This study presents a real-time system for detecting sheath damage in multicore wire production, addressing the limitations of conventional high-voltage testing. After evaluating multiple sensing techniques, laser-diffused reflection emerged as the most reliable, non-intrusive, and effective for continuous monitoring. A calibrated system combining a laser source and photodetector was implemented and tested on a live production line, achieving 83.75% detection accuracy. The system maintained consistent performance across wire colors and insulation conditions. This advancement offers a safer, more efficient alternative for in-process quality control. Future work aims to enhance robustness and incorporate intelligent algorithms to further optimize detection accuracy.

Wireless sensor networks (WSNs) implemented in the paradigm of the Internet of Things (IoT) are characterized by a large number of distributed sensor nodes that make measurements in-the-field and communicate with other sensor nodes and servers in the cloud by means of wireless technology. Sensor-to-microcontroller direct interface (SMDI) is a technique used for the measurement of resistive sensors without the use of an ADC. In SMDI based measurements, the sensor is directly interfaced with the digital input-output pins of the general purpose input output (GPIO) interface of microcontrollers and FPGAs. Compared with the measurements per-formed with an ADC, SMDI is characterized by lower cost and lower power consumption. In this paper, the impact of noise on the accuracy of resistive sensor measurements using SMDI is investigated. The study was carried out by LTSpice electrical level simulations and validated by preliminary experimental measurements, where a set of resistances in the range from 100 Ω to 10 kΩ were measured by SMDI under different levels of noise. For each operative condition, the simulations were also carried out in the case of measurements performed with a 12-bit ADC and the achieved accuracy for the measured resistances was compared with the results achieved by SMDI. The results have shown that noise can seriously impact the measured accuracy of resistive sensors by SMDI and, differently from the ADC measurements, the accuracy cannot be improved by averaging on multiple measurements. A mitigation strategy to estimate the noise level and to improve the measurement accuracy of resistive sensors by SMDI was also proposed.

The field deployment of low-cost air quality sensors systems enables enhanced spatial resolution in air quality monitoring. Although these sensor systems cannot achieve the same accuracy as regulatory monitoring stations, they can attain acceptable levels of confidence and provide Indicative Measurements as regulated by Ambient Air Quality EU Directive. The integration of an anemometer into a system can provide additional information for the classification of the measurement area, the identification of potential sources of pollutant emissions, and the assessment of the device’s operating conditions during measurement. The measurement capabilities of an Airbox, a low-cost air quality sensor system, have been extended through the integration of a DW6410 anemometer (Davis Instruments). The Airbox, designed to transmit data in real-time or near real-time to servers and IoT platforms, was deployed for a duration of 4 months, from October 2021 to February 2022, within the airport area of Grottaglie (Southern Italy). The anemometric measurements and particulate concentration data (PM_2.5 and PM₁₀, measured by NextPM sensor, Tera Sensor) were integrated and compared to meteorological open data and data from a regulatory regional air quality control network located in the area around the airport.

Accurate and continuous tracking of athletes is essential to meet the infotainment demands and health and safety requirements of major marathon events. However, the current ability to track individual athletes or groups at mass sporting events is severely limited by the weight, size and cost of the equipment required. In marathons, Radio Frequency Identification (RFID) technology is typically used for timing, but can only provide accurate tracking at widely spaced intervals, relying on heuristic and interpolation algorithms to estimate runners’ positions between measurement points. Alternative IOT solutions, such as Low Power Wide Area Network (LWPAN), have limitations in terms of range and require dedicated infrastructure and regulation. Instead, we analysed the potential use of smartwatches as accurate and continuous tracking devices for athletes, assessing battery consumption during tracking and standby drain, achievable GPS tracking accuracy and the update rate of data transfer from the device in urban environments. The 4G LTE battery drain is different from non-urban areas. Analysis of standby usage is necessary as devices need to conserve power for tracking. We programmed an application that allowed us to control the modalities of acquisition and transmission intervals, integrating advanced logging and statistics at runtime, and evaluated the achievable results in major marathon events. Our empirical evaluation at the Frankfurt, Athens and Vienna marathons with three different types of smartwatch tracking platforms showed the validity of this approach, while respecting some necessary limitations of the tracking settings. Median battery drain was 5.3%/hr in standby before race start (σ 1.5) and 16.5%/hr in tracking mode (σ 3.29), with an actual update rate varying between 19-57s on Wear OS devices. The average GPS offset to the track was 4.5 m (σ 8.7). Future work will focus on integrating these consumer devices with existing time and tracking infrastructure.

Cardiovascular exercise strengthens the heart and improves circulation, but most people struggle to fit regular workouts into their day. Short bursts of vigorous activity, sometimes called exercise snacks, can raise the heart rate and deliver meaningful health benefits. Accurate, real time monitoring of cardio exercises is essential to ensure that these workouts meet recommended intensity and rest guidelines. This paper proposes a Tiny Machine Learning (TinyML) wearable system that tracks the duration and type of common cardio exercises in real time. A compact device containing a six axis inertial measurement unit (IMU) is worn on the arm. The device streams accelerometer data to an on device neural network model, which classifies exercises such as jumping jacks, squat jumps, jogging in place, and a resting state. The TinyML model is trained with labelled motion data and deployed on a microcontroller using quantization to meet memory and latency constraints. Preliminary tests with ten participants show that the system correctly recognizes the targeted exercises with around 95% accuracy and an average F1 score of 0.93 while maintaining inference latency below 100 ms and a memory footprint under 60 KB. By prompting users to alternate 30–60 s of high intensity exercise with rest periods, the device can structure effective interval routines. This work demonstrates how TinyML can enable low cost, low power wearables for personalised cardiovascular exercise monitoring.

Wearables such as smartwatches provide opportunity for large-scale cardiovascular health monitoring. Wearables often use photoplethysmography (PPG), an optical sensing technique, to measure the arterial pulse wave and derive insights into cardiovascular physiology. Whilst there has been much research into the shape and physiological determinants of the finger-PPG pulse wave, much less is known about the wrist-PPG pulse wave. The aim of this study was to describe the morphology of wrist-PPG pulse waves and compare them with finger-PPG pulse waves. We analyzed wrist-PPG recordings from 686 adults in the Aurora-BP dataset. Visual inspection of pulse wave shapes revealed five classes of PPG pulse waves, three of which are similar to those seen in finger-PPG pulse waves, and two of which were different. An algorithm was developed to automatically classify wrist-PPG pulse waves, and revealed variability in pulse wave shape within and between subjects. A multivariable regression analysis of associations between subject metadata and two features of pulse wave shape indicated that wrist-PPG pulse wave shape is associated with heart rate, body size (BMI and height) and blood pressure. No significant associations with age were observed, in contrast to previous findings on finger-PPG pulse waves. The differences observed between wrist- and finger-PPG pulse wave shapes indicate a need for greater understanding of the physiological origins of the wrist-PPG pulse wave, and adaptation of algorithms specifically for wrist-PPG analysis.

The second derivative of the photoplethysmographic signal presents five relevant points which provide information about the structural properties of arteries. This study investigates the ratio between the amplitude of the d wave (end of systole) and the a wave (beginning of systole) as a potential indicator of vascular aging. The research combines an in vitro study on silicone models with different stiffness and an in vivo study on volunteers aged between 26 to 63 years. The results showed a strong negative correlation between the d/a ratio and arterial stiffness, confirming the potential of this parameter as a noninvasive index for assessing vascular health status.

The motivation of our work was to analyze influence of changes in humidity and temperature on temporal features of sensed photoplethysmography (PPG) waves. This paper describes a special prototype of a wearable PPG multi-sensor with integrated I2C humidity and thermo meters to carry out measurements in three skin moisture levels. This sensor is supplemented with a force-sensitive resistor for measurement of the physical contact-pressure between the measuring probe and the skin surface next usable to sense heart pulsation on the wrist radial artery. The realized experiments show that the performed skin manipulation (skin drying, moistening) was always detectable; the PPG signal range is mainly affected, while changes in signal ripple and heart rate variance are smaller. The detailed analysis per a hand and gender type yielded differences between male and female subjects, results of left and right hands differ less.

In the era of smart garments, textile electrodes for electromyography (EMG) or functional electric stimulation (FES) represent a very interesting and promising area of development and exploitation. In this frame, we conducted a patent landscape analysis of textile solution for EMG sensing and FES actuation, using Espacenet as a reference database and Orbit Intelligent platform as a data analysis tool. The landscape analysis focused on the following aspects: filing trends, top applicants in this domain, main publication countries, forward citations, and collaborations between applicants. Following the screening process, a total of 97 patent families were subjected to subsequent analysis. China and the United States account for the majority of patents. The main applicants by volume of the topic studied are universities or research public entities.

Here, we report a facile technique for fabricating inkjet-printed PEDOT:PSS thermally active devices on commercial tattoo paper, subsequently transferred to Kapton substrate with pre-patterned copper tracks, to enable integration with other electronic systems. Printing parameters were investigated for consistent film quality. Electrical and thermal characterization confirmed stable ohmic behavior; after transfer, the device exhibited superior contact performance with lower measured electrical resistance. Temperature coefficient of resistance (TCR) of −0.0164 °C⁻¹ was measured, indicating the device’s capability for accurate temperature sensing. Additionally, a temperature exceeding 37 °C was achieved with a power consumption of approximately 50 mW. This work presents a robust method for passivating and transferring electronics for on-skin applications.