Sleep posture is a key factor in assessing sleep quality, especially for individuals with Obstructive Sleep Apnea (OSA), where the sleeping position directly affects breathing patterns: the side position alleviates symptoms, while the supine position exacerbates them. Accurate detection of sleep posture is essential in assessing and improving sleep quality. Automatic sleep posture detection systems, both wearable and non-wearable, have been developed to assess sleep quality. However, wearable solutions can be intrusive and affect sleep, while non-wearable systems, such as camera-based approaches and pressure sensor arrays, often face challenges related to privacy, cost, and computational complexity. The system in this paper proposes a microcontroller-based approach exploiting the execution of an embedded machine learning (ML) model for posture classification. By locally processing data from a minimal set of pressure sensors, the system avoids the need to transmit raw data to remote units, making it lightweight and suitable for real-time applications. Our results demonstrate that this approach maintains high classification accuracy (i.e., 0.90 and 0.96 for the configurations with 6 and 15 sensors, respectively) while reducing both hardware and computational requirements.

In this paper, the WirelessHART communication scheme is investigated. The protocol is at first described, in order to underline the key aspects of its design; then, a simulation tool is presented: it enables analysis that accounts for both protocol and physical layers issues. In particular, interference effects on protocol choices can be easily analyzed. The experimental results, obtained from simulations, highlight improvements of WirelessHART with respect to IEEE 802.15.4 standard, concerning the Packet Error Ratio (PER) degradation of interfering networks.

The paper proposes the development of an innovative prototype of smart orthosis with a fully integrated multi-electrodes matrix for electromyography (EMG), to improve non-invasive personalized recording during rehabilitation. Both electrodes and conductive tracks were effectively printed onto the three-dimensional (3D) surface of the orthosis through Aerosol Jet Printing. Results from morphological and electrical characterization of printed elements showed an average thickness of 22.2 μm (relative standard deviation of 11%) with average resistivity of about 51∙10-8 Ω∙m (relative standard deviation of 10%) and an electrode-to-skin impedance comparable to the one of commercial dry electrodes. Portability and comfort were enabled by customized light-weight conditioning electronics attached to the orthosis allowing wireless data transmission. Muscular activity from three subjects was then evaluated while performing the same tasks involving multiple muscles. Results confirmed the ability of the device to monitor the activity of gastrocnemius muscle during both a sit-to-stand task and isometric contractions, both for intra- and inter-subjects’ analyses. A comparison with commercial surface EMG electrodes and with literature confirmed similar features both in time and frequency. Overall, the results presented suggest the possibility to exploit the potential to print customized electrodes onto 3D surfaces to fabricate smart personalized wearable orthoses useful to capture valuable feedback to improve effectiveness, consciousness, and interactivity during daily activities and specific exercises, for both patient and medical personnel.

In the last years, increasing interest has been addressed on wearable devices for the continuous monitoring of biochemical profiles of patients and athletes. Among various markers, lactate represents one of the most interesting to deepen the knowledge on fatigue processes, in combination with traditional electromyographic features. In this work, we propose a new, low-cost, rapid and flexible electrochemical amperometric lactate carbon biosensor fabricated by the novel technique of Aerosol Jet Printing. The developed biosensors were combined with a paper-based microfluidic system and a chitosan-based stable functionalization to allow a long-term continuous monitoring of human daily activities. The sensor performances were evaluated by static and dynamic electrochemical tests in buffered media. The limit of detection is 11±2 mM for the static setup and of 15±6 mM for the dynamic setup, while sensitivity is 0.02 μA/mM and 0.04 μA/mM, respectively, with single sigma standard deviation lower than 8%. Increasing and decreasing concentration steps of lactate induces comparable response times below 30 s. These results obtained in-vitro under controlled laboratory conditions represent a promising starting point to pursue with ex-vivo and in-vivo validation for the future development of stand-alone wearable patches for non-invasive lactate measurements.

In the paper we present a behavioral simulation of PTP synchronization, based on the OMNeT++ simulator. Node, link and traffic source behavior are represented by statistical source and flow models. This approach, which differs from most approaches to PTP analysis, allows to focus on fundamental aspects of analysis and can be useful to assess the suitability of PTP synchronization in relation to network architecture and operating conditions, as well as for the optimization of clock servo design.

Multi-channel measurements from the maternal abdomen acquired by means of dry electrodes can be employed to promote long-term monitoring of fetal heart rate (fHR). The signals acquired with this type of electrode have a lower signal-to-noise ratio and different artifacts compared to signals acquired with conventional wet electrodes. Therefore, starting from the benchmark algorithm with the best performance for fHR estimation proposed by Varanini et al., we propose a new method specifically designed to remove artifacts typical of dry-electrode recordings. To test the algorithm, experimental textile electrodes were employed that produce artifacts typical of dry and capacitive electrodes. The proposed solution is based on a hybrid (hardware and software) pre-processing step designed specifically to remove the disturbing component typical of signals acquired with these electrodes (triboelectricity artifacts and amplitude modulations). The following main processing steps consist of the removal of the maternal ECG by blind source separation, the enhancement of the fetal ECG and identification of the fetal QRS complexes. Main processing is designed to be robust to the high-amplitude motion artifacts that corrupt the acquisition. The obtained denoising system was compared with the benchmark algorithm both on semi-simulated and on real data. The performance, quantified by means of sensitivity, F1-score and root-mean-square error metrics, outperforms the performance obtained with the original method available in the literature. This result proves that the design of a dedicated processing system based on the signal characteristics is necessary for reliable and accurate estimation of the fHR using dry, textile electrodes.