Laughter has been known to have therapeutic benefits ranging from reducing stress and inflammation in the short term to lowering cholesterol and blood pressure in the longer term. Studies have also shown that even faked laughter could provide some of these benefits. In this paper, we present the design and validation of a game, "Laugh Out Loud", in which laughter acts as the controller of the game mechanics. The goal of the game is to bloom a wilted flower by laughing. The primary components of this system are a laughter detector and a game interface. The laughter detector is a machine learning algorithm that analyses signals recorded by the microphone in real time and measures the intensity and duration of laughter. The game interface displays a wilted flower that starts blooming step by step as the player laughs, with a fully bloomed flower at the final level. Each level has an increasing level of difficulty, which means that the player has to laugh louder and longer for crossing the higher levels. The game interface is implemented as an Android app, with the intention of making the well-being intervention available anytime, anywhere. To validate the game, we conducted a study in which 48 participants were asked to play the game, one at a time, while seated alone in a closed room. 76.6 % participants reported that they experienced reduced stress after playing the game. We present findings of this study and observations that could lead to some design improvements. CCS CONCEPTS• Human-centered computing → Ubiquitous and mobile computing systems and tools.

The word, touch is known by everyone in this modern world. Everybody is habituated with the usefulness of touchpad. This innovative technology will lead the new generation to a new area. All of these applications guide define the style standard for a single application system. To define a standard format for the user interface in an application this is controlled by touchpad (multi sensation) so that users perceive a uniform "look and feel". It may be researched in the design and use of computer technology on the interfaces between users and computers. In this paper, one thing is focused i.e. the architecture which is used for the development of this type of interactive device. Main challenge behind this system, camera should catch IR light and threshold value should be fixed according to my system.

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Research on self-efficacy and confidence has spread across several subfields of psychology and neuroscience. The role of one’s confidence is very crucial in the formation of attitude and communication skills. The importance of differentiating the levels of confidence is quite visible in this domain. With the recent advances in extracting behavioral insight from a signal in multiple applications, detecting confidence is found to have great importance. One such prominent application is detecting confidence in interview conversations. We have collected an audiovisual data set of interview conversations with 34 candidates. Every response (from each of the candidate) of this data set is labeled with three levels of confidence: high, medium, and low. Furthermore, we have also developed algorithms to efficiently compute such behavioral confidence from speech and video. A deep learning architecture is proposed for detecting confidence levels (high, medium, and low) from an audiovisual clip recorded during an interview. The achieved unweighted average recall (UAR) reaches 85.9% on audio data and 73.6% on video data captured from an interview session.

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