Electrocardiogram (ECG) is one of the most important and effective tools in clinical routine to assess the cardiac arrhythmias. In this research higherorder spectral estimations, bispectrum and third-order cumulants, are evaluated, saved, and pre-trained using convolutional neural networks (CNN) algorithm. CNN is transferred in this study to carry out automatic ECG arrhythmia diagnostics after employing the higher-order spectral algorithms. Transfer learning strategies are applied on pre-trained convolutional neural network, namely AlexNet and GoogleNet, to carry out the final classification. Five different arrhythmias of ECG waveform are chosen from the MIT-BIH arrhythmia database to evaluate the proposed approach. The main contribution of this study is to utilize the pre-trained convolutional neural networks with a combination of higher-order spectral estimations of arrhythmias ECG signal to implement a reliable and applicable deep learning classification technique. The Highest average accuracy obtained is 97.8 % when using third cumulants and GoogleNet. As is evident from these results, the proposed approach is an efficient automatic cardiac arrhythmia classification method and provides a reliable recognition system based on well-established CNN architectures instead of training a deep CNN from scratch.

<span>Pneumonia is a major cause for the death of children. In order to overcome the subjectivity and time consumption of the traditional detection of pneumonia from chest X-ray images; this work hypothesized that a hybrid deep learning system that consists of a convolutional neural network (CNN) model with another type of classifiers will improve the performance of the detection system. Three types of classifiers (support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF) were used along with the traditional CNN classification system (Softmax) to automatically detect pneumonia from chest X-ray images. The performance of the hybrid systems was comparable to that of the traditional CNN model with Softmax in terms of accuracy, precision, and specificity; except for the RF hybrid system which had less performance than the others. On the other hand, KNN hybrid system had the best consumption time, followed by the SVM, Softmax, and lastly the RF system. However, this improvement in consumption time (up to 4 folds) was in the expense of the sensitivity. A new hybrid artificial intelligence methodology for pneumonia detection has been implemented using small-sized chest X-ray images. The novel system achieved a very efficient performance with a short classification consumption time.</span>

Background: Handgrip or Grip strength (GS) is a common method used to evaluate muscle strength and affected by different factors, including age, gender, and arm's positions.Objective: This study aims to investigate the effect of both the gender and arm's positions on the handgrip strength and the fatigue resistance (FR), which is the time needed for the handgrip strength to drop to 75% (FR 75 ), 50% (FR 50 ), and 25% (FR 25 ) of its maximum strength during sustained maximal handgrip effort. Material and Methods:In this experimental study, 59 male and 41 female participants were asked to grip forcefully on a dynamometer for the longest period. GS and FR 75 , FR 50 , and FR 25 values were recorded for 7 different arm positions. Factorial ANOVA was used to find the main effect of gender and position and the interaction between them. Sidak and Tukey's HSD tests were used to find the gender and arm position effects, respectively. Results:The results showed a significant effect for gender and arm position on GS and FR and a significant interaction effect for GS that was significantly higher in males than females for all positions. The gender difference in FR depends on arm's positions and the level at which the FR was measured. GS was higher when arm adduction with 90 ͦ forward at the elbow as compared to arm abduction with 180 ͦ at the shoulder and 90 ͦ at the elbow. Conclusion:The results confirmed the significant effect of the gender and arm's positions on the maximal handgrip strength and fatigue resistance during sustained maximal handgrip effort.

Sound Source Localization refers to the ability to identify the location of a certain sound in direction and distance from a reference point. This paper introduces a sound source direction estimation system for an acoustical signal of interest in a 360° sweep in the horizontal plane. In this paper, an array of microphones is used as receivers for the sound waves which are recorded with prescribed blocks size of samples and interfaced to a PC for analysis using digital signal processing techniques. Spectral analysis and wavelet transform are applied on a reference microphone to test if a signal of interest has been received. If so, further processing for signals received on all microphones is initiated to estimate the sound source direction. Generalized Cross Correlation is used in this research for time delay estimation among the microphones since it has low computation complexity, sufficient estimation precision and it works very well in reverberant environments.

The purpose of this work is to investigate the effect of anteriorly-added mass to simulate pregnancy on lower extremities kinematic and lumbar and thoracic angles during stair ascending and descending. 18 healthy females ascended and descended, with and without a pseudo-pregnancy sac of 12 kg (experimental and control groups, respectively), a costume-made wooden staircase while instrumented with 20 reflective markers placed on the lower extremities and the spine. The movements were captured by 12 infrared cameras surrounding the staircase. Tracked position data were exported to MATLAB to calculate the required joints angles. SPSS was used to compare the ascent and descent phases of control group, and to find if there are any significant differences between control and experimental groups in the ascent phase as well as in the descent phase. When comparing the ascent and descent phases of control group, data revealed a higher hip flexion during ascending and greater ankle planter-flexion and dorsiflexion, lumbar, and thoracic angles during descending; however, no significant difference was shown in the knee flexion angle between ascending and descending. Non-pregnant data showed greater maximum hip flexion and ankle dorsiflexion during stair ascending compared to simulated-pregnant group; while ankle planter-flexion, knee flexion, and lumbar angle were greater for simulated-pregnant status. During stair descending, non-pregnant group had greater minimum hip flexion and ankle dorsiflexion compared to simulated pregnant group; while ankle planter-flexion, knee flexion, and maximum hip flexion were greater for simulated-pregnant group. However, the lumbar and thoracic angles were found to be similar for simulated-pregnant and non-pregnant groups during stair descending. In conclusion, the current study revealed important kinematic modifications pregnant women adopt while ascending and descending stairs at their final stage of pregnancy to increase their stability.