Search

Sugary beverages are a significant contributor to sugar consumption, and their excessive consumption is associated with increased risks of elevated blood glucose levels and diabetes. Many individuals have a strong preference for sugary beverages and often find beverages with lower sugar content to be less satisfying. Attempts to switch to less sugary options are frequently short-lived, leading to a return to higher-sugar beverages. Recognizing that 75 – 95% of taste perception is influenced by scent, we investigated a scent-based approach to reduce sugar intake while preserving the perception of sweetness. This study introduces an olfactory interface in the form of a mug named “Aromug,” designed to emit a sweet scent in sync with the drinking action. Aromug incorporates motion sensing and scent presentation functions to enhance the perceived sweetness of a beverage, thereby encouraging a gradual reduction in sugar intake. Our experiments, involving 33 participants, demonstrated that the combined scents of sugar-free coffee and chocolate increased the perception of sweetness (p $= 1.641 \times 10^{-2}$ ). The study also found that the simultaneous presentation of scent and visual cues improved taste satisfaction and sweetness perception. Additionally, we observed variations in sweetness preference related to age and frequency of coffee consumption. It was particularly observed that people in their 20s and those who frequently drink coffee tend to perceive the taste of beverages as sweeter. This suggests a potential for Aromug to customize the scent experience based on individual preferences, offering a novel way to encourage healthier beverage choices.

The recent advancements in sensing technology have opened up the possibilities for various services that support daily life, such as energy-saving home appliance control. To realize such services, accurate and cost-effective daily living activity recognition in a wide range is essential. To actualize such a system, it is imperative to address the following requirements: the acquisition of sensors entails very high costs (Issue 1), it is hard to achieve precise recognition for location-independent activities like reading a book (Issue 2), a burden of wearing devices from the perspective of residents (Issue 3), and the preservation of residents’ privacy is compromised by using image data from the camera (Issue 4). In this paper, we propose a method for recognizing daily living activities utilizing Doppler sensors in a relatively longer detection range than other motion detection sensors that can be used for dynamic objects. We assess the proposed system by optimizing recognition accuracy, evaluating ensemble methods, and examining sensor reduction’s impact. In the first assessment, the logistic regression achieved the highest accuracy of 65.99% in the leave-one-person-out cross-validation. The second assessment revealed an accuracy of 59.39% for the parallel activity recognition method and 57.24% for the joint recognition method of location and activity. In the third assessment, logistic regression achieved a recognition accuracy of 65.26% when four sensor nodes were used: two sensors were placed on both sides of a participant, another was diagonally behind the participant, and the other was installed on the ceiling.

Wireless sensing represented by WiFi channel state information (CSI) is now enabling various fields of applications such as person identification, human activity recognition, occupancy detection, localization, and crowd estimation these days. So far, those fields are mostly considered as separate topics in WiFi CSI-based methods, on the contrary, some camera and vision-based crowd estimation systems intuitively estimate both crowd size and location at the same time. Our work is inspired by the idea that WiFi CSI also may be able to perform the same as the camera does. In this paper, we construct Wi-CaL, a simultaneous crowd counting and localization system by using ESP32 modules for WiFi links. We extract several features that contribute to dynamic state (moving crowd) and static state (location of the crowd) from the CSI bundles, then assess our system by both conventional machine learning (ML) and deep learning (DL). As a result of ML-based evaluation, we achieved 0.35 median absolute error (MAE) of counting and 91.4% of localization accuracy with five people in a small-sized room, and 0.41 MAE of counting and 98.1% of localization accuracy with 10 people in a medium-sized room, by leave-one-session-out cross-validation. We compared our result with percentage of non-zero elements metric (PEM), which is a state-of-the-art metric for crowd counting, and confirmed that our system shows higher performance (0.41 MAE, 81.8% of within-1-person error) than PEM (0.62 MAE, 66.5% of within-1-person error).