Entertainment Applications Research Articles

PREVALENCE OF MUSCULOSKELETAL DISORDER IN SMARTPHONE USERS: CROSS SECTIONAL STUDY

The smartphone supersedes the capability of the cellular phone, as it offers the user internet access, in addition to various applications for social, finical, entertainment, and healthcare needs. So, the aim of the study prevalence of the musculoskeletal disorder in people using smartphones ranging from the age group of 18 to 30 years. Objective: To find out musculoskeletal disorders occurring due to smartphone use. A total of 500 participates were screened according to the inclusion criteria all the participants were assessed for pain using the visual analogue scale in who use a smartphone. In the age group 18-30 years, 3 groups were made. The first group was between 18-21 years, the second group was 22-25 years, and the third group 26-30 years respectively. 18-21 years age group had 169 affected respondents. Area A represents the index, middle and lateral half of the ring finger. Area B represents the medial half of the ringer finger and little finger, Area C represents Thumb, Area D represents the central palmar aspect, Area E represents the Thenar eminence and Area F represents the Hypothenar region of the hand. The present study shows that the overall prevalence of the musculoskeletal disorder in smartphone users is 68%. Males are more affected than females. In this study neck and thumb were more affected regions compared to other parts of the body, secondary to the neck and thumb, the wrist is the affected region. Long-time use of smartphones leads to the repeated strain injury of the wrist and thumb, long time flexion of the neck during smartphone use leads to neck pain.

Robo Ludens

The use of games as vehicles to study human-robot interaction (HRI) has been established as a suitable solution to create more realistic and naturalistic opportunities to investigate human behavior. In particular, multiplayer games that involve at least two human players and one or more robots have raised the attention of the research community. This article proposes a scoping review to qualitatively examine the literature on the use of multiplayer games in HRI scenarios employing embodied robots aiming to find experimental patterns and common game design elements. We find that researchers have been using multiplayer games in a wide variety of applications in HRI, including training, entertainment and education, allowing robots to take different roles. Moreover, robots have included different capabilities and sensing technologies, and elements such as external screens or motion controllers were used to foster gameplay. Based on our findings, we propose a design taxonomy called Robo Ludens, which identifies HRI elements and game design fundamentals and classifies important components used in multiplayer HRI scenarios. The Robo Ludens taxonomy covers considerations from a robot-oriented perspective as well as game design aspects to provide a comprehensive list of elements that can foster gameplay and bring enjoyable experiences in HRI scenarios.

Cali-sketch: Stroke calibration and completion for high-quality face image generation from human-like sketches

Image generation has received increasing attention because of its wide application in security and entertainment. Sketch-based face generation brings more fun and better quality of image generation due to supervised interaction. However, when a sketch poorly aligned with the true face is given as input, existing supervised image-to-image translation methods often cannot generate acceptable photo-realistic face images. To address this problem, in this paper we propose Cali-Sketch, a human-like-sketch to photo-realistic-image generation method. Cali-Sketch explicitly models stroke calibration and image generation using two constituent networks: a Stroke Calibration Network (SCN), which calibrates strokes of facial features and enriches facial details while preserving the original intent features; and an Image Synthesis Network (ISN), which translates the calibrated and enriched sketches to photo-realistic face images. In this way, we manage to decouple a difficult cross-domain translation problem into two easier steps. Extensive experiments verify that the face photos generated by Cali-Sketch are both photo-realistic and faithful to the input sketches, compared with state-of-the-art methods.

Creating pseudo-tactile feedback in virtual reality using shared crossmodal properties of audio and tactile feedback

Virtual reality has the potential to enhance a variety of real-world training and entertainment applications by creating the illusion that a user of virtual reality is physically present inside the digitally created environment. However, the use of tactile feedback to convey information about this environment is often lacking in VR applications. New methods for inducing a degree of tactile feedback in users are described, which induced the illusion of a tactile experience, referred to as pseudo-tactile feedback. These methods utilised shared properties between audio and tactile feedback that can be crossmodally mapped between the two modalities in the design of a virtual reality prototype for a qualitative usability study in order to test the effectiveness and underlying causes of such feedback in the total absence of any real-world tactile feedback. Results show that participants required believable audio stimuli that they could conceive as real-world textures as well a sense of hand-ownership to suspend disbelief and construct an internally consistent mental model of the virtual environment. This allowed them to conceive believable tactile sensations that result from interaction with virtual objects inside this environment.

Modeling Topics in User Dialog for Interactive Tablet Media

In this paper, we present a set of crowdsourcing and data processing techniques for annotating, segmenting and analyzing spoken dialog data to track topics of discussion between multiple users. Specifically, our system records the dialog between the parent and child as they interact with a reading game on a tablet, crowdsources the audio data to obtain transcribed text, and models topics of discussion from speech transcription using ConceptNet, a freely available commonsense knowledge base. We present preliminary results evaluating our technique using dialog collected using an interactive reading game for children 3-5 years of age. We successfully demonstrate the ability to form discussion topics by grouping words with similar meaning. The presented approach is entirely domain independent and in future work can be applied to a broad range of interactive entertainment applications, such as mobile devices, tablets and games.

Simulating Player Behavior for Data-Driven Interactive Narrative Personalization

Data-driven approaches to interactive narrative personalization show significant promise for applications in entertainment, training, and education. A common feature of data-driven interactive narrative planning methods is that an enormous amount of training data is required, which is rarely available and expensive to collect from observations of human players. An alternative approach to obtaining data is to generate synthetic data from simulated players. In this paper, we present a long short-term memory (LSTM) neural network framework for simulating players to train data-driven interactive narrative planners. By leveraging a small amount of previously collected human player interaction data, we devise a generative player simulation model. A multi-task neural network architecture is proposed to estimate player actions and experiential outcomes from a single model. Empirical results demonstrate that the bipartite LSTM network produces the better-performing player action prediction models than several baseline techniques, and the multi-task LSTM derives comparable player outcome prediction models within a shorter training time. We also find that synthetic data from the player simulation model contributes to training more effective interactive narrative planners than raw human player data alone.

3D Reconstruction Human Body From Anthropometric Measurements Using Diversity Control Oriented Genetic Algorithm

3D digitalization of the human body has been studied extensively for various applications in anthropology, ergonomics, healthcare, entertainment and fashion industries. There are different methods and approaches to reconstruct the 3D body model namely using RGB cameras, depth cameras, scanning systems or anthropometric measurements of the human body. Generally, most of existing approaches have to tackle issues relating to security of personal data, the impact of the surrounding environment, cost of 3D scanning systems and complication of anthropometric measurements. This study proposes a method using simple body measurements and given body shapes to digitalize the human body. The effectiveness of proposed method is evaluated and demonstrated based on two datasets: a synthetic dataset generated from a parametric model and a real dataset on Vietnamese collected by Viettel Military Industry and Telecoms Group (Vietnam).

Image-to-Video Generation via 3D Facial Dynamics

We present a versatile model, FaceAnime, for various video generation tasks from still images. Video generation from a single face image is an interesting problem and usually tackled by utilizing Generative Adversarial Networks (GANs) to integrate information from the input face image and a sequence of sparse facial landmarks. However, the generated face images usually suffer from quality loss, image distortion, identity change, and expression mismatching due to the weak representation capacity of the facial landmarks. In this paper, we propose to “imagine” a face video from a single face image according to the reconstructed 3D face dynamics, aiming to generate a realistic and identity-preserving face video, with precisely predicted pose and facial expression. The 3D dynamics reveal changes of the facial expression and motion, and can serve as a strong prior knowledge for guiding highly realistic face video generation. In particular, we explore face video prediction and exploit a well-designed 3D dynamic prediction network to predict a 3D dynamic sequence for a single face image. The 3D dynamics are then further rendered by the sparse texture mapping algorithm to recover structural details and sparse textures for generating face frames. Our model is versatile for various AR/VR and entertainment applications, such as face video retargeting and face video prediction. Superior experimental results have well demonstrated its effectiveness in generating high-fidelity, identity-preserving, and visually pleasant face video clips from a single source face image.

In or out? A field observational study on the placement of entertaining robots in retailing

PurposeThe purpose of this paper is to empirically investigate the role of the placement (i.e. location) of humanoid service robots (HSRs) for entertainment applications in retailing by inspecting a multitude of performance metrics along the point-of-sale conversion funnel.Design/methodology/approachThe study was conducted using unobtrusive observations at a Belgian chocolate store. In total, 42 h of video observation material was collected and analyzed, with an even spread over three conditions: (1) an HSR placed outside, (2) an HSR inside the store and (3) a control condition (no robot stimuli). All passersby and their interactions with the robot and the store were systematically coded and compared.FindingsThe study found that the better placement of HSRs (inside or outside the store) is contingent on the goals the retailer prioritizes. When the goal is to create awareness and interest toward the store, the HSR should be placed outside, as it has double the stopping power. To induce consumers to enter the store, placement of the HSR inside the store is the better option. Ultimately, however, in terms of the number of transactions and total amount spent, outside placement of the HSR outperforms inside placement.Research limitations/implicationsThis study was not able to verify the internal emotional/cognitive state of the passersby, as the method relied on unobtrusive camera observations. A longitudinal research design would be desirable to exclude potential bias due to the novelty effect.Originality/valueWhile research on robots in retail services is emerging, this study is the first to provide insights on how retailers can decide on the placement of robots inside or outside the store, depending on the particular goals they are aiming to reach at the point of purchase.

Initiative Defense against Facial Manipulation

Benefiting from the development of generative adversarial networks (GAN), facial manipulation has achieved significant progress in both academia and industry recently. It inspires an increasing number of entertainment applications but also incurs severe threats to individual privacy and even political security meanwhile. To mitigate such risks, many countermeasures have been proposed. However, the great majority methods are designed in a passive manner, which is to detect whether the facial images or videos are tampered after their wide propagation. These detection-based methods have a fatal limitation, that is, they only work for ex-post forensics but can not prevent the engendering of malicious behavior. To address the limitation, in this paper, we propose a novel framework of initiative defense to degrade the performance of facial manipulation models controlled by malicious users. The basic idea is to actively inject imperceptible venom into target facial data before manipulation. To this end, we first imitate the target manipulation model with a surrogate model, and then devise a poison perturbation generator to obtain the desired venom. An alternating training strategy are further leveraged to train both the surrogate model and the perturbation generator. Two typical facial manipulation tasks: face attribute editing and face reenactment, are considered in our initiative defense framework. Extensive experiments demonstrate the effectiveness and robustness of our framework in different settings. Finally, we hope this work can shed some light on initiative countermeasures against more adversarial scenarios.

Three-Dimensional Mesh Steganography and Steganalysis: A Review.

Three-dimensional (3-D) meshes are commonly used to represent virtual surfaces and volumes. Over the past decade, 3-D meshes have emerged in industrial, medical, and entertainment applications, being of large practical significance for 3-D mesh steganography and steganalysis. In this article, we provide a systematic survey of the literature on 3-D mesh steganography and steganalysis. Compared with an earlier survey (Girdhar et al., 2017), we propose a new taxonomy of steganographic algorithms with four categories: 1) two-state domain, 2) LSB domain, 3) permutation domain, and 4) transform domain. Regarding steganalysis algorithms, we divide them into two categories: 1) universal steganalysis and 2) specific steganalysis. For each category, the history of technical developments and the current technological level are introduced and discussed. Finally, we highlight some promising future research directions and challenges in improving the performance of 3-D mesh steganography and steganalysis.

My Virtual Self: The Role of Movement in Children's Sense of Embodiment.

There are vast potential applications for children's entertainment and education with modern virtual reality (VR) experiences, yet we know very little about how the movement or form of such a virtual body can influence children's feelings of control (agency) or the sensation that they own the virtual body (ownership). In two experiments, we gave a total of 197 children aged 4-14 years a virtual hand which moved synchronously or asynchronously with their own movements and had them interact with a VR environment. We found that movement synchrony influenced feelings of control and ownership at all ages. In Experiment 1 only, participants additionally felt haptic feedback either congruently, delayed or not at all - this did not influence feelings of control or ownership. In Experiment 2 only, participants used either a virtual hand or non-human virtual block. Participants embodied both forms to some degree, provided visuomotor signals were synchronous (as indicated by ownership, agency, and location ratings). Yet, only the hand in the synchronous movement condition was described as feeling like part of the body, rather than like a tool (e.g., a mouse or controller). Collectively, these findings highlight the overall dominance of visuomotor synchrony for children's own-body representation; that children can embody non-human forms to some degree; and that embodiment is also somewhat constrained by prior expectations of body form.

Towards a Live Feedback Training System: Interchangeability of Orbbec Persee and Microsoft Kinect for Exercise Monitoring

Many existing motion sensing applications in research, entertainment and exercise monitoring are based on the Microsoft Kinect and its skeleton tracking functionality. With the Kinect’s development and production halted, researchers and system designers are in need of a suitable replacement. We investigated the interchangeability of the discontinued Kinect v2 and the all-in-one, image-based motion tracking system Orbbec Persee for the use in an exercise monitoring system prototype called ILSE. Nine functional training exercises were performed by six healthy subjects in front of both systems simultaneously. Comparing the systems’ internal tracking states from ’not tracked’ to ‘tracked’ showed that the Persee system is more confident during motion sequences, while the Kinect is more confident for hip and trunk joint positions. Assessing the skeleton tracking robustness, the Persee’s tracking of body segment lengths was more consistent. Furthermore, we used both skeleton datasets as input for the ILSE exercise monitoring including posture recognition and repetition-counting. Persee data from exercises with lateral movement and in uncovered full-body frontal view provided the same results as Kinect data. The Persee further preferred tracking of quasi-static lower limb motions and tight-fitting clothes. With these limitations in mind, we find that the Orbbec Persee is a suitable replacement for the Microsoft Kinect for motion sensing within the ILSE exercise monitoring system.

Security Challenges of Location Privacy in VANETs and State-of-the-Art Solutions: A Survey

A Vehicular Ad-hoc Network (VANET) comprises a group of moving or stationary vehicles connected by a wireless network. VANETs play a vital role in providing safety and comfort to drivers in vehicular environments. They provide smart traffic control and real-time information, event allocation. VANETs have received attention in support of safe driving, intelligent navigation, emergency and entertainment applications in vehicles. Nevertheless, these increasingly linked vehicles pose a range of new safety and security risks to both the host and its associated properties and may even have fatal consequences. Violations of national privacy and vehicle identities are a major obstacle to introducing forced contact protocols in vehicles. Location privacy refers to the privacy of the vehicle (driver) and the location of the vehicle. Whenever a vehicle sends a message, no one but authorized entities should know their real identity and location of the vehicle. All the messages sent by the vehicle must be authenticated before processing, hence location privacy is an important design aspect to be considered in VANETs operations. The novelty of this paper is that it specifically reviews location privacy in VANETs in terms of operational and safety concerns. Furthermore, it presents a critical analysis of various attacks, identity thefts, manipulation and other techniques in vogue for location privacy protection available in state-of-the-art solutions for VANETs. The efforts in this paper will help researchers to develop a great breadth of understanding pertaining to location privacy issues and various security threats encountered by VANETs and present the critical analysis of the available state-of-the- art solutions to maintain location privacy in VANETs.

Mobility-aware computational offloading in mobile edge networks: a survey

Technological evolution of mobile devices, such as smart phones, laptops, wearable and other handheld devices have come up with the emergence of different user applications in learning, social networking, entertainment, and community computing domains. Many of such applications are fully or partially offloaded to the nearby server capable with high computing and storage resources. The delivery of task offloading results to the users is a challenge in those networks where the frequency of user mobility is high, leading to increased latency, higher energy consumption and inefficient resource utilization. In this paper, we survey the existing studies which optimize the task offloading in edge networks with mobility management. We formulate taxonomy of the research domain for classification of research works. We compare the listed state-of-the-art research works based on the components identified from taxonomy. Moreover, we debate future research directions for mobility, security, and scalability aware MEC offloading.

Wireless Virtual Reality in Beyond 5G Systems with the Internet of Intelligence

Virtual reality (VR) over wireless has promising applications in healthcare, education, entertainment, and industrial production. However, it is difficult for the existing wireless systems to meet the needs of massive content transmission, ultra-low latency, and high computation in wireless VR. In this article, with the recent advances of edge intelligence and the Internet of Intelligence, we propose a novel framework that can jointly provide computation, storage, and communication resources for wireless VR in beyond 5G systems. In this framework, intelligence can be fully exploited to coordinate the computing, caching, and transmission systems to enable ubiquitous deployments of wireless VR. We present some key techniques and propose specific methods to support wireless VR. In addition, we propose a novel quantum-inspired RL reinforcement learning (QRL) algorithm for the multidimensional resource provisioning issue in wireless VR. In the simulations, some essential performance metrics are evaluated and some interesting results are presented, showing the effectiveness of the proposed strategy.

Serverless streaming for emerging media: towards 5G network-driven cost optimization

Immersive 3D media is an emerging type of media that captures, encodes and reconstructs the 3D appearance of people and objects, with applications in tele-presence, teleconference, entertainment, gaming and other fields. In this paper, we discuss a novel concept of live 3D immersive media streaming in a serverless setting. In particular, we present a novel network-centric adaptive streaming framework which deviates from a traditional client-based adaptive streaming used in 2D video. In our framework, the decisions for the production of the transcoding profiles are taken in a centralized manner, by considering consumer metrics vs provisioning costs and inferring an expected consumer quality of experience and behaviour based on them. In addition, we demonstrate that a naive application of the serverless paradigm might be sub optimal under some common immersive 3D media scenarios.

Learning Deep Patch representation for Probabilistic Graphical Model-Based Face Sketch Synthesis

Face sketch synthesis has a wide range of applications in both digital entertainment and law enforcement. State-of-the-art examplar-based methods typically exploit a Probabilistic Graphical Model (PGM) to represent the joint probability distribution over all of the patches selected from a set of training data. However, these methods suffer from two main shortcomings: (1) most of these methods capture the evidence between patches in pixel-level, which lead to inaccurate parameter estimation under bad environment conditions such as light variations and clutter backgrounds; (2) the assumption that a photo patch and its corresponding sketch patch share similar geometric manifold structure is not rigorous. It has shown that deep convolutional neural network (CNN) has outstanding performance in learning to extract high-level feature representation. Therefore, we extract uniform deep patch representations of test photo patches and training sketch patches from a specially designed CNN model to replace pixel intensity, and directly match between them, which can help select better candidate patches from training data as well as improve parameter learning process. In this way, we investigate a novel face sketch synthesis method called DPGM that combines generative PGM and discriminative deep patch representation, which can jointly model the distribution over the parameters for deep patch representation and the distribution over the parameters for sketch patch reconstruction. Then, we apply an alternating iterative optimization strategy to simultaneously optimize two kinds of parameters. Therefore, both the representation capability of deep patch representation and the reconstruction ability of sketch patches can be boosted. Eventually, high quality reconstructed sketches which is robust against light variations and clutter backgrounds can be obtained. Extensive experiments on several benchmark datasets demonstrate that our method can achieve superior performance than other state-of-the-art methods, especially under the case of bad light conditions or clutter backgrounds.

Fully Printed, Large-Size Alternating Current Electroluminescent Device on Fabric for Wearable Textile Display

Wearable display has attracted more and more attention from academia and industry due to its potential applications in entertainment and fashion, human–computer interaction, intelligent clothing, a...

Kopin Hits OLED Microdisplay Milestone

MICRODISPLAY SUPPLIER KOPIN CORPORATION—which specializes in wearable headset products for the military, industrial, enterprise, and consumer markets—says it shipped the first production order of its 720p color OLED microdisplays to a US company for public safety applications. The 0.49-in.-diagonal 720p display uses the combination of Kopin's proprietary ColorMax technology for superior color fidelity and a duo-stack OLED structure (which is essentially two layers of OLEDs) for high current efficiency and low power consumption. Kopin makes microdisplays used in everything from helmet displays for fighter jets to smart glasses for running and cycling. It has historically manufactured its own active-matrix LCD and liquid crystal on silicon (LCOS) displays, but it is pursuing a fabless model for its OLED products. The 720p-color OLEDs are made by the Chinese firm Lakeside Optoelectronic Technology at its factory in Changzhou, China. “We are very pleased with our first production order of our color duo-stack 720 OLED microdisplays, which is for a product made by a US company,” John C.C. Fan, CEO of Kopin, says in a press release. “Though this initial order is fairly modest in size, it is an important milestone for our OLED microdisplay business, and is a true proof-point of our fabless OLED display business model in which our OLED partner, Lakeside, manufactures the displays on our ColorMax backplane wafers to meet our specifications. With the successful production of duo-stack color displays on our ColorMax wafers, we believe it will be the first of many orders from companies around the world that want to leverage our industry-leading technology.” Kopin's color display is made with a duo-stack white OLED structure and color filters. The company's patent-pending ColorMax technology uses a specially configured anode structure in the silicone backplane to suppress color mixing among tiny subpixels (2.8 × 8.4 micrometers in the 720p display), which be problematic would otherwise in duo-stack OLED structures. Duo-stack is Kopin's strategy to improve luminance, a long-time challenge in using OLED microdisplays for augmented reality applications, says Guillaume Chansin, director of display research for Display Supply Chain Consultants. “When you're going to overlap an image in front of the reality, you want that image to be bright enough so you can see it, especially in bright daylight,” Chansin says. “The OLED technology that has been available so far was not bright enough, so now what companies like Kopin are doing is to find ways to push the brightness up.” Other microdisplay firms are taking different approaches, such as Hopewell Junction, New York-based eMagin Corporation, which uses a technique called direct patterning in its OLEDs that produces red, green, and blue (RGB) light without requiring a color filter. Both Kopin and eMagin have achieved more than 7,000 nits with their OLED microdisplays. Chansin says it remains unclear, though, which technical approach is superior for mass-market consumer devices. He notes that Sony is also a big player in OLED microdisplays, but it has yet to disclose a particular strategy for improving brightness. “It's really hard to tell at this point, because this problem has been around for quite a long time and a lot of people have come out with ideas to do a brighter OLED,” Chansin says. “But we still don't know what the limit is. Both companies [Kopin and eMagin] say they want to go to something like 30,000 nits eventually.” In the near term, other opportunities exist for OLED microdisplays in virtual reality (VR) headsets, where brightness may not be as much of a key requirement. Kopin has already worked with Panasonic to produce micro-OLED panels for prototype ultra-high definition (UHD) VR goggles. The 1.3-in. panels, which feature high dynamic range and 2.6K (2,560 × 2,560) resolution, work in combination with 3M's Pancake optics to create a lightweight goggle suitable for VR sports or entertainment applications. —Glen Dickson Kopin's Microdisplay A 0.49-in. diagonal 720p OLED display incorporating ColorMax technology. source: Kopin