Surface electromyography (sEMG) is an important measurement technique used in biomechanical, rehabilitation and sport environments. In this article the design, development and testing of a low-cost wearable sEMG system are described. The hardware architecture consists of a two-cascade small-sized bioamplifier with a total gain of 2,000 and band-pass of 3 to 500 Hz. The sampling frequency of the system is 1,000 Hz. Since real measured EMG signals are usually corrupted by various types of noises (motion artifacts, white noise and electromagnetic noise present at 50 Hz and higher harmonics), we have tested several denoising techniques, both on artificial and measured EMG signals. Results showed that a wavelet—based technique implementing Daubechies5 wavelet and soft sqtwolog thresholding is the most appropriate for EMG signals denoising. To test the system performance, EMG activities of six dominant muscles of ten healthy subjects during gait were measured (gluteus maximus, biceps femoris, sartorius, rectus femoris, tibialis anterior and medial gastrocnemius). The obtained EMG envelopes presented against the duration of gait cycle were compared favourably with the EMG data available in the literature, suggesting that the proposed system is suitable for a wide range of applications in biomechanics.

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Laser pointers are one of the most widely used interactive and pointing devices in different human-computer interaction systems. Existing approaches to vision-based laser spot tracking are designed for controlled indoor environments with the main assumption that the laser spot is very bright, if not the brightest, spot in images. In this work, we are interested in developing a method for an outdoor, open-space environment, which could be implemented on embedded devices with limited computational resources. Under these circumstances, none of the assumptions of existing methods for laser spot tracking can be applied, yet a novel and fast method with robust performance is required. Throughout the paper, we will propose and evaluate an efficient method based on modified circular Hough transform and Lucas–Kanade motion analysis. Encouraging results on a representative dataset demonstrate the potential of our method in an uncontrolled outdoor environment, while achieving maximal accuracy indoors. Our dataset and ground truth data are made publicly available for further development.

Background:Neonatal hyperbilirubinemia is a common clinical manifestation of the inherited glucose-6-phosphate dehydrogenase (G6PD) deficiency.Aim of the study:The aim of this study was to investigate the influence of the inherited G6PD deficiency on the appearance of neonatal hyperbilirubinemia in southern Croatia.Methods:The fluorescent spot test (FST) was used in a retrospective study to screen blood samples of 513 male children who had neonatal hyperbilirubinemia, of unknown cause, higher than 240 μmol/L. Fluorescence readings were performed at the beginning and at the fifth and tenth minute of incubation and were classified into three groups bright fluorescence (BF), weak fluorescence (WF) and no fluorescence (NF). Normal samples show bright fluorescence. All NF and WF samples at the fifth minute were quantitatively measured using the spectrophotometric method.Results:Bright fluorescence was present in 461 patients (89.9%) at the fifth minute. The remaining 52 (10.1%) were quantitatively estimated using the spectrophotometric method. G6PD deficiency was observed in 38 patients (7.4%).Conclusions:Prevalence rate of G6PD deficiency among male newborns with hyperbilirubinemia in southern Croatia is significantly higher (p < 0.01) compared with the previously reported prevalence rate among male in general population of southern Croatia (0.75%). We recommend FST to be performed in hyperbilirubinemic newborns in southern Croatia.

The main goal of any classification or regression task is to obtain a model that will generalize well on new, previously unseen data. Due to the recent rise of deep learning and many state-of-the-art results obtained with deep models, deep learning architectures have become one of the most used model architectures nowadays. To generalize well, a deep model needs to learn the training data well without overfitting. The latter implies a correlation of deep model optimization and regularization with generalization performance. In this work, we explore the effect of the used optimization algorithm and regularization techniques on the final generalization performance of the model with convolutional neural network (CNN) architecture widely used in the field of computer vision. We give a detailed overview of optimization and regularization techniques with a comparative analysis of their performance with three CNNs on the CIFAR-10 and Fashion-MNIST image datasets.