Blood glucose variance measured by continuous glucose monitors across the menstrual cycle

Past studies on how blood glucose levels vary across the menstrual cycle have largely shown inconsistent results based on limited blood draws. In this study, 49 individuals wore a Dexcom G6 continuous glucose monitor and a Fitbit Sense smartwatch while measuring their menstrual hormones and self-reporting characteristics of their menstrual cycles daily. The average duration of participation was 79.3 ± 21.2 days, leading to a total of 149 cycles and 554 phases in our dataset. We use periodic restricted cubic splines to evaluate the relationship between blood glucose and the menstrual cycle, after which we assess phase-based changes in daily median glucose level and associated physiological parameters using mixed-effects models. Results indicate that daily median glucose levels increase and decrease in a biphasic pattern, with maximum levels occurring during the luteal phase and minimum levels occurring during the late-follicular phase. These trends are robust to adjustments for participant characteristics (e.g., age, BMI, weight) and self-reported menstrual experiences (e.g., food cravings, bloating, fatigue). We identify negative associations between each of daily estrogen level, step count, and low degrees of fatigue with higher median glucose levels. Conversely, we find positive associations between higher food cravings and higher median glucose levels. This study suggests that blood glucose could be an important parameter for understanding menstrual health, prompting further investigation into how the menstrual cycle influences glucose fluctuation

A 2-Way Laser-Assisted Selection Scheme for Handhelds in a Physical Environment

Abstract. We present a 2-way selection method to select objects in a physical environment with a novel feedback and transfer of control mechanism. A modulated laser pointer signal sent from a handheld device triggers a photosensitive tag placed in the environment. The tag responds via a standard wireless channel directly to the handheld with information regarding an object it represents. We describe a prototype implementation for a Motorola iDEN i95cl cell phone, discuss the interaction challenges and application possibilities for this physical world selection that extends a common handheld device. We also compare this solution to related attempts in the literature.

The ContextCam: Automated Point of Capture Video Annotation

Abstract: Rich, structured annotations of video recordings enable interesting uses, but existing techniques for manual, and even semi-automated, tagging can be too time-consuming. We present in this paper the ContextCam, a prototype of a consumer video camera that provides point of capture annotation of time, location, person presence and event information associated to recorded video. Both low- and high-level metadata are discovered via a variety of sensing and active tagging techniques, as well as through the application of machine learning techniques that use past annotations to suggest metadata for the current recordings. Furthermore, the ContextCam provides users with a minimally intrusive interface for correcting predicted high-level metadata during video recording.

BLUI: Low-cost Localized Blowable User Interfaces

We describe a unique form of hands-free interaction that can be implemented on most commodity computing platforms. Our approach supports blowing at a laptop or computer screen to directly control certain interactive applications. Localization estimates are produced in real-time to determine where on the screen the person is blowing. Our approach relies solely on a single microphone, such as those already embedded in a standard laptop or one placed near a computer monitor, which makes our approach very cost-effective and easy-to-deploy. We show example interaction techniques that leverage this approach. ACM Classification: H5.2 [Information interfaces an

DANTE VISION: IN-AIR AND TOUCH GESTURE SENSING FOR NATURAL SURFACE INTERACTION WITH COMBINED DEPTH AND THERMAL CAMERAS

ABSTRACT Researchers have paid considerable attention to natural user interfaces, especially sensing gestures and touches upon an un-instrumented surface from an overhead camera. We present a system that combines depth sensing from a Microsoft Kinect and temperature sensing from a thermal imaging camera to infer a variety of gestures and touches for controlling a natural user interface. The system, coined Dante, is capable of (1) inferring multiple touch points from multiple users (92.6% accuracy), (2) detecting and classifying each user using their depth and thermal footprint (87.7% accuracy), and (3) detecting touches on objects placed upon the table top (91.7% accuracy). The system can also classify the pressure of chording motions. The system is real time, with an average processing delay of 40 ms