The paper presents preliminary analyses of pulse transit time (PTT) derived from photoplethysmography (PPG) measurements taken at the elbow, wrist, and finger. Data were collected from ten participants under the approval of a bioethics committee. The study examined the correlation coefficients of PTT between these locations using four fiducial points of the PPG signal. The results show strong dependence on the individual participant, the choice of fiducial point, and the sensor placement (the correlation coefficient varies from -0.69 to 1.00). These findings highlight the need for further analysis on a larger and more diverse population, including individuals with varying health conditions and broader ranges of blood pressure fluctuations.

In this paper, device construction and preliminary results for shower safety assistance are presented. The device allows monitoring of shower-taking persons without violating privacy and intimacy, while it has the ability to detect a persons entering and leaving the shower and detecting fall conditions. It allows better supervision of elders living independently at their locations.

There are many approaches to non-invasively deter-mine blood pressure. Among them there are methods based on utilizing pulse transit time measured by means of photoplethys-mography. The variability of the blood pressure drop between two measurement sensors placed along the artery and its dependence on the selected parameters describing the cardiovascular system is presented in the paper. This pressure drop modifies the pressure pulse propagation velocity, thus also value of PTT. The properties of four fiducial points on the radial and brachial arteries were analyzed. Publicly available simulation data were used for the analyses. The best results were achieved for the point defined by the maximum of the first derivative of the signal.

Pulse Wave Velocity (PWV) is measured and utilized in many clinical applications. Recently, a wide research has been led to develop a cuff-less and continuous blood pressure method basing on PWV. However, in this application a decision on choosing an appropriate fiducial point of pulse wave (PW) waveform is necessary and substantial. It would allow to measure time parameters necessary to determine PWV. An influence of sampling frequency and other preprocessing algorithms on an accuracy of fiducial points determination are discussed in the paper.

In this paper the simple architecture of neural network for hand gesture classification was presented. The network classifies the previously calculated parameters of EMG signals. The main goal of this project was to develop simple solution that is not computationally complex and can be implemented on microprocessors in low-cost 3D printed prosthetic arms. As the part of conducted research the data set EMG signals corresponding to 5 different gestures was created. The accuracy of elaborated solution was 90% when applied real time on data sampled with 1kHz frequency and 75% when applied real time on data acquired and process directly on microprocessor with lower,100Hz sampling frequency.

A non-invasive and continuous blood pressure estimation could allow a better diagnosis and an earlier detection of various diseases. It could be performed using a photoplethysmography. However, it requires that a relation between blood pressure and pulse transit time is known. Eleven theoretical formulas were used in obtaining the simulated and noise free synthetic data. Then, they were utilized in validation of three, commonly used, formulas for blood pressure estimation. Finally, the best one was indicated.

In this preliminary study we, investigate the possibility of user recognition techniques suitable on smart home devices like chairs, beds, aiming for low-power, high accuracy and quick response time. We propose the two well know technique: voice speaker recognition and accelerometer signal from device mounted on the chair, and the third one optical system basing on IR LED transmitter/receiver circuit. The preliminary results proved that the developed system based on measurement and analysis of the accelerometer signal is very promising and suitable for implementation in the final product. The optical system requires further work but potentially it can be used for user identification purposes.

This paper describes the possibility of monitoring the progress of knowledge and skills acquisition by the students of radiology. It is achieved by an analysis of a visual attention distribution patterns during image-based tasks solving. The concept is to use the eye-tracking data to recognize the way how the radiographic images are read by recognized experts, radiography residents involved in the training program, and untrained users who graduated from biomedical engineering. The results of research presented in this paper support the usefulness of earlier elaborated eye metrics, Visual Attention Time Product VATP, for skills measurement when using Electronic Medical Record for CT images evaluation. Moreover, it appears that it is possible to differentiate the level of residents skills in radiology structures recognition. Presented methodology can also be utilized in other areas where operator performs image related tasks.

EDR is an interesting measuring technique that allows an indirect assessment of respiratory activity. This is an alternative solution to direct methods that are based on the measurement of air flow, which require a specialized sensor or even a system. However, due to inter-personal anatomical differences, the optimal ECG lead (placement of the electrodes) ensuring the best EDR signal quality is not fixed. An influence of ECG lead geometrical relation to the heart axis on the EDR signal quality has been examined in the paper. It was found that optimal electrodes position strongly differs between individuals. For each person there were at least few leads with high correlation with reference signal (over 0.8). They allowed for respiration frequency estimation with almost 100% accuracy.

Impedance properties of bath solution are compared. Any ingredient such as soap, shampoo or bathing salt changes bath-solution's electrical properties eg. impedance. We have measured and analysed influence of typical bathing ingredients on electrical properties of the solution. We investigate impedance changes over frequency range from 100Hz tu 100kHz as it is most applicable for person detection. Electrical properties of bath solution knowledge is essential for person detection and biosignal acquisition of bathing person.

In this paper the possibility of person identification based on biosignal is investigated. The work focus on the analysis of the changes in intervals between successive R-waves of electrocardiogram (ECG) recorded by wearable electronics in form of a necklaces. The main idea behind this project is to find efficient tool which may prevent sudden consciousness loss episodes or even sudden death episodes related to rapid temperature changes. Proposed method relays on the estimation of the RR interval changes in time with three approximation functions. Then, the identification of a person is performed based on the parameters of the utilized estimation functions.

The estimation of systolic and diastolic blood pressure using artificial neural network is considered in the paper. The blood pressure values are estimated using pulse arrival time, and additionally RR intervals of ECG signal together with respiration signal. A single layer recurrent neural network with hyperbolic tangent activation function was used. The average blood pressure estimation error for the data obtained from 21 subjects from MIMIC database was equal to 2.490 mmHg with standard deviation equal to 1.063 mmHg for systolic blood pressure, and was equal to 1.330 mmHg with standard deviation equal to 0.627 mmHg for diastolic blood pressure using vanilla recurrent neural networks. Similar results were obtained for long short term memory cells. The simulation shows that taking into account pulse arrival time together with RR intervals and respiration signal gave better results than pulse arrival time alone.

In a smart home, health and well-being monitoring systems could be embedded in everyday devices providing a pervasive care. A home bathroom scale is an example of such a device, typically used to measure body weight and very often its composition (e.g. body water/fat percentage). In this paper, we analyzed a potential use of the bathroom scale to measure electrocardiogram (ECG) from electrodes located on the scale's tile. In particular we used both, simulations and real measurements to analyze the possibility of a such approach. We originally created a simple finite element model to analyze a role of a vector cardiogram projected on the foot-foot lead. We also analyzed results obtained during experiments in which took part (in total) 13 healthy volunteers. They were examined both, in sitting and standing positions. As a result we showed that the quality of the recorded ECG signal from feet is highly related to the electrical vector orientation of the individual's heart. We additionally compared the detected QRS complexes from hands-and feet-sampled ECG for sitting and standing positions. Results showed that for the sitting position the difference was only for 3.6% of all QRS complexes, while for standing one about 24.9%.

In this paper we propose to use temporal muscle contraction to perform certain actions. Method: The set of muscle contractions corresponding to one of three actions including “single-click”, “double-click” “click-n-hold” and “non-action” were recorded. After recording certain amount of signals, the set of five parameters was calculated. These parameters served as an input matrix for the neural network. Two-layer feedforward neural network with one hidden layer of 200 neurons was applied to classify gestures based on the input matrix. Results: The network was trained using the dataset consisted of 43 samples and then tested on the 34 samples dataset. All gestures from the test set were correctly classified.

In this paper we present the research conducted on synchronous measurements of biosignals. The experiment was conducted to evaluate the possibility of estimating vital signs based on eye tracking. Method: The eGlasses platform was used for acquisition of ECG, respiration rate, eye and pupil movement and blood pressure. Data were acquired in three 5 min. intervals during which a subject was performing certain tasks. The signals were filtered and resampled. The correlation between blood pressure, respiration and pupil dilation was calculated. Results: The measurements were conducted on 12 healthy volunteers. The obtained correlation coefficient was similar for all people and measurement positions. Conclusions: The electronic eyewear like eGlasses can be utilized to measure and process essential vital signs. Although some results are promising, estimating a value of blood pressure or hart rate based on data provided by eye tracking module requires some further studies.

Nearly 10% of all upper limb amputations concern the whole arm. It affects the mobility and reduces the productivity of such a person. These two factors can be restored by using prosthetics. However, the complexity of human arm makes restoring its basic functions quite difficult. When the osseointegration and/or targeted muscle reinnervation (TMR) are not possible, different modalities can be used to control the prosthesis. In this paper the usability of electromyography (EMG) signals for such a control is evaluated. Method: first, the types of operations performed by the prosthetic arm that could be handled by EMG module were defined. The raw EMG signal, corresponding to the predefined gesture, was acquired from the surface of trapezius muscle. The pattern recognition neural network was trained to classify gestures based on recorded RAW data. Results: The neural network was trained using 56 signals corresponding to performed gestures. Optimal performance was achieved for 29 training cycles. The network was tested using data set of 56 gestures. The designed network was tested on gestures recorded from 10 volunteers. The gestures were correctly classified with nearly 84% accuracy. Conclusions: The EMG analysis is a reliable modality when it comes to hybrid interfaces for control over prosthetic arm.

The paper presents an example of model-based estimation of blood pressure parameters (onset, systolic and diastolic pressure) from continuous measurements.First, the signal was low pass filtered and its quality was estimated.Good quality periods were divided into beats using an electrocardiogram.Next, the beginning of each beat of the blood pressure signal was approximated basing on the function created from the sum of two independent distributions: Gaussian and exponential.The nonlinear least square method was used to fit measurement data to the model.The initial conditions for the fitting procedure were selected for each beat on the basis of its parameters.Finally, the diastolic and systolic values of blood pressure and onset were determined.

In this paper we deal with the problem of precise gesture recognition for the active optical proximity sensor with sparse 8 photodiodes. We particularly focus on developing the method of estimating the real, usually not observable, maximum signal value representing maximum intensity of light reflected from an obstacle present in the front of the sensor. Different configurations of the fingers were used as an obstacle. The Monte Carlo simulations were performed in order to cognize the accurate pattern created by fingers of considered configurations. The accurate description of curves representing finger profiles was obtained after applying the least squares method. The results of analyzes proved that finger arrangement configurations, particularly 1 finger and 2 jointed fingers, can be considered as sources of Gaussian like shapes on the face of the sensor. Therefore, data measured by 8 sparse photodiodes of the active optical gesture sensor were compared to normal distribution curves. The results of the comparison allow normalization of the measured data and the estimation of the real, usually not observable, maximum signal value. The results and discussion sections of the paper present the average detection accuracy, advantages and limitations of the proposed approach.

In this paper we investigate the effectiveness of the interaction using eye tracking and electromyography. Smart glasses requires reliable interfaces for controlling the graphic content displayed directly in front of the user's eye. Presented research is related with the eGlasses project, which is focused on the development of an open platform in the form of multisensory electronic glasses and related interaction methods. One of the implemented interaction methods is the one based on eye tracking module. The eye tracking provides quite accurate pointing but there is always a matter of how to select and confirm while using the eye tracking. In this paper we combine the eye tracking with the EMG signal acquired from the skeletal muscles. Both, the advantages and limitation of the method are discussed.

This paper presents the algorithm for validation of electrodes locations for the thoracic impedance measurements. In particular the presented algorithm was designed to perform the telemetric sleep apnea monitoring. One of the problems, during the clinical tests of a developed device, was to preserve the repeatability of measurements. It strongly depended on the appropriate electrodes placement on the examined person's thorax. It seems that the developed software, which allows inspecting the electrodes localization, significantly increases a possibility of impedance based sleep apnea monitoring at home. In this article, the properties of the evaluation algorithm of electrodes placement accuracy are presented.