Nicole Morresi

This study proposes a non-invasive experimental setup that integrates environmental and physiological sensors with personal parameters (age, gender, and Body Mass Index (BMI)) to measure the perceived thermal comfort of occupants via a Machine Learning (ML) framework. An experimental campaign involving 23 participants was conducted across cold (17–20 °C), neutral (22–24 °C), and warm conditions (26–28 °C). Data collected combined environmental measurements, continuous wearable physiological signals, subjective Thermal Sensation Votes (TSVs), and a multidimensional Global Sensation Vote (GSV). Evaluating six distinct datasets via a Leave-One-Subject-Out (LOSO) cross-validation strategy revealed that the value of personal data depends heavily on the underlying sensor architecture. While integrating personal data introduced slight noise in purely environmental linear models (decreasing accuracy by 4.77%), it yielded significant quantitative increments as sensor complexity shifted toward the individual, boosting accuracy by 2.03% in physiological setups and a remarkable 13.6% in bare-minimum sensor networks. Overall predictive performance reached an accuracy of 73% using a Support Vector Machine (SVM-LIN) for environmental configurations, while an XGBoost model achieved a robust 68% accuracy in a highly reduced, low-cost sensor setup by leveraging personal context. The innovative aspect of this research is the simultaneous use of sensors to monitor both physiological and environmental parameters, which, when combined with subjective feedback and personal data, significantly increases the prediction of TSV in practical cases. This integrated approach suggests prospective applications for vulnerable categories whose thermoregulatory responses deviate from standardized models, though it requires future empirical validation among these specific demographics. Overall, the integration of personal parameters into data-driven models contributes to generalizing individual physiological differences and supports the development of more effective, human-centric IoT monitoring systems.

Urban heat exposure is one of the most critical and rapidly intensifying impacts of climate change in metropolitan areas. Reliable measurement of outdoor thermal comfort is therefore essential not only for long-term climate-adaptation planning but also for real-time heat-risk management. However, conventional approaches for measuring the Universal Thermal Climate Index (UTCI) rely either on computationally demanding radiative models or on dense in situ monitoring networks, both of which limit scalability and operational deployment at city scale. This study introduces an innovative data-driven framework for urban-scale measurement of mean radiant temperature (t_r) and UTCI using exclusively open-access reanalysis and real-time meteorological data. A supervised Multi-Layer Perceptron (MLP) model is trained to estimate t_r without requiring detailed urban morphology inputs or demanding radiometric instrumentation. The framework achieves high predictive performance (R² = 0.99; RMSE = 1.4 °C for t_r) and computational efficiency suitable for real-time applications. Another key innovation of this study lies in the comfort–vulnerability integration. The estimated outdoor thermal comfort (i.e., UTCI) is combined with socio-demographic vulnerability measurements within a unified Urban Heat Risk Index (UHRI). Applied to the Besòs district in Barcelona, the framework identifies hotspots where thermal stress and social vulnerability converge, supporting urban resilience and adaptation strategies. A Monte Carlo sensitivity and uncertainty-propagation analysis further showed limited error propagation across the framework, with standard uncertainties of 0.88 °C for MLP-based t_r, 0.67 °C for UTCI, and 0.017 for UHRI.

This study presents a methodology for measuring indoor thermal comfort assessment using data measured from environmental sensors and fed into a simplified model. The proposed measurement system enables thermal comfort evaluation using a reduced set of directly measurable mechanical and thermal quantities, namely indoor air temperature, outdoor air temperature, and relative humidity, thereby avoiding the need for full microclimatic instrumentation. To this aim, an optimized simplified Predicted Mean Vote model (sPMVopt) is introduced within a model-based measurement framework. Clothing insulation, which is not directly measurable in operational buildings, is estimated dynamically using a Generalized Additive Model (GAM) to capture the non-linear relationship between environmental variables and occupants’ clothing behavior. The resulting indirect clothing insulation estimates are subsequently approximated by third-degree polynomial functions, yielding an analytical formulation suitable for real-time implementation. Based on the distribution of the estimated insulation values, four refined clothing insulation ranges are identified, and for each range climate-specific regression coefficients are calibrated using data from the ASHRAE Global Thermal Comfort Database II, considering three representative European climate zones (France, Portugal, and Sweden). The proposed measurement system is validated against the reference PMV defined by ISO 7730 and tested in a real office environment. Results show that sPMVopt achieves mean absolute error (MAE) and mean squared error (MSE) values of 0.19 and 0.06, respectively, corresponding to improvements of 13% and 17% compared to existing reference methodologies (e.g. PMV). A sensitivity analysis also shows that air temperature is the input variable that causes a higher deviation sPMVopt. Overall, the study demonstrates that reliable thermal comfort measurement can be achieved through a simplified sensing setup and a validated model-based estimation process, enabling scalable and real-time comfort monitoring for building diagnostics, HVAC commissioning, and smart-building applications.

Traditional methods for personalized thermal comfort measurement rely on subjective questionnaires, which are often impractical to collect in real-world contexts due to their intrusiveness, discontinuity, and dependency on user. To overcome these limitations, this study proposes an innovative methodology for the assessment of thermal comfort measuring solely physiological data, thereby eliminating the need for feedback collection. A non-intrusive sensing system comprising a wearable smartwatch, an infrared temperature sensor, and a social robot, has been employed to measure occupants’ skin temperature and heart rate variability under varying indoor thermal conditions. K-means clustering was applied to unlabeled physiological features. A silhouette score of 0.52 has been obtained with physiological data collected during winter season measurement campaign, revealing that it is possible to discern comfort and discomfort sensation based on solely physiological signals. The results demonstrate the feasibility and robustness of a physiology-based, label-free approach for thermal comfort measurement, which provides a scalable alternative to survey-based methods.

Air pollution is a major environmental and public health challenge, especially in urban areas where fine-grained air quality data are essential to effective interventions. Traditional monitoring networks, while accurate, often lack spatial resolution and public engagement. This study presents a novel wearable wireless sensor node (WSN) that was developed within the Horizon Europe SOCIO-BEE project to support air quality monitoring through citizen science (CS). The low-cost, body-mounted WSN measures NO₂, O₃, and PM_2.5. Three pilot campaigns were conducted in Ancona (Italy), Maroussi (Greece), and Zaragoza (Spain), and involved diverse user groups—seniors, commuters, and students, respectively. PM_2.5 sensor data were validated through two approaches: direct comparison with reference stations and spatial clustering analysis using K-means. The results show strong correlation with official PM_2.5 data (R² = 0.75), with an average absolute error of 0.54 µg/m³ and a statistical confidence interval of ±3.3 µg/m³. In Maroussi and Zaragoza, where no reference stations were available, the clustering approach yielded low intra-cluster coefficients of variation (CV = 0.50 ± 0.40 in Maroussi, CV = 0.28 ± 0.30 in Zaragoza), indicating that the measurements had high internal consistency and spatial homogeneity. Beyond technical validation, user engagement and perceptions were evaluated through pre-/post-campaign surveys. Across all pilots, over 70% of participants reported satisfaction with the system’s usability and inclusiveness. The findings demonstrate that wearable low-cost sensors, when supported by a structured engagement and data validation framework, can provide reliable, actionable air quality data, empowering citizens and informing evidence-based environmental policy.

This study presents a preliminary prototype of a sensor-based system for assessing cognitive status through finger force modulation. The system integrates strain-gauge load cells within a game-like interface, prompting users to apply and maintain specific force levels using their index finger. The resulting force-time profiles are analyzed and compared to a template curve, using Dynamic Time Warping (DTW) and Partial Curve Mapping (PCM), generating a Similarity Index (SI) that quantifies task accuracy. A thresholding system classifies participants into cognitive status levels (High, Medium, Low). The sensing platform was calibrated using standard weights to ensure reliable force measurement. The prototype was tested on 24 participants (16 young adults and 8 seniors) and results showed that PCM outperforms DTW with the 80 % of young adults achieved a "High" cognitive status, while among seniors, 79 % were classified as "Medium" and 6 % as "Low”. These findings confirm that age-related motor variability can reflect underlying cognitive differences. The proposed system demonstrates potential for non-invasive, remote cognitive assessment and supports future development of digital tools for aging populations and individuals diagnosed with Mild Cognitive Impairment (MCI) or dementia (PwD).

This study proposes a calibration procedure for a portable point-of-care (POC) bilirubinometer that adjusts optical measurements of total blood bilirubin by accounting for the interference caused by oxyhemoglobin concentration. For this purpose, a neonatal POC bilirubinometer was used, and optical measurements were conducted at 455 nm and 575 nm wavelengths on samples containing different concentrations of bovine total serum bilirubin (TSB): 0, 16.7, 12.8, 8.4, 5.3, 3.1 mg/dl) and human-derived oxyhemoglobin (O₂Hb: 0, 5, 10, 20, 40, 80 mg/dl). The voltage outputs from these samples were used to generate a calibration curve. In the pure TSB samples, the accuracy was ±0.08 mg/dl, the precision was 0.32 mg/dl, and the mean absolute percentage error (MAPE) was 23.9%. When using the model that incorporates samples with both TSB and O₂Hb, the accuracy increased at ±0.04 mg/dl, the precision to 0.30 mg/dl, and the MAPE to 15.4%, showing a significant improvement in measurement performances when the O₂Hb interference is taken in consideration. The study concludes that incorporating O₂Hb concentrations into the calibration procedure for neonatal POC bilirubinometers significantly reduces interference and enhances the accuracy and precision of TSB concentrations.

「福祉科技與服務管理學刊」創辦於2012年12月，由中華福祉科技與服務管理學會發行，旨在提供老人福祉科技(gerontechnology)及高齡者照護服務與管理之相關學者、產業界和從業人員，以中文發表研發成果或實務經驗之平台，期能促進跨領域知識交流與傳播，提升整體研究水平與相關產業發展，進而為高齡化社會衍生之各種問題提出具體解決方案，提升高齡者之生活品質。