As the 2003 European heatwave demonstrated, overheating in homes can cause wide-scale fatalities. With temperatures and heatwave frequency predicted to increase due to climate change, such events can be expected to become more common. Thus, investigating the risk of overheating in buildings is key to understanding the scale of the problem and in designing solutions. Most work on this topic has been theoretical and based on lightweight dwellings that might be expected to overheat. By contrast, this study collects temperature and air quality data over two years for vulnerable and non-vulnerable UK homes where overheating would not be expected to be common. Overheating was found to occur, particularly and disproportionately in households with vulnerable occupants. As the summers in question were not extreme and contained no prolonged heatwaves, this is a significant and worrying finding. The vulnerable homes were also found to have worse indoor air quality. This suggests that some of the problem might be solved by enhancing indoor ventilation. The collected thermal comfort survey data were also validated against the European adaptive model. Results suggest that the model underestimates discomfort in warm conditions, having implications for both vulnerable and non-vulnerable homes.

Photoplethysmography (PPG) is a simple and cost-efficient technique that effectively measures cardiovascular response by detecting blood volume changes in a noninvasive manner. A practical challenge in the use of PPGs in real-world applications is noise reduction. PPG signals are likely to be compromised by various types of noise, such as scattering or motion artifacts, and removing such compounding noises using a monotonous method is not easy. To this end, this paper proposes a neural PPG denoiser that can robustly remove multiple types of noise from a PPG signal. By casting the noise reduction problem into a signal restoration approach, we aim to achieve a solid performance in the reduction of different noise types using a single neural denoiser built upon transformer-based deep generative models. Using this proposed method, we conducted the experiments on the noise reduction of a PPG signal synthetically contaminated with five types of noise. Following this, we performed a comparative study using six different noise reduction algorithms, each of which is known to be the best model for each noise. Evaluation results of the peak signal-to-noise ratio (PSNR) show that the neural PPG denoiser is superior in three out of five noise types to the performance of conventional noise reduction algorithms. The salt-and-pepper noise type showed the best performance, with the PSNR of the neural PPG denoiser being 36.6080, and the PSNRs of the other methods were 19.8160 and 32.8234. The Poisson noise type performed the worst, showing a PSNR of 33.0090; the PSNRs of other methods were 35.1822 and 33.4795, respectively. Thereafter, an experiment to recover a signal synthesized with two or more of the five noise types was conducted. When the number of mixed noises was two, three, four, and five, the PSNRs were 29.2759, 27.8759, 26.5608, and 25.9402, respectively. Finally, an experiment to recover motion artifacts was also conducted. The synthesized motion artifact signal was created by synthesizing only a certain ratio of the total signal length. As a result of the motion artifact signal restoration, the PSNRs were 25.2872, 22.8240, 21.2901, and 19.9577 at 30%, 50%, 70%, and 90% motion artifact ratios, respectively. In the three experiments conducted, the neural PPG denoiser showed that various types of noise were effectively removed. This proposal contributes to the universal denoising of continuous PPG signals and can be further expanded to denoise continuous signals in the general domain.

Traditional ball sport training methods often lack targeted feedback and personalized guidance, limiting beginners’ ability to develop the rapid responses and precise movements essential for such sports. This study investigates the effectiveness of integrating Visual-Haptic Feedback into simulated ball sport training to enhance body awareness and coordination without increasing cognitive load. By delivering tactile feedback to the wrists and ankles, we aim to improve beginners’ motor skills and overall performance. Our research reviews advancements in human-computer interaction focused on enhancing bodily awareness and discusses the application of tactile feedback in sports training. Experimental results demonstrate that Visual-Haptic Feedback significantly enhances beginners’ body awareness, enabling them to perceive and adjust their movements more accurately without imposing a cognitive burden. Furthermore, this approach effectively standardizes and coordinates motor performance, with a particularly notable improvement in lower limb movements. Additionally, Visual-Haptic Feedback reduces the error rate in executing complex tasks and improves the precision of limb movements. This study enriches the understanding of using Visual-Haptic Feedback in sports training and highlights its potential to improve skills and body coordination, offering valuable insights for future applications and developments in this field.

Insights from decision neuroscience raise hope for intelligent brain-inspired solutions to robot learning in real dynamic environments.

One’s own voice undergoes unique processing that distinguishes it from others’ voices, and thus listening to it may have a special neural basis for self-talk as an emotion regulation strategy. This study aimed to elucidate how neural effects of one’s own voice differ from those of others’ voices on the implementation of emotion regulation strategies. Twenty-one healthy adults were scanned using fMRI while listening to sentences synthesized in their own or others’ voices for self-affirmation and cognitive defusion, which were based on mental commitments to strengthen one’s positive aspects and imagining metaphoric actions to shake off negative aspects, respectively. The interaction effect between voice identity and strategy was observed in the superior temporal sulcus, middle temporal gyrus, and parahippocampal cortex, and activity in these regions showed that the uniqueness of one’s own voice is reflected more strongly for cognitive defusion than for self-affirmation. This interaction was also seen in the precuneus, suggesting intertwining of self-referential processing and episodic memory retrieval in self-affirmation with one’s own voice. These results imply that unique effects of one’s own voice may be expressed differently due to the degree of engagement of neural sharpening-related regions and self-referential networks depending on the type of emotion regulation.