Weight control device using bites detection

The present invention relates to a device that can be used in individual weight control protocols that is capable of detecting in real time information with regard to number of bites taken, time between bites, and so forth. The weight control device can detect bites through motion detection via a sensor worn on the wrist or hand of a user. The device can include notification capabilities that can alert a user as to excessive eating speed, excessive amounts of food intake, and the like so as to provide immediate feedback for purposes of weight control

Device and method for detecting eating activities

Devices and methods for detecting an eating activity occurrence are provided. A device includes a sensor for monitoring movement of a portion of an arm of a subject, and a processor in communication with the sensor for collecting raw data associated with movement of the portion of the arm. The processor is configured to process the raw data and form processed data. The processed data includes a determination of whether an eating activity has occurred. A method includes sensing movement of a portion of an arm of a subject, and processing raw data associated with the movement of the portion of the arm of the subject to form processed data. The processed data includes a determination of whether an eating activity has occurred

The Effect of a Target Bite Count and Plate Size on Food Intake.

The purpose of this study was to determine if an instruction to take fewer bites than typically taken, would reduce intake and overcome the known environmental cue of plate size. In a previous study, fifty-five participants (34F) ate ad libitum macaroni and cheese in groups of four, either from a small plate or a large plate. They ate 111±35g with 12±4 bites from the small plate, and 195±111g with 20± 6 bites from the large plate. The current study employed the same paradigm. Sixty participants (33F) were given bite count feedback and were instructed to take only 12 bites, while eating from either a small plate or a large plate. Participants ate 135±52g with 12±3 bites from the small plate and 177±63g with 12±2 bites from the large plate. Results of a 2x2 ANOVA indicate a main effect of plate size (p\u3c.001) and instruction (p\u3c.001) on bites taken and an interaction (p\u3c.001). Plate size also affected grams consumed (p\u3c.001). Notably, instruction also affected bite size (p\u3c.001). These results suggest that people will reduce the number of bites when instructed to, but will increase their bite size to compensate for the reduced bite allowance

Correlations of Structure and Dynamics in an Aging Colloidal Glass

We study concentrated colloidal suspensions, a model system which has a glass transition. Samples in the glassy state show aging, in that the motion of the colloidal particles slows as the sample ages from an initial state. We study the relationship between the static structure and the slowing dynamics, using confocal microscopy to follow the three-dimensional motion of the particles. The structure is quantified by considering tetrahedra formed by quadruplets of neighboring particles. We find that while the sample clearly slows down during aging, the static properties as measured by tetrahedral quantities do not vary. However, a weak correlation between tetrahedron shape and mobility is observed, suggesting that the structure facilitates the motion responsible for the sample aging.Comment: Submitted to Solid State Communication

Machine Learning at Microsoft with ML .NET

Machine Learning is transitioning from an art and science into a technology available to every developer. In the near future, every application on every platform will incorporate trained models to encode data-based decisions that would be impossible for developers to author. This presents a significant engineering challenge, since currently data science and modeling are largely decoupled from standard software development processes. This separation makes incorporating machine learning capabilities inside applications unnecessarily costly and difficult, and furthermore discourage developers from embracing ML in first place. In this paper we present ML .NET, a framework developed at Microsoft over the last decade in response to the challenge of making it easy to ship machine learning models in large software applications. We present its architecture, and illuminate the application demands that shaped it. Specifically, we introduce DataView, the core data abstraction of ML .NET which allows it to capture full predictive pipelines efficiently and consistently across training and inference lifecycles. We close the paper with a surprisingly favorable performance study of ML .NET compared to more recent entrants, and a discussion of some lessons learned