This article introduces a two-electrode ground-free electrocardiogram (ECG) with minimal hardware complexity, which is ideal for wearable battery-powered devices. The main issue of ground-free measurements is the presence of noise. Therefore, noise suppression methods that can be employed for a two-electrode ECG acquisition system are discussed in detail. Experimental measurements of a living subject and patient simulator are used to investigate and compare the performance of the three proposed methods utilizing the ADS1191 analogue front-end for biopotential measurements. The resulting signals recorded for the simulator indicate that all three methods should be suitable for suppressing power-line noise. The Power Spectral Density (PSD) of the signals measured for a subject exhibits differences across methods; the signal power at 50 Hz is −28, −24.8, and −26 dB for the first, second, and third method, respectively. The digital postprocessing of measured signals acquired a high-quality ECG signal comparable to that of three-electrode sensing. The current consumption measurements demonstrate that all proposed two-electrode ECG solutions are appropriate as a battery-powered device (current consumption < 1.5 mA; sampling rate of 500 SPS). The first method, according to the results, is considered the most effective method in the suppression of power-line noise, current consumption, and hardware complexity.

In this paper, capabilities of the Random Forest algorithm are tested with application to the Parkinson's disease progression that can be determined from speech. Results are compared with the linear regression model and the Classification and Regression Tree method. Mean Squared Error and Mean Absolute Error values were calculated and compared for each of the approaches. The Random Forest algorithm belongs to the group model category and usually improves the results achieved by regression trees, making it more suitable for fighting the disease.

A wobble board (WB) is a balance rehabilitation tool that is used in physiotherapy to improve strength and stability. The WB tested in this study includes a sensory module for measuring patients' tilt and rotation during stance. The aim of this study was to assess the reliability and validity of a balance measurement using a WB. Thirty healthy young adults participated in this study. The participants stood on the WB to simultaneously record the tilt of the WB and the center of pressure data using a force plate. The data were recorded during five measurement sessions on various days, with four trials each. Sways, velocities and indexes of complexity (CI) were computed. For reliability assessment, we used intra-class correlation coefficients within and between sessions; for validity, we computed Spearman correlation coefficients. The velocities and CI showed good intra-session reliability, and the sways showed mostly poor intra-session reliability. The results of inter-session reliability showed good to excellent reliability for CI, poor reliability for sways and poor to good reliability for velocities. The Spearman correlation coefficient showed excellent agreement between the mean velocities computed from the force plate and the WB. Our results confirm that the WB tested is suitable for stability assessment in young adults.

With the development of human society, there is an increasing importance for reliable person identification and authentication to protect a person’s material and intellectual property. Person identification based on brain signals has captured substantial attention in recent years. These signals are characterized by original patterns for a specific person and are capable of providing security and privacy of an individual in biometric identification. This study presents a biometric identification method based on a novel paradigm with accrual cognitive brain load from relaxing with eyes closed to the end of a serious game, which includes three levels with increasing difficulty. The used database contains EEG data from 21 different subjects. Specific patterns of EEG signals are recognized in the time domain and classified using a 1D Convolutional Neural Network proposed in the MATLAB environment. The ability of person identification based on individual tasks corresponding to a given degree of load and their fusion are examined by 5-fold cross-validation. Final accuracies of more than 99% and 98% were achieved for individual tasks and task fusion, respectively. The reduction of EEG channels is also investigated. The results imply that this approach is suitable to real applications.

In this paper, we describe an investigation of brain activity while playing a serious game (SG). A SG is focused on improving logical thinking, specifically on cognitive training of students in the field of basic logic gates, and we summarize SG description, design, and development. A method based on various signal processing techniques for evaluating electroencephalographic (EEG) data was implemented in the MATLAB. This assessment was based on the analysis of the spectrogram of particular brain activity. Changes in brain activity power at a characteristic frequency band during the gameplay were calculated from the spectrogram. The EEG of 21 respondents was measured. Based on the results, the respondents can be divided into three groups according to specific EEG activity changes during the gameplay compared to a relaxed state. The beta/alpha ratio, an indicator of brain employment to a mental task, was increased during gameplay in 18 of the 21 subjects. Our results reflected the sex of respondents, time of the game and the indicator, and whether the game was successfully completed.