A Food Recognition System for Diabetic Patients based on an Optimized Bag of Features Model

Computer vision-based food recognition could be used to estimate a meal's carbohydrate content for diabetic patients. This study proposes a methodology for automatic food recognition, based on the Bag of Features (BoF) model. An extensive technical investigation was conducted for the identification and optimization of the best performing components involved in the BoF architecture, as well as the estimation of the corresponding parameters. For the design and evaluation of the prototype system, a visual dataset with nearly 5,000 food images was created and organized into 11 classes. The optimized system computes dense local features, using the scale-invariant feature transform on the HSV color space, builds a visual dictionary of 10,000 visual words by using the hierarchical k-means clustering and finally classifies the food images with a linear support vector machine classifier. The system achieved classification accuracy of the order of 78%, thus proving the feasibility of the proposed approach in a very challenging image dataset.

Towards an SMS-based Social Network for Health Workers in Rural Areas in Myanmar

Seventy percent of the population in Myanmar lives in rural areas. Although health workers are adequately trained, they are overburdened due to understaffing and insufficient supplies. Literature confirms that information and communication technologies can extend the reach of healthcare. In this paper, we present an SMS-based social network that aims to help health workers to interact with other medical professionals through topic-based message delivery. Topics describe interests of users and the content of message. A message is delivered by matching message content with user interests. Users describe topics as ICD- 10 codes, a comprehensive medical taxonomy. In this ICD-10 coded SMS, a set of prearranged codes provides a common language for users to send structured information that fits inside an SMS.