The increased availability of immersive headsets in the games industry has promoted wide adoption of immersive technology amongst consumers. The benefits of spatial freedom and agency for body movements in virtual reality pave the way for everyday fitness and physical training applications. We conducted a systematic review through ACM Digital Library, IEEEXplore, and Scopus, to investigate how immersive technologies have been applied for physical training in fitness and sports. Our review included all peer-reviewed papers, published and written in English, discussing all forms of hardware for VR, AR, and MR technologies, targeted towards healthy population. We excluded applications for clinical populations and treatment of specific diseases, and all non-peer reviewed articles like position papers, workshops, tutorials, and magazine columns. From the shortlisted 144 papers, we summarize the development trends of immersive technologies and highlight the challenges of designing immersive technologies for everyday fitness. We also provide recommendations for future work and highlight the need to support better collaboration with industry partners, trainer-trainee experiences, and social dynamics of sports for designing better experiences.

Mobile edge computing (MEC) provides a new computing paradigm that can overcome the inability of the traditional cloud computing paradigm to ensure low service latency by pushing computing power and resources to the network edge. Many studies have attempted to formulate edge data caching strategies for app vendors to optimize caching performance by caching the right data on the right edge servers. However, existing edge data caching approaches have unfortunately ignored fairness, which is an important issue from the app vendor's perspective. In general, an app vendor needs to cache data on edge servers to serve its users with insignificant latency differences at a minimum caching cost. In this paper, we make the first attempt to tackle the fair edge data caching (FEDC) problem. Specifically, we formulate the FEDC problem as a constraint optimization problem (COP) and prove its$\mathcal {NP}$-hardness. An optimal approach named FEDC-OPT is proposed to find optimal solutions to small-scale FEDC problems with integer programming technique. In addition, an approximate algorithm named FEDC-APX is proposed to find approximate solutions in large-scale FEDC problems. The performance of the proposed approaches is analyzed theoretically, and evaluated experimentally on a widely-used real-world data set against four representative approaches. The experimental results show that the proposed approaches can solve the FEDC problem efficiently and effectively.

Virtual Reality (VR) challenges us with the requirements of ultra-low latency and ultra-high bandwidth. Existing methods that rely on cloud computing systems to improve the latency and bandwidth problems cannot satisfy the high computation and fast communication requirements in VR. Edge computing has emerged as a promising solution that can be applied in VR to optimize the latency and bandwidth prob-lems. However, another challenge is applying edge computing technology to improve the seamless for the VR users. Based on this, this paper proposes an edge-end collaboration testbed called Insta VarjoLive and conducts the experiments on the real-time 360° video live streaming seamless using VR headsets. We compared our experiments with the 360° videos watched by users from the cloud through VR headset and obtained the results showing that the edge-assisted 360° videos live streaming method has three major advantages: better real-time delivery, lower response time, and higher bandwidth guaranteed. Furthermore, we tested our experiments and discussed the other possible optimization methods in the future.

Summary Human emotions can be recognized from facial expressions captured in videos. It is a growing research area in which many have attempted to improve video emotion detection in both lab‐controlled and unconstrained environments. While existing methods show a decent recognition accuracy on lab‐controlled datasets, they deliver much lower accuracy in a real‐world uncontrolled environment, where a variety of challenges need to be addressed such as variations in illumination, head pose, and individual appearance. Moreover, automatically identifying the key frames consisting of the expression from real‐world videos is another challenge. In this article, to overcome these challenges, we provide a video emotion recognition via multiple feature fusion method. First, a uniform local binary pattern (LBP) and the scale‐invariant feature transform features are extracted from each frame in the video sequences. By applying a random forest classifier, all of the static frames are then labelled by the related emotion class. In this way, the key frames can be automatically identified, including neutral and other expressions. Furthermore, from the key frames, a new geometric feature vector and the LBP from three orthogonal planes are extracted. To further improve robustness, audio features are extracted from the video sequences as an additional dimension to augmenting visual facial expression analysis. The audio and visual features are fused through a kernel multimodal sparse representation. Finally, the corresponding emotion labels to the video sequences can be assigned when a multimodal quality measure specifies the quality of each modality and its role in the decision. The results on both acted facial expressions in the Wild and MMI datasets demonstrate that the proposed method outperforms several counterpart video emotion recognition methods.

The rapid development of computer and internet technology is calling for new interaction and cooperation methods among people that are physically distributed. Having successfully involved many people, online shopping is an area that worth looking into. New platform and communication methods, such as virtual technology and voice-based communication, are being considered and proposed. This paper reviews the history, characteristics and culture of virtual worlds, based on which it also designs and tests stores with different media channels in a popular online virtual world, SecondLife. Results of the experiment and discussions are presented as well.

The paper develops and discusses a conceptual model for a mixed reality-based remote collaborative system (MR-Collab) based on motivated learning agents. The proposed MR-Collab system is described in details as well to help better understand this model. Self-development agent based on previous work is an intelligent agent, which not only receives information from sensors in the environment, but also gives valuable suggestions that could help to improve the system. The model is not limited to this specific system, but could be adapted to other collaboration systems as well.