The perception of sound greatly impacts users’ emotional states, expectations, affective relationships with products, and purchase decisions. Consequently, assessing the perceived quality of sounds through jury testing is crucial in product design. However, the subjective nature of jurors’ responses may limit the accuracy and reliability of jury test outcomes. This research explores the utility of facial expression analysis in jury testing to enhance response reliability and mitigate subjectivity. Some quantitative indicators allow the research hypothesis to be validated, such as the correlation between jurors’ emotional responses and valence values, the accuracy of jury tests, and the disparities between jurors’ questionnaire responses and the emotions measured by FER (facial expression recognition). Specifically, analysis of attention levels during different statuses reveals a discernible decrease in attention levels, with 70 percent of jurors exhibiting reduced attention levels in the ‘distracted’ state and 62 percent in the ‘heavy-eyed’ state. On the other hand, regression analysis shows that the correlation between jurors’ valence and their choices in the jury test increases when considering the data where the jurors are attentive. The correlation highlights the potential of facial expression analysis as a reliable tool for assessing juror engagement. The findings suggest that integrating facial expression recognition can enhance the accuracy of jury testing in product design by providing a more dependable assessment of user responses and deeper insights into participants’ reactions to auditory stimuli.

This paper presents a methodology for the application of electroencephalographic (EEG) Entropy measurements for indoor thermal comfort estimation. Wearables have been demonstrated to be capable of providing accurate physiological measurements to interpret individual thermal responses. Several studies demonstrated the correlation between the EEG Power Spectrum Density (PSD) variation and the subjects' responses exposed to different ambient temperatures. We present a complementary approach based on Approximate Entropy (ApEn) of EEG as a measure for the predictability of EEG series in describing the human thermal condition. We analysed the ApEn of EEG signals acquired from 24 subjects, exposed to three different temperatures (cold: 16°C; neutral: 25°C; warm: 33°C) in a controlled environment, by 4-channels wearable EEG sensors (256 Hz sampling frequency). Statistical analysis showed for both anterior frontal and temporoparietal sites significant differences between neutral, cold, and warm conditions, with a higher value of ApEn in the neutral one. In the anterior frontal area, there was a significative trend of ApEn with smaller values from the neutral to the warm condition, with the cold intermediate. The outcome opens the scenario up to innovative measurement systems, based on wearable EEG devices, for the application of personal comfort models to indoor environmental monitoring and control.

The Human Experience in Regulated Offices (HERO) dataset is a synchronized, multimodal resource dataset designed to characterize human physiological and perceptual dynamics during realistic office activities in a controlled indoor setting. The dataset comprises data gathered on 24 voluntary healthy adults, each completing two laboratory sessions under distinct thermal regimens: Lower Temperature (LT; 22–24 °C) and Higher Temperature (HT; 30–32 °C), regulated within ±0.5 °C, conducted during the summer season in Köppen–Geiger Csa climate classification (temperate with dry and hot summer). In each session, participants followed a standardized nine-phase office-work protocol comprising an acclimation baseline, activity blocks, and interleaved washout periods, enabling analyses that explicitly account for activity type, sequencing, and recovery. The release integrates four-channel electroencephalography (EEG) acquired with a Muse-2 headband, electrodermal activity (EDA), and peripheral skin temperature (ST) recorded using an EmotiBit wrist-worn wearable sensor, indoor multi-domain environmental measurements acquired via the NEXT.ROOM facility, and phase-aligned surveys capturing multi-domain perceptions of the indoor environment and task experience. Visual context, such as, derived video quality check (QC)/coverage features, was recorded using laptop webcams. All data are distributed as level-0 device exports, restricted to the experimental window and enriched with consistent session identifiers and phase labels, while intentionally avoiding downstream preprocessing choices. The dataset is archived on Zenodo (DOI: 10.5281/zenodo.17957768) with documentation and code in the associated project’s GitHub repository.

The digitalisation of the deep renovation process and built environment is enabled by ubiquitous connectivity and monitoring of the environment itself, the artefacts and actors within it, and events that occur. Such monitoring is important for efficient construction management, dynamic peak demand reduction, affordability, and occupants’ well-being. Sensor networks based on Internet of Things (IoT) technologies represent an important prerequisite for both optimising and redefining the stages of the building process to meet environmental challenges. This chapter provides an overview of how computation capabilities are being integrated into the physical environment and the role of sensor networks in the context of deep renovation. The key advantages and benefits of these technologies at the pre, during and post-renovation stages are discussed together with different use cases. The value of sensor network infrastructures and the legal and ethical implications of the use of such sensor infrastructures is also discussed.