1,721,047 research outputs found
Environmental data clustering analysis through wearable sensing techniques: New bottom‐up process aimed to identify intra‐urban granular morphologies from pedestrian transects
No full textThe built environment modifies energy budget at its proximity and the main related effect is the so-called Urban Heat Island phenomenon consisting in higher air temperature detected in urban contexts with respect to rural surroundings. Many studies focus the attention on the existing correlations between cities morphology and Urban Heat Island intensity. Nevertheless, the urban environment is complex and heterogeneous mining that at a lower scale, distinctive microclimate conditions express intra-urban granularity. The intensity of such phenomenon is most commonly analysed by means of a network of weather stations or remote sensing. The current work proposes to analyse the intra-urban microclimate diversification by means of cluster analysis of environmental data gathered through mobile transects at pedestrian perspective. This methodology is applied to four different typologies of urban context, i.e. mainly open site, packed historical, packed low-rise buildings and packed high-rise buildings, where monitoring campaigns are carried out during both day-time and night-time. The obtained results demonstrate potentials of the method in identifying similar morphological structure on the base of row environmental data. The heterogeneity of the selected contexts demonstrates the replicability of the proposed method while suggests the selection of different number of final clusters as function of the monitored context as further development of the study
A New Wearable System for Sensing Outdoor Environmental Conditions for Monitoring Hyper-Microclimate
No full textThe rapid urbanization process brings consequences to urban environments, such poor air
quality and the urban heat island issues. Due to these effects, environmental monitoring is gaining
attention with the aim of identifying local risks and improving cities’ liveability and resilience.
However, these environments are very heterogeneous, and high-spatial-resolution data are needed to
identify the intra-urban variations of physical parameters. Recently, wearable sensing techniques
have been used to perform microscale monitoring, but they usually focus on one environmental
physics domain. This paper presents a new wearable system developed to monitor key multidomain
parameters related to the air quality, thermal, and visual domains, on a hyperlocal scale from
a pedestrian’s perspective. The system consisted of a set of sensors connected to a control unit
settled on a backpack and could be connected via Wi-Fi to any portable equipment. The device was
prototyped to guarantee the easy sensors maintenance, and a user-friendly dashboard facilitated a
real-time monitoring overview. Several tests were conducted to confirm the reliability of the sensors.
The new device will allow comprehensive environmental monitoring and multidomain comfort
investigations to be carried out, which can support urban planners to face the negative effects of
urbanization and to crowd data sourcing in smart cities
Decoding Living Lab sensing system through Bayesian networks: The preferable working space targeting comfort and productivity
No full textWorkplace environmental conditions significantly influence workers' well-being, health, and productivity. The understanding of the interaction between environmental factors across multiple domains can improve occupants' satisfaction and indoor conditions. This study aims to identify optimal office environmental conditions by analysing comfort demands and multi-domain interplay in a dynamic context. A year-long experimental campaign was performed in a Living Lab comprising five offices, monitoring indoor and outdoor environmental parameters and gathering occupants' feedback through surveys. Gaussian Naïve Bayes technique was applied to develop probabilistic models that identified optimal conditions for comfort and satisfaction, including self-perceived productivity as combined effect analysis. Participants showed more acceptability of warmer conditions in cooling seasons. Probabilistic models aligned with the current standards, indicating optimal temperatures of 22°C-24 °C for heating seasons and 23°C-24 °C for cooling seasons. Visual comfort was affected by the balance of natural and artificial light, with higher visual discomfort when the former was limited during cooling seasons. Temperatures higher than 22 °C demonstrated increased “stale air” discomfort, potentially linked to respiration and sweating, even without elevated CO2 levels. Self-perceived productivity decreased in temperatures higher than 24 °C and CO2 concentrations exceeding 800 ppm. Other factors and more comprehensive measurements, together with monitoring of physiological signals should be included in future studies, allowing the creation of guidelines for more comfortable office places. These findings offer valuable insights for enhancing workplace human-centric standards and regulations globally. They have the potential to shape policies that foster more sustainable productive environments for workers’ wellbeing worldwide
Empowering human–environment well‐being through wearable sensing: Unveiling trends and addressing gaps in the energy transition
No full textInteractions between individuals and their environment play a vital role in uncovering the energy usage of building systems and improving human well-being. The use of technology, such as wearable devices, enhances the study of people's perception of their surroundings and helps to comprehend the factors that influence individuals' satisfaction in both indoor and outdoor settings. Despite the growing number of publications in this field, there is still a lack of comprehensive understanding and exploitation of wearable sensing potential in urban planning and building operations. To address this gap, this research conducted a bibliometric review of 1661 scientific studies on the topic, identifying trends and areas where wearable applications for human-centric well-being research in the built environment are lacking. The analysis of keywords revealed a focus on the application of data analytics to process the vast amount of information collected through wearable sensors. However, the complexity of the subject necessitates cross-disciplinary and international collaborations, which are still in their early stages due to a variety of reasons. Additionally, there is a lack of research exploring the potential of multidomain studies and long-term monitoring. When considering outdoor environments, the use of people-as-sensors through wearables can significantly contribute to the development of resilient urban planning and environmental risk management in smart cities. Wearable sensing technologies offer valuable insights into people's experiences and preferences, but further research and collaboration are needed to fully harness their potential in urban planning and building operations toward the energy transition. By embracing these technologies and exploring multidomain research, more resilient and human-centric environments could enhance well-being of individuals in both indoor and outdoor contexts
A novel methodology for human thermal comfort decoding via physiological signals measurement and analysis
No full textPersonal comfort models (PCM) represent the most promising paradigm for human-centric thermal comfort in buildings. Several data sources can be used to generate a PCM: environmental data, physiological data, occupants' response. Advances in wearable sensing suggest that the use of physiological data for real time comfort measurement can be the start-up of the next generation of building design and operation with PCMs. However, proof of evidence about the adoption of non-invasive but accurate measurement methods and about correlations between physiological features and thermal sensation, are still required. This study presents the results from a large original experimental campaign aiming at human thermal comfort decoding via physiological signal. Two non-invasive wearables were used to simultaneously measure four key physiological signals (electroencephalography (EEG), Heart Rate Variability (HRV), electrodermal activity (EDA) and skin temperature (ST) on 52 subjects exposed to three different thermal conditions (namely cold, warm, and neutral) in a controlled environment. Data acquired from 219 tests were therefore analysed to determine the statistical importance of physiological features. Results showed that cold and warm thermal sensations can be uniquely identified by each physiological signal; while neutral sensation is the less distinguishable. More specifically, statistical differences (p-value <0.01) between cold and warm conditions were detected for the first time among EEGs features (Beta TP10, Gamma TP10 relative alpha TP9), time- and frequency-domain features of HRV, EDA tonic component and mean ST. Experimental results finally demonstrated that physiological measurements can identify specific thermal sensation, of crucial importance for the most advanced PCMs and for disclosing novel energy saving opportunities, accounting for people's diversities
Exploring office comfort and productivity in living labs: A yearlong structural equation modeling study
No full textThe role of a detailed microclimate monitoring in developing a cultural heritage resilience plan
No full textData collected by coupling fix and wearable sensors for addressing urban microclimate variability in an historical Italian city
No full textThis article presents the data collected through an extensive research work conducted in a historic hilly town in central Italy during the period 2016e2017. Dat
Long-Term Thermal Comfort Monitoring via Wearable Sensing Techniques: Correlation between Environmental Metrics and Subjective Perception
No full textThe improvement of comfort monitoring resources is pivotal for a better understanding of personal perception in indoor and outdoor environments and thus developing personalized comfort models maximizing occupants’ well-being while minimizing energy consumption. Different daily routines and their relation to the thermal sensation remain a challenge in long-term monitoring campaigns. This paper presents a new methodology to investigate the correlation between individuals’ daily Thermal Sensation Vote (TSV) and environmental exposure. Participants engaged in the long-term campaign were instructed to answer a daily survey about thermal comfort perception and wore a device continuously monitoring temperature and relative humidity in their surroundings. Normalized daily profiles of monitored variables and calculated heat index were clustered to identify common exposure profiles for each participant. The correlation between each cluster and expressed TSV was evaluated through the Kendall tau-b test. Most of the significant correlations were related to the heat index profiles, i.e., 49% of cases, suggesting that a more detailed description of physical boundaries better approximates expressed comfort. This research represents the first step towards personalized comfort models accounting for individual long-term environmental exposure. A longer campaign involving more participants should be organized in future studies, involving also physiological variables for energy-saving purposes
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