Carbohydrate Estimation by a Mobile Phone-Based System Versus Self-Estimations of Individuals With Type 1 Diabetes Mellitus: A Comparative Study

Background: Diabetes mellitus is spreading throughout the world and diabetic individuals have been shown to often assess their food intake inaccurately; therefore, it is a matter of urgency to develop automated diet assessment tools. The recent availability of mobile phones with enhanced capabilities, together with the advances in computer vision, have permitted the development of image analysis apps for the automated assessment of meals. GoCARB is a mobile phone-based system designed to support individuals with type 1 diabetes during daily carbohydrate estimation. In a typical scenario, the user places a reference card next to the dish and acquires two images using a mobile phone. A series of computer vision modules detect the plate and automatically segment and recognize the different food items, while their 3D shape is reconstructed. Finally, the carbohydrate content is calculated by combining the volume of each food item with the nutritional information provided by the USDA Nutrient Database for Standard Reference. Objective: The main objective of this study is to assess the accuracy of the GoCARB prototype when used by individuals with type 1 diabetes and to compare it to their own performance in carbohydrate counting. In addition, the user experience and usability of the system is evaluated by questionnaires. Methods: The study was conducted at the Bern University Hospital, “Inselspital” (Bern, Switzerland) and involved 19 adult volunteers with type 1 diabetes, each participating once. Each study day, a total of six meals of broad diversity were taken from the hospital’s restaurant and presented to the participants. The food items were weighed on a standard balance and the true amount of carbohydrate was calculated from the USDA nutrient database. Participants were asked to count the carbohydrate content of each meal independently and then by using GoCARB. At the end of each session, a questionnaire was completed to assess the user’s experience with GoCARB. Results: The mean absolute error was 27.89 (SD 38.20) grams of carbohydrate for the estimation of participants, whereas the corresponding value for the GoCARB system was 12.28 (SD 9.56) grams of carbohydrate, which was a significantly better performance ( P=.001). In 75.4% (86/114) of the meals, the GoCARB automatic segmentation was successful and 85.1% (291/342) of individual food items were successfully recognized. Most participants found GoCARB easy to use. Conclusions: This study indicates that the system is able to estimate, on average, the carbohydrate content of meals with higher accuracy than individuals with type 1 diabetes can. The participants thought the app was useful and easy to use. GoCARB seems to be a well-accepted supportive mHealth tool for the assessment of served-on-a-plate meals.

Visualizing the Simulation of 3-D Underwater Sensor Networks

The majority of sensor network research deals with land-based networks, which are essentially two-dimensional, and thus the majority of simulation and animation tools also only handle such networks. Underwater sensor networks on the other hand, are essentially 3D networks because the depth at which a sensor node is located needs to be considered as well. Due to that additional dimension, specialized tools need to be used when conducting simulations for experimentation. The School of Engineering’s Underwater Sensor Network (UWSN) lab is conducting research on underwater sensor networks and requires simulation tools for 3D networks. The lab has extended NS-2, a widely used network simulator, so that it can simulate three-dimensional networks. However, NAM, a widely used network animator, currently only supports two-dimensional networks and no extensions have been implemented to give it three-dimensional capabilities. In this project, we develop a network visualization tool that functions similarly to NAM but is able to render network environments in full 3-D. It is able to take as input a NS-2 trace file (the same file taken as input by NAM), create the environment, position the sensor nodes, and animate the events of the simulation. Further, the visualization tool is easy to use, especially friendly to NAM users, as it is designed to follow the interfaces and functions similar to NAM. So far, the development has fulfilled the basic functionality. Future work includes fully functional capabilities for visualization and much improved user interfaces.

Physicochemical properties and 3D geostatistical simulations of the Herten and the Descalvado aquifer analogs

This data set presents a comprehensive characterisation of the sedimentary structures from important groundwater hosting formations in Germany (Herten aquifer analog) and Brazil (Descalvado aquifer analog). Multiple 2-D outcrop faces are described in terms of hydraulic, thermal and chemical properties and interpolated in 3D using stochastic techniques. For each aquifer analog, multiple 3D realisations of the facies heterogeneity are provided using different stochastic simulations settings. These are unique analogue data sets that can be used by the wider community to implement approaches for characterising aquifer formations.

Lagrangian back-tracing results for Kohnen station; output of time dependent numerical experiment with a 3D nested Antarctic ice sheet model

A nested ice flow model was developed for eastern Dronning Maud Land to assist with the dating and interpretation of the EDML deep ice core. The model consists of a high-resolution higher-order ice dynamic flow model that was nested into a comprehensive 3-D thermomechanical model of the whole Antarctic ice sheet. As the drill site is on a flank position the calculations specifically take into account the effects of horizontal advection as deeper ice in the core originated from higher inland. First the regional velocity field and ice sheet geometry is obtained from a forward experiment over the last 8 glacial cycles. The result is subsequently employed in a Lagrangian backtracing algorithm to provide particle paths back to their time and place of deposition. The procedure directly yields the depth-age distribution, surface conditions at particle origin, and a suite of relevant parameters such as initial annual layer thickness. This paper discusses the method and the main results of the experiment, including the ice core chronology, the non-climatic corrections needed to extract the climatic part of the signal, and the thinning function. The focus is on the upper 89% of the ice core (appr. 170 kyears) as the dating below that is increasingly less robust owing to the unknown value of the geothermal heat flux. It is found that the temperature biases resulting from variations of surface elevation are up to half of the magnitude of the climatic changes themselves.

A multimodal tablet-based interface for designing and reviewing 3D engineering models

The ability to view and interact with 3D models has been happening for a long time. However, vision-based 3D modeling has only seen limited success in applications, as it faces many technical challenges. Hand-held mobile devices have changed the way we interact with virtual reality environments. Their high mobility and technical features, such as inertial sensors, cameras and fast processors, are especially attractive for advancing the state of the art in virtual reality systems. Also, their ubiquity and fast Internet connection open a path to distributed and collaborative development. However, such path has not been fully explored in many domains. VR systems for real world engineering contexts are still difficult to use, especially when geographically dispersed engineering teams need to collaboratively visualize and review 3D CAD models. Another challenge is the ability to rendering these environments at the required interactive rates and with high fidelity. In this document it is presented a virtual reality system mobile for visualization, navigation and reviewing large scale 3D CAD models, held under the CEDAR (Collaborative Engineering Design and Review) project. It’s focused on interaction using different navigation modes. The system uses the mobile device's inertial sensors and camera to allow users to navigate through large scale models. IT professionals, architects, civil engineers and oil industry experts were involved in a qualitative assessment of the CEDAR system, in the form of direct user interaction with the prototypes and audio-recorded interviews about the prototypes. The lessons learned are valuable and are presented on this document. Subsequently it was prepared a quantitative study on the different navigation modes to analyze the best mode to use it in a given situation.

A Mobile Feedback System for Integrated E-health Platforms to improve Self-Care and Compliance of Diabetes Mellitus Patients

Exploiting the full potential of telemedical systems means using platform based solutions: data are recovered from biomedical sensors, hospital information systems, care-givers, as well as patients themselves, and are processed and redistributed in an either centralized or, more probably, decentralized way. The integration of all these different devices, and interfaces, as well as the automated analysis and representation of all the pieces of information are current key challenges in telemedicine. Mobile phone technology has just begun to offer great opportunities of using this diverse information for guiding, warning, and educating patients, thus increasing their autonomy and adherence to their prescriptions. However, most of these existing mobile solutions are not based on platform systems and therefore represent limited, isolated applications. This article depicts how telemedical systems, based on integrated health data platforms, can maximize prescription adherence in chronic patients through mobile feedback. The application described here has been developed in an EU-funded R&D project called METABO, dedicated to patients with type 1 or type 2 Diabetes Mellitus