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Earlier studies showed that external focusing enhances motor performance and reduces muscular activity compare to internal one. However, low activity is not always desired especially in case of Human-Machine Interface applications. This study is based on investigating the effects of attentional focusing preferences on EMG based control systems. For the EMG measurements via biceps brachii muscles, 35 subjects were asked to perform weight-lifting under control, external and internal focus conditions. The difference between external and internal focusing was found to be significant and internal focus enabled higher EMG activity. Besides, six statistical features, namely, RMS, maximum, minimum, mean, standard deviation, and variance were extracted from both time and frequency domains to be used as inputs for Artificial Neural Network classifiers. The results found to be 87.54% for ANN1 and 82.69% for ANN2, respectively. These findings showed that one’s focus of attention would be predicted during the performance and unlike the literature, internal focusing could be also useful when it is used as an input for HMI studies. Therefore, attentional focusing might be an important strategy not only for performance improvement to human movement but also for advancing the study of EMG-based control mechanisms.

In photodynamic therapy, the knowledge of the penetration depth for the light is needed in order to ensure that the adequate optical energy is received by the tumorous tissue. In this study, the optical penetration depth of 635 nm laser light in chicken breast tissue has been measured by using 8 tissue samples with different thicknesses between 2.5 mm and 9.0 mm. Transmitted light intensities through the tissue samples have been measured for 11 different optical power values in the range of 130 mW-660 mW. Measurement results for each power value have been analyzed according to the Beer-Lambert law. With the help of statistical analyzes, it has been determined that the optical penetration depth in biological tissue does not depend on the optical power.

This study is based on measuring the Electrocardiogram (ECG) signals from the human body in real-time with the help of the software called NI LabVIEW. Not only the raw ECG signals, the digital filtered version of the ECG signals can also be displayed in real-time by processing the signals using the digital filtering tools of the program. The ECG itself provides various diagnostic information and NI LabVIEW biomedical toolkit offers many tools that helps to process the signals and perform feature extraction. Thus, this software was preferred for the ECG data acquisition. In this project, heart rate of a patient is calculated by detecting R-R intervals on the ECG tracing using the method called Teager Energy. In order to test the system, several experiments have been conducted with 12 subjects (6 non-smokers + 6 smokers). Their ECG signals were taken in relaxed and after running conditions. The experimental results were recorded for the graphical and statistical analysis. According to the results, the effect of smoking to the heart rate was discussed.