In this editorial, an overview of the content of the Special Issue on “Wireless Sensor Networks for Environmental Monitoring” is provided

Objective: This paper proposes the development of a novel electromagnetic tracking system for navigation surgery. Main objective is to provide a system able to operate in a wide tracking volume to make easier and efficient the surgical procedures by assuring high measurement accuracy. Methods: A new field generator consisting in five transmitting coils excited with Frequency Division Multiplexing technique has been developed. Attention is devoted to designing and arrangement of the coils to assure high sensitivity, system scalability and a homogeneous magnetic field inside working volume. A suitable technique based on Look-Up-Table is applied for sensor position calculation and an anthropomorphic robot is used for table calibration. Results: Experimental tests highlight a good repeatability of the measurement data and a negligible noise influence for the proposed system. The obtained tracking volume is wider with respect to the commercial tracking device used in surgical applications and seem promising. Conclusion: The main characteristic of the developed system consists of: scalable and modular configuration of Field Generator, high measured sensitivity due to the increased number of transmitting coils with respect to the classical configuration and large tracking volume. The development of the proposed magnetic tracking systems with high accuracy and wide working volume allows to promote broader utilization of advantaged techniques in surgery procedures for both improving the effectiveness and decreasing the invasiveness of medical interventions.

In this paper, a machine learning (ML) approach to estimate blood pressure (BP) using photoplethysmography (PPG) is presented. The final aim of this paper was to develop ML methods for estimating blood pressure (BP) in a non-invasive way that is suitable in a telemedicine health-care monitoring context. The training of regression models useful for estimating systolic blood pressure (SBP) and diastolic blood pressure (DBP) was conducted using new extracted features from PPG signals processed using the Maximal Overlap Discrete Wavelet Transform (MODWT). As a matter of fact, the interest was on the use of the most significant features obtained by the Minimum Redundancy Maximum Relevance (MRMR) selection algorithm to train eXtreme Gradient Boost (XGBoost) and Neural Network (NN) models. This aim was satisfactorily achieved by also comparing it with works in the literature; in fact, it was found that XGBoost models are more accurate than NN models in both systolic and diastolic blood pressure measurements, obtaining a Root Mean Square Error (RMSE) for SBP and DBP, respectively, of 5.67 mmHg and 3.95 mmHg. For SBP measurement, this result is an improvement compared to that reported in the literature. Furthermore, the trained XGBoost regression model fulfills the requirements of the Association for the Advancement of Medical Instrumentation (AAMI) as well as grade A of the British Hypertension Society (BHS) standard.

Additive manufacturing technologies allow the fabrication of smart objects, which are made up of a dielectric part and an embedded sensor able to give real-time feedback to the final user. This research presents the characterization of a low-cost 3D-printed strain sensor, fabricated using material extrusion (MeX) technology by using a conductive material composed of a polylactic acid (PLA)-based matrix doped with carbon black and carbon nanotubes (CNT), thus making the plastic conductive. A suitable measurement set-up was developed to perform automatic characterization tests using a high repeatability industrial robot to define either displacement or force profiles. The correlation between the applied stimulus and the variation of the electrical resistance of the 3D-printed sensor was evaluated, and an approach was developed to compensate for the effect of temperature. Results show that temperature and hysteresis affect repeatability; nevertheless, the sensor accurately detects impulse forces ranging from 10 g to 50 g. The sensor showed high linearity and exhibited a sensitivity of $0.077{\mathsf{\Omega }\mathrm{g}}^{-1}$ and $12.54 {\mathsf{\Omega }\mathrm{mm}}^{-1}$ in the force and displacement range of 114 g and 0.7 mm, respectively, making them promising due to their low cost, ease of fabrication, and possible integration into more complex devices in a single-step fabrication cycle.

In this paper, new features relevant to blood pressure (BP) estimation using photoplethysmography (PPG) are presented. A total of 195 features, including the proposed ones and those already known in the literature, have been calculated on a set composed of 50,000 pulses from 1080 different patients. Three feature selection methods, namely Correlation-based Feature Selection (CFS), RReliefF and Minimum Redundancy Maximum Relevance (MRMR), have then been applied to identify the most significant features for BP estimation. Some of these features have been extracted through a novel PPG signal enhancement method based on the use of the Maximal Overlap Discrete Wavelet Transform (MODWT). As a matter of fact, the enhanced signal leads to a reliable identification of the characteristic points of the PPG signal (e.g., systolic, diastolic and dicrotic notch points) by simple means, obtaining results comparable with those from purposely defined algorithms. For systolic points, mean and std of errors computed as the difference between the locations obtained using a purposely defined already known algorithm and those using the MODWT enhancement are, respectively, 0.0097 s and 0.0202 s; for diastolic points they are, respectively, 0.0441 s and 0.0486 s; for dicrotic notch points they are 0.0458 s and 0.0896 s. Hence, this study leads to the selection of several new features from the MODWT enhanced signal on every single pulse extracted from PPG signals, in addition to features already known in the literature. These features can be employed to train machine learning (ML) models useful for estimating systolic blood pressure (SBP) and diastolic blood pressure (DBP) in a non-invasive way, which is suitable for telemedicine health-care monitoring.

In this paper we present a study of the repeatability of an innovative electromagnetic tracking system (EMTS) for surgical navigation, developed to overcome the state of the art of current commercial systems, allowing for the placement of the magnetic field generator far from the operating table. Previous studies led to the development of a preliminary EMTS prototype. Several hardware improvements are described, which result in noise reduction in both signal generation and the measurement process, as shown by experimental tests. The analysis of experimental results has highlighted the presence of drift in voltage components, whose effect has been quantified and related to the variation of the sensor position. Repeatability in the sensor position measurement is evaluated by means of the propagation of the voltage repeatability error, and the results are compared with the performance of the Aurora system (which represents the state of the art for EMTS for surgical navigation), showing a repeatability error about ten times lower. Finally, the proposed improvements aim to overcome the limited operating distance between the field generator and electromagnetic (EM) sensors provided by commercial EM tracking systems for surgical applications and seem to provide a not negligible technological advantage.