Pun Monster: How to effectively maintain fitness and improve socialization

With the global outbreak of the COVID-19 in 2020, national governments have introduced policies of home quarantine and closing public places, and following social distances. As people spent more time at home, lifestyles changed dramatically, people spent more time looking at their phones and computers, and eating habits changed. This has brought about weight gain and increased anxiety and stress. People have started to do fitness activities at home, but have encountered various problems such as lack of motivation to exercise and lack of peers to supervise each other. The purpose of this paper is to investigate the key factors affecting people\u27s inability to be active and to design a game system to create a positive exercise environment

Exploring The Benefits Of Context In 3D Gesture Recognition For Game-Based Virtual Environments

We present a systematic exploration of how to utilize video game context (e.g., player and environmental state) to modify and augment existing 3D gesture recognizers to improve accuracy for large gesture sets. Specifically, our work develops and evaluates three strategies for incorporating context into 3D gesture recognizers. These strategies include modifying the well-known Rubine linear classifier to handle unsegmented input streams and per-frame retraining using contextual information (CA-Linear); a GPU implementation of dynamic time warping (DTW) that reduces the overhead of traditional DTW by utilizing context to evaluate only relevant time sequences inside of a multithreaded kernel (CA-DTW); and a multiclass SVM with per-class probability estimation that is combined with a contextually based prior probability distribution (CA-SVM). We evaluate each strategy using a Kinect-based third-person perspective VE game prototype that combines parkour-style navigation with hand-to-hand combat. Using a simple gesture collection application to collect a set of 57 gestures and the game prototype that implements 37 of these gestures, we conduct three experiments. In the first experiment, we evaluate the effectiveness of several established classifiers on our gesture set and demonstrate state-of-the-art results using our proposed method. In our second experiment, we generate 500 random scenarios having between 5 and 19 of the 57 gestures in context. We show that the contextually aware classifiers CA-Linear, CA-DTW, and CA-SVM significantly outperform their noncontextually aware counterparts by 37.74%, 36.04%, and 20.81%, respectively. On the basis of the results of the second experiment, we derive upper-bound expectations for in-game performance for the three CA classifiers: 96.61%, 86.79%, and 96.86%, respectively. Finally, our third experiment is an in-game evaluation of the three CA classifiers with and without context. Our results show that through the use of context, we are able to achieve an average in-game recognition accuracy of 89.67% with CA-Linear compared to 65.10% without context, 79.04% for CA-DTW compared to 58.1% without context, and 90.85% with CA-SVM compared to 75.2% without context

Authenticating Users with 3D Passwords Captured by Motion Sensors

Authentication plays a key role in securing various resources including corporate facilities or electronic assets. As the most used authentication scheme, knowledgebased authentication is easy to use but its security is bounded by how much a user can remember. Biometrics-based authentication requires no memorization but ‘resetting’ a biometric password may not always be possible. Thus, we propose study several behavioral biometrics (i.e., mid-air gestures) for authentication which does not have the same privacy or availability concerns as of physiological biometrics. In this dissertation, we first propose a user-friendly authentication system Kin- Write that allows users to choose arbitrary, short and easy-to-memorize passwords while providing resilience to password cracking and password theft. Specifically, we let users write their passwords (i.e., signatures in the 3D space), and verify a user’s identity with similarities between the user’s password and enrolled password templates. Dynamic time warping distance is used for similarity calculation between 3D passwords samples. In the second part of the dissertation, we design an authentication scheme that does not depend on the handwriting contents, i.e., regardless of the written words or symbols, and adapt challenge-response mechanism to avoid possible eavesdropping, man-in-the-middle attacks, and reply attacks. We design a MoCRA system that utilizes Leap Motion to capture users’ writing movements and use writing style to verify users, even if what they write during the verification is completely different from what they write during the enrollment. Specifically, MoCRA leverages co-occurrence matrices to model the handwriting styles, and use a Support Vector Machine (SVM) to accept a legitimate user and reject the rest. In the third part, we study both security and usability performance on multiple types of mid-air gestures that used as passwords, including writing signatures in the air. We objectively quantify the usability performance by metrics related to the enroll time and the complexity of the gestures, and evaluate the security performance by the authentication performance. In addition, we subjectively evaluate the gestures by survey responses from both field subjects who participated in gesture experiments and on-line subjects who watched a short video on gesture introducing. Finally, we study the consistency of gestures over samples collected in a two-month period, and evaluate their security under shoulder surfing attacks