Today, there are a variety of devices or platforms that are capable of providing virtual reality (VR) experiences. Some examples are: Google VR (cardboard and Daydream), MS Hololens, Oculus Rift, HTC Vive, etc. Each device presents an opportunity for a unique way of interaction between the user and the system. This largely depends on the hardware capabilities and limitations of the device itself and, of course, the imagination of the designers or developers developing applications for these devices.

This study aims to explore the different kinds of interactions that are currently being employed by designers or developers across a variety of applications across different platforms.

Smartphone text input methods have been augmented by features like adaptive keypads and error correction, which could potentially fix errors that a user makes while typing, thereby improving the overall efficiency of text input. Such methods need to be evaluated for performance before they can be deemed fit for deployment.

Swarachakra Malayalam is an open-source keyboard for Android. It generates a steadily growing database of Malayalam words. This word database could be a resource that can be used to fuel the development of new tools for the language. But the database (corpus) contains incorrect or unusable words (for certain contexts). Tagging these words becomes an important task to make this corpus usable by ‘cleaning’ the corpus.

Due to the complexity of the language's grammar paired with its agglutinative property, it is challenging to programmatically categorise the words. But while this may be challenging for a computer, it is easier and even enjoyable for a person who knows the language. But due to the large number of words in the corpus, it becomes a huge task. The aim of this project is to crowdsource, through gamification, the cleaning of the corpus.

During the course of the project, the corpus cleaning activity was broken down into multiple steps and turned into minitasks. Then multiple possible ways of gamifying these tasks were looked into. After weighing the pros and cons of each, one of the ideas was designed, detailed, and developed into functioning prototypes. The prototype version 1 had minimal gamification elements (only level scores and player levels). The prototype version 2 has more gamification elements like scores, player levels, achievements and badges, leaderboards, etc. The proto V1 acts as a benchmark against which player engagement levels of proto V2.

While the proto-V2 doesn’t have all the gamification elements that were explored, it lays a foundation upon which the others can also be added. The effectiveness of the game in cleaning the corpus and the effect of these gamification elements on player engagement were evaluated using a functioning prototype. Out of the gamification elements that were tried out in the prototype, the tutorial levels, game stats, achievements, and leaderboards seemed to have a direct positive impact on the players’ engagement levels. Identifying the impact of the other gamification elements will require a longer evaluation with a larger user base. In the bigger picture, the outcome of this project would be one of the several layers of filters that can be used to clean up the existing database and create a comprehensive database of words.

Smartphones have become an integral part of the lives of a major section of the population. Smartphones help their users get a lot of work done on the go and stay connected to people and information. But this sometimes comes at the cost of some nagging issues. One such issue is that of numerous and often useless notifications. These could range from promotional messages to group chats to app notifications. Research suggests that such interruptions have the potential to adversely affect the productivity of the users as well as raise their anxiety levels. While there are methods by which the user can block or silence such notifications, it requires the user to actively tune the settings to meet their requirements. But there are users who lack the knowledge or patience to do so. This is also due to the fact that they don’t realise how these notifications are disrupting their work or lives.

In this project, an attempt is made to develop a framework that can reduce the number of low-priority smartphone-generated interruptions. This system would be personalised to each user since it would learn from the individual’s behaviour. This framework includes the interactions through which the user can communicate their opinion about a notification, an underlying algorithm that would learn from the user input and identify low-priority notifications, and finally, methods through which the user can interact with the low-priority notifications. This framework is being proposed as an expansion to the Android OS. The primary research was done in the form of semi-structured interviews and a data collection tool to collect notification data from users.

Separate prototypes were developed to demonstrate the working of the UI elements and the working of the algorithm. The prototype was used to collect data from eight users and was evaluated for accuracy in the identification of low-priority notifications in both long-term and short-term use cases.