continuous increase in incidence. Over the years, increasing efforts and resources have been devoted to the search for a systematic and optimized strategy in breast cancer diagnosis and treatment. Today, the Breast Unit model is considered the gold standard in order to ensure optimal patient-centered and research-based clinical services through multidisciplinary and integrated management.Surgical treatment has gradually evolved toward less aggressive approaches with the adoption of new therapeutic strategies. The evolution of evidence-based guidelines in such leading disciplines as radiation and medical oncology has led to a steady improvement in survival rates. This Special Issue will highlight innovations in the integrated management of breast cancer, their potential advantages, and the many open issues that still need to be properly defined and addressed
PRELIMINARY FINDINGS OF A POTENZIATED PIEZOSURGERGICAL DEVICE AT THE RABBIT SKULL
The number of available ultrasonic osteotomes has remarkably increased. In vitro and in vivo studies
have revealed differences between conventional osteotomes, such as rotating or sawing devices, and
ultrasound-supported osteotomes (Piezosurgery®) regarding the micromorphology and roughness
values of osteotomized bone surfaces.
Objective: the present study compares the micro-morphologies and roughness values of
osteotomized bone surfaces after the application of rotating and sawing devices, Piezosurgery
Medical® and Piezosurgery Medical New Generation Powerful Handpiece.
Methods: Fresh, standard-sized bony samples were taken from a rabbit skull using the following
osteotomes: rotating and sawing devices, Piezosurgery Medical® and a Piezosurgery Medical New
Generation Powerful Handpiece. The required duration of time for each osteotomy was recorded.
Micromorphologies and roughness values to characterize the bone surfaces following the different
osteotomy methods were described. The prepared surfaces were examined via light microscopy,
environmental surface electron microscopy (ESEM), transmission electron microscopy (TEM), confocal
laser scanning microscopy (CLSM) and atomic force microscopy. The selective cutting of mineralized
tissues while preserving adjacent soft tissue (dura mater and nervous tissue) was studied. Bone
necrosis of the osteotomy sites and the vitality of the osteocytes near the sectional plane were
investigated, as well as the proportion of apoptosis or cell degeneration.
Results and Conclusions: The potential positive effects on bone healing and reossification
associated with different devices were evaluated and the comparative analysis among the different
devices used was performed, in order to determine the best osteotomes to be employed during
cranio-facial surgery
Recommended from our members
ESICM LIVES 2017 : 30th ESICM Annual Congress. September 23-27, 2017.
INTRODUCTION. Unplanned readmission to intensive care is highly
undesirable in that it contributes to increased variance in care,
disruption, difficulty in resource allocation and may increase length
of stay and mortality particularly if subject to delays. Unlike the ICU
admission from the ward, readmission prediction has received
relatively little attention, perhaps in part because at the point of ICU
discharge, full physiological information is systematically available to
the clinician and so it is expected that readmission should be largely
due to unpredictable factors. However it may be that there are
multidimensional trends that are difficult for the clinician to perceive
that may nevertheless be predictive of readmission.
OBJECTIVES. We investigated whether machine learning (ML)
techniques could be used to improve on the simple published SWIFT
score [1] for the prediction of unplanned readmission to ICU within
48 hours.
METHODS. We extracted systolic BP, pulse pressure, heart and
respiration rate, temperature, SpO2, bilirubin, creatinine, INR, lactate,
white cell count, platelet count, pH, FiO2, and total Glasgow Coma
Score from ICU stays of over 2000 adult patients from our hospital
electronic patient record system. We trained our own custom
multidimensional / time-sensitive algorithmic ML system to predict
failed discharges defined as either readmission or unexpected death
within 48 hours of discharge. We used 10-fold cross validation to assess performance. We also assessed the effect of augmenting our
system by transfer learning (TL) with 44,000 additional cases from
the MIMIC III database.
RESULTS. The SWIFT score performed relatively poorly with an
AUROC of around 0.6 which our ML system trained on local data was
also able to match. However when augmented with an additional
dataset by TL, the AUROC for the ML system improved statistically
and clinically significantly to over 0.7.
CONCLUSIONS. Machine learning is able to improve on predictors
based on simple multiple logistic regression. Thus there is likely to
be information in the trends and in combinations of variables. A
disadvantage with this technique is that ML approaches require large
amounts of data for training. However, ML approaches can be
improved by TL. Basing prediction models on locally derived data
augmented by TL is a potentially novel approach to generating tools
that customised to the institution yet can exploit the potential power
of ML algorithms.
REFERENCES
[1] Gajic O, Malinchoc M, Comfere TB, et al. The Stability and
Workload Index for Transfer score predicts unplanned intensive care
unit patient readmission: initial development and validation. Crit Care
Med. 2008;36(3):676–82.
Grant Acknowledgement
This work was internally funded
Management of Degenerative Cervical Myelopathy and Spinal Cord Injury
The present Special Issue is dedicated to presenting current research topics in DCM and SCI in an attempt to bridge gaps in knowledge for both of the two main forms of SCI. The issue consists of fourteen studies, of which the majority were on DCM, the more common pathology, while three studies focused on tSCI. This issue includes two narrative reviews, three systematic reviews and nine original research papers. Areas of research covered include image studies, predictive modeling, prognostic factors, and multiple systemic or narrative reviews on various aspects of these conditions. These articles include the contributions of a diverse group of researchers with various approaches to studying SCI coming from multiple countries, including Canada, Czech Republic, Germany, Poland, Switzerland, United Kingdom, and the United States
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