Healthcare facility is scarce for rural women in the developing world. The situation is worse for patients who are suffering from diseases that require long-term feedback-oriented monitoring such as breast cancer. Lack of motivation to go to the health centers on patients' side due to sociocultural barriers, financial restrictions and transportation hazards results in inadequate data for proper assessment. Fortunately, mobile phones have penetrated the masses even in rural communities of the developing countries. In this scenario, a mobile phone-based remote symptom monitoring system (RSMS) with inspirational videos can serve the purpose of both patients and doctors. Here, we present the findings of our field study conducted on 39 breast cancer patients in rural Bangladesh. Based on the results of extensive field studies, we have categorized the challenges faced by patients in different phases of the treatment process. As a solution, we have designed, developed and deployed e-ESAS-the first mobile-based RSMS in rural context. Along with the detail need assessment of such a system, we describe the evolution of e-ESAS and the deployment results. We have included the unique and useful design lessons that we learned as e-ESAS evolved through participatory design process. The findings show how e-ESAS addresses NOT THE PUBLISHED VERSION; this is the author's final, peer-reviewed manuscript. The published version may be accessed by following the link in the citation at the bottom of the page.

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Social networking sites like Facebook, twitter, and myspace are becoming overwhel mingly powerful media in today's world. Facebook has 500 million active users and twitter has 190 million visitors per month and increasing each second. On the other hand number of smart phone users has crossed 45 millions. Now we are focusing on building an application that will connect these two revolutionary spheres of modern science that has huge potential in different sectors. IRENE a facial expression based mood detection model has been developed by capturing images while the users use webcam supported laptops or mobile phones. This image will be analyzed to classify one of several moods. This mood information will be shared in the user profile of Facebook according to privacy settings of the user. Several activities and events will also be generated based on the identified mood.