Inappropriate alcohol drinking may cause health and social problems. Although controlling the intake of alcohol is effective to solve the problem, it is laborious to track consumption manually. A system that automatically records the amount of alcohol consumption has a potential to improve behavior in drinking activities. Existing devices and systems support drinking activity detection and liquid intake estimation, but our target scenario requires the capability of determining the alcohol concentration of a beverage. We present Al-light, a smart ice cube to detect the alcohol concentration level of a beverage using an optical method. Al-light is the size of 31.9 x 38.6 x 52.6 mm and users can simply put it into a beverage for estimation. It embeds near infrared (1450 nm) and visible LEDs, and measures the magnitude of light absorption. Our device design integrates prior technology in a patent which exploits different light absorption properties between water and ethanol to determine alcohol concentration. Through our revisitation studies, we found that light at the wavelength of 1450 nm has strong distinguishability even with different types of commercially-available beverages. Our quantitative examinations on alcohol concentration estimation revealed that Al-light was able to achieve the estimation accuracy of approximately 2 % v/v with 13 commercially-available beverages. Although our current approach needs a regressor to be trained for a particular ambient light condition or the sensor to be calibrated using measurements with water, it does not require beverage-dependent models unlike prior work. We then discuss four applications our current prototype supports and future research directions.
過度な飲酒は， 健康面や社会的な面で様々な問題を引き起こし得る．アルコール摂取量を自動的に計測し，ユーザの飲酒を定量的に管理することが可能となれば，これらの問題の予防に貢献できる．しかしながら，アルコール摂取量を計測する上で知る必要があるアルコール濃度について，これを手軽に計測するスマートデバイスは知られていない．そこで我々は，近赤外・可視光LED と光検出器を搭載し，水とアルコールの光吸収特性を利用してアルコール濃度計測を行うスマートアイスキューブを提案する．本研究で作成したプロトタイプは3 x 4 x 5 cm の直方体であり，ユーザがこのデバイスをグラスの中に入れるだけで，通常の飲酒行為を妨げることなく濃度計測が行われる．本稿では，プロトタイプを用いて市販のアルコール飲料のアルコール濃度を計測した実験結果を報告し，実現されるアプリケーションについて議論する．
Hidenori Matsui, Takahiro Hashizume, and Koji Yatani. Al-light: An Alcohol-Sensing Smart Ice Cube. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. (PACM IMWUT) 2, 3, Article 126 (September 2018), 20 pages. (paper)(video)
Managing time while presenting is challenging, but mobile devices offer both convenience and flexibility in their ability to support the end-to-end process of setting, refining, and following presentation time targets. From an initial HCI-Q study of 20 presenters, we identified the need to set such targets per “zone” of consecutive slides (rather than per slide or for the whole talk), as well as the need for feedback that accommodates two distinct attitudes towards presentation timing. These findings led to the design of TalkZones, a mobile application for timing support. When giving a 20-slide, 6m40s rehearsed but interrupted talk, 12 participants who used TalkZones registered a mean overrun of only 8s, compared with 1m49s for 12 participants who used a regular timer. We observed a similar 2% overrun in our final study of 8 speakers giving rehearsed 30-minute talks in 20 minutes. Overall, we show that TalkZones can encourage presenters to advance slides before it is too late to recover, even under the adverse timing conditions of short and shortened talks.
B. Saket, S. Yang, H. Z. Tan, K. Yatani, and D. Edge, “TalkZones: Section-based Time Support for Presentations,” in Proceedings of the ACM SIGCHI International Conference on Human Computer Interaction with Mobile Devices and Services (MobileHCI 2014), 2014. [Paper][Video]Honorable Mention Award
Review comments posted in online websites can help the user decide a product to purchase or place to visit. They can also be useful to closely compare a couple of candidate entities. However, the user may have to read different webpages back and forth for comparison, and this is not desirable particularly when she is using a mobile device. We present ReviewCollage, a mobile interface that aggregates information about two reviewed entities in a one-page view. ReviewCollage uses attribute-value pairs, known to be effective for review text summarization, and highlights the similarities and differences between the entities. Our user study confirms that ReviewCollage can support the user to compare two entities and make a decision within a couple of minutes, at least as quickly as existing summarization interfaces. It also reveals that ReviewCollage could be most useful when two entities are very similar.
H. Jin, T. Sakai, and K. Yatani, “ReviewCollage: A Mobile Interface for Direct Comparison Using Online Reviews,” in Proceedings of the ACM SIGCHI International Conference on Human Computer Interaction with Mobile Devices and Services (MobileHCI 2014), 2014. [Paper][Video]Honorable Mention Award
Our preliminary study reveals that individuals use various management strategies for limiting smartphone use, ranging from keeping smartphones out of reach to removing apps. However, we also found that users often had difficulties in maintaining their chosen management strategies due to the lack of self-regulation. In this paper, we present NUGU, a group-based intervention app for improving self-regulation on smartphone use through leveraging social support: groups of people limit their use together by sharing their limiting information. NUGU is designed based on social cognitive theory and it has been developed iteratively through two pilot tests. Our three-week user study (n = 62) demonstrated that compared with its non-social counterpart, the NUGU users’ usage amount significantly decreased and their perceived level of managing disturbances improved. Furthermore, our exit interview confirmed that NUGU’s design elements are effective for achieving limiting goals.
M. Ko, S. Yang, J. Lee, C. Heizmann, J. Jeong, U. Lee, D. H. Shin, K. Yatani, J. Song, and K. Chung, “NUGU: A Group-based Intervention App for Improving Self-Regulation of Limiting Smartphone Use,” in Proceedings of the ACM conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2015), 2015. [Paper]