Hand gesture recognition is the process of detecting the hand movements via sensor measurements for detecting an activity, such as writing a letter or a number. Recognising the handwritten characters using wearable devices enables machine-human interaction to occur without the need for a communication method. An intelligent automated framework is required to accurately detect the handwritten characters using wrist worn sensor signals, in particular, with minimal number of sensors. Moreover, the system developed needs to have the capacity to recognise the characters written in different sizes. In order to address these, we analyse performance of several machine learning models using single/multiple sensors namely, accelerometer or/and gyroscope, for recognising hand gesture characters including alphabet and numbers of varying sizes. We formulate a set of features that enable robust and accurate detection of the characters.We performed novel data collection using an off-the-shelf wrist-worn sensor based device, and evaluated our framework to detect the different characters effectively. The maximum accuracy (90.40%) was achieved using both sensors and Random Forest (RF) model. This was dropped to 82.51% for the same model using accelerometer sensor alone. Using the gyroscope sensor, an overall average accuracy of 80.16% was achieved with the Forward Neural Network (FNN) model. Although the model based on both sensors showed the best performance, our evaluation reveals that it is feasible to develop a machine learning model using single sensor to detect hand gesture characters of varying sizes with reasonable (≥ 80%) accuracy.

Policy making is influenced by a number of factors, including electoral politics, ideological biases of actors involved in the policy making process, and the interlocks between corporate and government entities. This influence is also exercised by shaping public opinion through mass media. In this paper, we study four ICTD policies in India, and explore the political economy around them by using data about how these policies are covered in the mass media. We study which actors are covered more in media, how they speak on the policy issues, and which aspects are given more coverage for these policies. We find that politicians get the highest coverage in mass media regarding discussions on policies, and that the politicians and business-persons often express similar ideologies related to these policies. We also observe that mass media is often biased towards issues related to its middle class reader base with a strong sense of technology driven high-modernism, and negative aspects of these policies and issues faced by the poor due to improper policy implementation are often not given significant coverage. Our key contribution is a methodology of using automated analysis of mass media data to reveal the factors that might be shaping the political economy behind policy making.