Haiwei Dong

Evaluating and Improving the Depth Accuracy of Kinect for Windows v2

Microsoft Kinect sensor has been widely used in many applications since the launch of its first version. Recently, Microsoft released a new version of Kinect sensor with improved hardware. However, the accuracy assessment of the sensor remains to be answered. In this paper, we measure the depth accuracy of the newly released Kinect v2 depth sensor, and obtain a cone model to illustrate its accuracy distribution. We then evaluate the variance of the captured depth values by depth entropy. In addition, we propose a trilateration method to improve the depth accuracy with multiple Kinects simultaneously. The experimental results are provided to ascertain the proposed model and method.

Towards whole body fatigue assessment of human movement: a fatigue-tracking system based on combined sEMG and accelerometer signals.

This paper proposes a method to assess the overall fatigue of human body movement. First of all, according to previous research regarding localized muscular fatigue, a linear relation is assumed between the mean frequency and the muscular working time when the muscle is experiencing fatigue. This assumption is verified with a rigorous statistical analysis. Based on this proven linearity, localized muscular fatigue is simplified as a linear model. Furthermore, localized muscular fatigue is considered a dynamic process and, hence, the localized fatigue levels are tracked by updating the parameters with the most current surface electromyogram (sEMG) measurements. Finally, an overall fatigue level is computed by fusing localized muscular fatigue levels. The developed fatigue-tracking system is evaluated with two fatigue experiments (in which 10 male subjects and seven female subjects participated), including holding self-weight (dip start position training) and lifting weight with one arm (arm curl training).

From 3D Sensing to Printing: A Survey

Three-dimensional (3D) sensing and printing technologies have reshaped our world in recent years. In this article, a comprehensive overview of techniques related to the pipeline from 3D sensing to printing is provided. We compare the latest 3D sensors and 3D printers and introduce several sensing, postprocessing, and printing techniques available from both commercial deployments and published research. In addition, we demonstrate several devices, software, and experimental results of our related projects to further elaborate details of this process. A case study is conducted to further illustrate the possible tradeoffs during the process of this pipeline. Current progress, future research trends, and potential risks of 3D technologies are also discussed.

Exploring Latent Preferences for Context-Aware Personalized Recommendation Systems

Context-aware recommendations offer the potential of exploiting social contents and utilize related tags and rating information to personalize the search for content considering a given context. Recommendation systems tackle the problem of trying to identify relevant resources from the vast number of choices available online. In this study, we propose a new recommendation model that personalizes recommendations and improves the user experience by analyzing the context when a user wishes to access multimedia content. We conducted empirical analysis on a dataset from last.fm to demonstrate the use of latent preferences for ranking items under a given context. Additionally, we use an optimization function to maximize the mean average precision measure of the resulted recommendation. Experimental results show a potential improvement to the quality of the recommendation in terms of accuracy when compared with state-of-the-art algorithms.

RecAm: a collaborative context-aware framework for multimedia recommendations in an ambient intelligence environment

AbstractWith an ever-increasing accessibility to different multimedia contents in real-time, it is difficult for users to identify the proper resources from such a vast number of choices. By utilizing the user’s context while consuming diverse multimedia contents, we can identify different personal preferences and settings. However, there is a need to reinforce the recommendation process in a systematic way, with context-adaptive information. The contributions of this paper are twofold. First, we propose a framework, called RecAm, which enables the collection of contextual information and the delivery of resulted recommendation by adapting the user’s environment using Ambient Intelligent (AmI) Interfaces. Second, we propose a recommendation model that establishes a bridge between the multimedia resources, user joint preferences, and the detected contextual information. Hence, we obtain a comprehensive view of the user’s context, as well as provide a personalized environment to deliver the feedback. We demonstrate the feasibility of RecAm with two prototypes applications that use contextual information for recommendations. The offline experiment conducted shows the improvement of delivering personalized recommendations based on the user’s context on two real-world datasets.

See in 3D: state of the art of 3D display technologies

With advances in lasers, optics, and electronics, many new 3D display technologies have been proposed with prototypes in research labs or have entered the marketplace. Although some of these technologies (such as Stereoscopy) are familiar to people, other technologies, such as holography, remain far-fetched to most. This survey introduces the principles of current popular 3D display technologies, which are generally categorized into four categories: 3D movies, on-stage holograms, holographic projections and volumetric 3D displays. Furthermore, the limitations of each of the aforementioned technologies are deeply analyzed, and comparisons of these technologies are provided. Moreover, we note appropriate application situations for the various technologies. Because computer-generated hologram (CGH) technologies are considered to be the next generation of 3D display technology and have become a dominant direction in 3D display technology development, we address the challenges that CGH is currently facing and provide an insightful analysis of solutions proposed in recent years. Finally, we study the current 3D display applications associated with the four categorized technology principles.

Ubiquitous Biofeedback Serious Game for Stress Management

Serious games augment utilitarian applications with an entertainment dimension. Hence, information pertaining to a utilitarian objective is seamlessly incorporated into the gaming scenario. In this paper, we present the concept of ubiquitous biofeedback serious games (UBSGs), a family of games that integrate biofeedback processes in their operation. They rely on physiological inputs collected from the player through biological sensors for game control. These physiological inputs are converted into quantifiable parameters that reflect the status of a certain physiological process. To prove the practicality of this concept, we develop a UBSG aimed at providing mental stress management services to players. We assess the ability of the game feedback to assist players in modulating their behavior to reduce their stress levels. In our evaluation, we have shown that the majority of participating subjects showed more control over their mental stress when game feedback was enabled.

From Sense to Print: Towards Automatic 3D Printing from 3D Sensing Devices

In this paper, we introduce the From Sense to Print system. It is a system where a 3D sensing device connected to the cloud is used to reconstruct an object or a human and generate 3D CAD models which are sent automatically to a 3D printer. In other words, we generate ready-to-print 3D models of objects without manual intervention in the processing pipeline. Our proposed system is validated with an experimental prototype using the Kinect sensor as the 3D sensing device, the KinectFusion algorithm as our reconstruction algorithm and a fused deposition modeling (FDM) 3D printer. In order for the pipeline to be automatic, we propose a semantic segmentation algorithm applied to the 3D reconstructed object, based on the tracked camera poses obtained from the reconstruction phase. The segmentation algorithm works with both inanimate objects lying on a table/floor or with humans. Furthermore, we automatically scale the model to fit in the maximum volume of the 3D printer at hand. Finally, we present initial results from our experimental prototype and discuss the current limitations.

3-D Markerless Tracking of Human Gait by Geometric Trilateration of Multiple Kinects

In this paper, we develop an integrated markerless gait tracking system with three Kinect v2 sensors. A geometric principle-based trilateration method is proposed for optimizing the accuracy of the measured gait data. To tackle the data synchronization problem among the Kinect clients and the server, a synchronization mechanism based on network time protocol (NTP) is designed for synchronizing the server and Kinect clients’ clocks. Furthermore, a time schedule is designed for timing each Kinect clients data transmission. In the experiment, participants are asked to perform a 60-s walk, while the proposed tracking system obtains the participants gait data. Six joints (including left hip, right hip, left knee, right knee, left ankle, and right ankle) of the participants are tracked where the obtained gait data are described as 6000 movements of joint positions (1000 movements for each joint). The results show that the trilateration tracking result by the three Kinect sensors has a much higher accuracy compared with the accuracy measured by a single Kinect sensor. Within a randomly sampled time period (67.726 s in the experiment), 98.37

A Framework of Reconfigurable Transducer Nodes for Smart Home Environments

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