The central aortic pressure (CAP) is traditionally measured using a pressure sensor inserted into aorta. This invasive procedure is costly and can lead to complications. Therefore, attention is paid to the development of non-invasive methods. Tonometry sensors are mostly used for getting the primary pressure information non-invasively from the radial artery. Authors of the paper introduced a method of electrical bioimpedance (EBI) measurements on the radial artery to create a more robust method than tonometry. The main attention is paid on the synthesis of transfer functions connecting the central aortic and radial blood pressure curves. Finally it is concluded that the bioimpedance sensing method is at least a useful complementary component to the tonometry or can even replace it in the future developments.

Background: Wearable technologies for monitoring cardiovascular parameters, including electrocardiography (ECG) and impedance cardiography (ICG), propose a challenging research subject. The expectancy for wearable devices to be unobtrusive and miniaturized sets a goal to develop smarter devices and better methods for signal acquisition, processing, and decision-making. Methods: In this work, non-standard electrode placement configurations (EPC) on the thoracic area and single arm were experimented for ECG signal acquisition. The locations were selected for joint acquisition of ECG and ICG, targeted to suitability for integrating into wearable devices. The methodology for comparing the detected signals of ECG was developed, presented, and applied to determine the R, S, and T waves and RR interval. An algorithm was proposed to distinguish the R waves in the case of large T waves. Results: Results show the feasibility of using non-standard EPCs, manifesting in recognizable signal waveforms with reasonable quality for post-processing. A considerably lower median sensitivity of R wave was verified (27.3%) compared with T wave (49%) and S wave (44.9%) throughout the used data. The proposed algorithm for distinguishing R wave from large T wave shows satisfactory results. Conclusions: The most suitable non-standard locations for ECG monitoring in conjunction with ICG were determined and proposed.

A test and evaluation platform has been developed to research and develop solutions and algorithms for joint acquisition, processing, and reasoning from the electrical bioimpedance, electro-cardiography, and photoplethysmography (EBI, ECG, and PPG) signals. The solution is based on the low-cost Launchpad of the Texas Instrument TMS320F28379D dual core 16/32-bit DSP with 16-bit multichannel ADC and several other peripherals (PWM, ADC, timers, I2C, SPI, a lot of SRAM and flash memory). In the simplest mode, the platform is used to simultaneously acquire the EBI, ECG, and PPG signals at 1 kHz and send them to the PC by virtual COMport, for further analysis and presentation. The EBI, ECG, and PPG signals are sampled with a 16-bit ADC and have a resolution of more than four decimal digits, with an EBI error of less than 0.002%. In the next stage, various embedded and ”at the edge” measurement algorithms will be developed, tested, demonstrated, and evaluated on this platform.

Anomaly detection and fall prevention represent one of the key research areas within gait analysis for patients suffering from neurological disorders. Deep Learning has penetrated into healthcare applications, encompassing disease diagnosis and anomaly prediction. Connected wearable medical sensors are emerging due to computationally expensive machine learning tasks, which traditionally require use of remote PC or cloud computing. However, to reduce needs for wireless communication channel throughput, for data processing latency, and increase service reliability and safety, on device machine learning is gaining attention. This paper presents an innovative approach that leverages one dimensional convolutional neural network (1D-CNN) and long-short term memory (LSTM) neural network for the real-time detection of abnormal gait patterns during the step. Real-time anomaly detection pertains to the algorithm’s ability to promptly detect true gait abnormality occurrence during the swing phase of an ongoing step.For the experiments, we have collected eight different common gait anomalies, simulated by 22 persons, using motion sensors containing multidimensional inertial measurement units (IMUs).Results have demonstrated that the proposed 1D-CNN-AD algorithm achieves an average accuracy of 95% and an average F1-score of 88% for all gait types and can run in true real-time. Average earliness for 1D-CNN-AD algorithm was 0.6 s, which is mid-swing phase of the step. Proposed LSTM-AD algorithm achieved average accuracy of 87% and average F1-score of 70% for all gait types.

Changes in a certain parameter are often a few magnitudes smaller than the base value of the parameter, specifying significant requirements for the dynamic range and noise levels of the measurement system. In case of electrical bioimpedance acquisition, the variations can be 1000 times smaller than the entire measured value. Synchronous or lock-in measurement of these variations is discussed in the current paper, and novel measurement solutions are presented. Proposed methods are simple and robust when compared to other applicable solutions. A common feature shared by all members of the group of the proposed solutions is differentiation. It is achieved by calculating the differences between synchronously acquired consecutive samples, with lock-in integration and analog differentiation. All these methods enable inherent separation of variations from the static component of the signal. The variable component of the bioimpedance can, thus, be acquired using the full available dynamic range of the apparatus for its detection. Additive disturbing signals and omnipresent wideband noise are considered and the method for their reduction is proposed.