Internet of Things (IoT) provides a wide range of services to improve convenience and comfort in our daily lives. However, various sensors equipped on IoT devices often raise privacy concerns. Prior works on privacy focus on passive protection from the data and device perspective, such as data encryption and communication protocol design. In this work, we introduce PrivaSee, an augmented reality (AR)-enabled privacy visualization platform to empower users with proactive privacy protection by enhancing their understanding of privacy perception for multimodal sensors.

Remote sports coaching connects athletes to interactive training sessions, despite busy schedules and/or lack of access to local trainers. In current formats, athletes record videos of themselves, which they send to coaches for feedback or use in self-coaching. Additionally, some coaches turn to video conferencing platforms such as FaceTime or Zoom for live coaching. A significant challenge with these methods of remote sports coaching is the absence of spatial analysis capabilities, which hinders in-depth assessment of athletic performance.This paper introduces Augmented Coach, an immersive and interactive sports coaching system. Augmented Coach utilizes volumetric data to reconstruct the 3D representations of the athletes. As a result, coaches can not only view the resulting point cloud videos of the athletes performing athletic movements, but also employ the system’s spatial annotation and visualization tools to gain insights into movement patterns and communicate with remote athletes. Unlike existing tools tailored to specific sports, Augmented Coach explores spatial kinesthetic values shared across various sports through a pilot study and designs adaptable features applicable to diverse sports coaching scenarios. To assess the system’s usability, we conducted a user study involving ten users, spanning certified coaches and experienced athletes from various sports, illustrating how they can utilize the system’s features to enhance coaching in their respective disciplines.

Remote coaching for sports is challenged by the lack of 3D spatial communication. While athletes send live or recorded videos to their coaches, these 2D representations fail to capture the spatial relationships of the body, limiting the ability to understand timing, weight distribution, and smoothness in an athletic movement. This demonstration presents Augmented Coach, an AR sports coaching platform for coaches to remotely view, manipulate, and annotate athletic movements in 3D augmented space. Also, this demonstration provides an adaptive platform to study real-time efficient volumetric data transmission between remotely connected devices, including over 5G cellular networks.

The stage background is one of the most important features for a dance performance, as it helps to create the scene and atmosphere. In conventional dance performances, the background images are usually selected or designed by professional stage designers according to the theme and the style of the dance. In new media dance performances, the stage effects are usually generated by media editing software. Selecting or producing a dance background is quite challenging and is generally carried out by skilled technicians. The goal of the research reported in this article is to ease this process. Instead of searching for background images from the sea of available resources, dancers are recommended images that they are more likely to use. This work proposes the idea of a novel system to recommend images based on content-based social computing. The core part of the system is a probabilistic prediction model to predict a dancer’s interests in candidate images through social platforms. Different from traditional collaborative filtering or content-based models, the model proposed here effectively combines a dancer’s social behaviors (rating action, click action, etc.) with the visual content of images shared by the dancer using deep matrix factorization (DMF). With the help of such a system, dancers can select from the recommended images and set them as the backgrounds of their dance performances through a media editor. According to the experiment results, the proposed DMF model outperforms the previous methods, and when the dataset is very sparse, the proposed DMF model shows more significant results.