Human Resources and Knowledge Management Based on E-Democracy

Human-computer interaction for virtual-real fusion

Object acquisition and selection are two important functions performed in most of the human computer interaction (HCI) systems. Various techniques are devised by the researchers to perform these operations and the selection of a combination of object acquisition and selection techniques for a particular HCI system has become a research issue, especially, when the user of these systems are differently abled persons. This paper presents a review on object acquisition and selection techniques used in HCI systems. It starts with the introduction to HCI systems, gives an overview on object acquisition & selection techniques, feedback modes, performing mouse analogous actions using eye blinks, the applications of HCI systems, and finally discusses challenges and issues related to these techniques.

Building it better: Applying human–computer interaction and persuasive system design principles to a monetary limit tool improves responsible gambling

We first introduce the concepts of single-modal information processing and multi-modal information fusion in cognitive science. Some classical multi-modal information fusion models and their computer implementations in history are also explained. Under the conditions that each channels information can be obtained, and their features could be unified representation synchronously, the fusion of multi-modal information can be transformed into classification or regression problems. For practical human-computer interaction systems, the performance of multi-modal information fusion largely relies on the accuracy of the single-modal information identification and the design of the interactive system. We present a practical example of multi-modal information fusion system, and discuss its performances on human computer interaction. Finally, the possible and important development trends for multi-modal human-computer interaction techniques and systems are discussed.

In recent years, flexible sensors based on laser-induced graphene (LIG) have played an important role in areas such as smart healthcare, smart skin, and wearable devices. This paper presents the fabrication of flexible sensors based on LIG technology and their applications in human-computer interaction (HCI) systems. Firstly, LIG with a sheet resistance as low as 4.5 Ω per square was generated through direct laser interaction with commercial polyimide (PI) film. The flexible sensors were then fabricated through a one-step method using the as-prepared LIG. The applications of the flexible sensors were demonstrated by an HCI system, which was fabricated through the integration of the flexible sensors and a flexible glove. The as-prepared HCI system could detect the bending motions of different fingers and translate them into the movements of the mouse on the computer screen. At the end of the paper, a demonstration of the HCI system is presented in which words were typed on a computer screen through the bending motion of the fingers. The newly designed LIG-based flexible HCI system can be used by persons with limited mobility to control a virtual keyboard or mouse pointer, thus enhancing their accessibility and independence in the digital realm.

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The use of hand gestures provides a natural alternative to cumbersome interface devices for Human-Computer Interaction (HCI) systems. As the technology advances and communication between humans and machines becomes more complex, HCI systems should also be scaled accordingly in order to accommodate the introduced complexities. In this paper, we propose a methodology to scale hand gestures by forming them with predefined gesture-phonemes, and a convolutional neural network (CNN) based framework to recognize hand gestures by learning only their constituents of gesture-phonemes. The total number of possible hand gestures can be increased exponentially by increasing the number of used gesture-phonemes. For this objective, we introduce a new benchmark dataset named Scaled Hand Gestures Dataset (SHGD) with only gesture-phonemes in its training set and 3-tuples gestures in the test set. In our experimental analysis, we achieve to recognize hand gestures containing one and three gesture-phonemes with an accuracy of 98.47% (in 15 classes) and 94.69% (in 810 classes), respectively. Our dataset, code and pretrained models are publicly available.

Since 21st century, social aging trend has become more and more serious. It needs accurately identify the nursing needs of elderly disabled people, who are characterized by inconvenient movement and unclear speech. How to solve these problems has become a key point in the field of elderly care and medical care. In response to this problem, this research has designed a human-computer interactive gesture recognition system for elderly nursing beds in the context of big data. The recognition rate of the fusion feature + support vector machine (SVM) classifier adopted in this study is higher than 90% for each category of gesture. On the test set, this method has an average recognition rate of 96.35%, which is much higher than that of single feature + SVM classifier. While other methods’ recognition rate is lower than 90%. The recognition rate of tag c (nursing bed posture turning left) with obvious gesture feature information is as high as 99.28%, and that of tag h (nursing bed posture bedpan lowering) with weak gesture feature is 93.65%. The human-computer interaction system has well realized the recognition intention of user’s dynamic and static gestures, achieved the goal set by the research, and the interaction form is natural and reliable. In the later research, we can further realize a more comprehensive, accurate and natural human-computer interaction product design through the multi-channel joint decision-making scheme to meet the needs of the elderly.

The main reason for motor vehicular accidents is the driver drowsiness. This work shows a surveillance system developed to detect and alert the vehicle driver about the presence of drowsiness. It is used a smartphone like small computer with a mobile application using Android operating system to implement the Human Computer Interaction System. For the detection of drowsiness, the most relevant visual indicators that reflect the driver’s condition are the behavior of the eyes, the lateral and frontal assent of the head and the yawn. The system works adequately under natural lighting conditions and no matter the use of driver accessories like glasses, hearing aids or a cap. Due to a large number of traffic accidents when driver has fallen asleep this proposal was developed in order to prevent them by providing a non-invasive system, easy to use and without the necessity of purchasing specialized devices. The method gets 93.37% of drowsiness detections.

In the recent era of smart world, smarter technologies are gaining focus on human computer interaction (HCI) systems, and traditional ways like remote control, mouse, keyboard, etc. are becoming less popular. This paper presents a framework of simple yet efficient approach for future applications of the new age smart and intelligent technologies that shall enhance the HCI specifically for smart television. Hand gesture recognition (HGR)-based model is proposed for wireless control of smart interactive television (SITV), which includes controlling volume and selecting channels. The proposed framework undergoes three steps: 1) the hand gesture of the person is detected by using shape, colour and skin similarity; 2) the extracted features are classified by using rule-based classification and a gesture code is generated; 3) the classified gesture is interpreted by a novel interpreter. The performance of the proposed framework is evaluated with different people's hand gestures and compared with the techniques of others.

In human computer interaction and dialogue systems, it is often desirable for text-to-speech synthesis to be able to generate natural sounding speech with an arbitrary speaker's voice and with varying speaking styles and/or emotional expressions. We have developed an average-voice-based speech synthesis method using statistical average voice models and model adaptation techniques for this purpose. In this paper, we describe an overview of the speech synthesis system and show the current performance with several experimental results.

The European research project INT-MANUS embedded in the I*PROMS European network of excellence addresses the increasing demand for flexibility and adaptivity, which is summarised by rapid reconfiguration of complete factories, flexible reaction to new demands as well as related aspects in human computer interaction (HCI), software, and production systems. The project's main goal has been to develop a new technology for the production plants of the future: the Smart Connected Control Platform (SCCP). This platform allows controlling a factory with the help of an open distributed and learning agent platform that integrates machines, robots, and human personnel. It offers an enterprise service bus like concept for dynamic and decentrally controlled production systems, which flexibly connects machines and IT systems like robotic transport systems, terminals, mobile control systems, etc.

Head pose is an important indicator of a person's attention, gestures, and communicative behavior with applications in human computer interaction, multimedia and vision systems. In this paper, we present a novel head pose estimation system by performing head region detection using the Kinect [2], followed by face detection, feature tracking, and finally head pose estimation using an active camera. Ten feature points on the face are defined and tracked by an Active Appearance Model (AAM). We propose to use the scene flow approach to estimate the head pose from 2D video sequences. This estimation is based upon a generic 3D head model through the prior knowledge of the head shape and the geometric relationship between the 2D images and a 3D generic model. We have tested our head pose estimation algorithm with various cameras at various distances in real time. The experiments demonstrate the feasibility and advantages of our system.

DAVE is a comprehensive set of event detection techniques to monitor and detect 5 important verbal agitations: asking for help, verbal sexual advances, questions, cursing, and talking with repetitive sentences. The novelty of DAVE includes combining acoustic signal processing with three different text mining paradigms to detect verbal events (asking for help, verbal sexual advances, and questions) which need both lexical content and acoustic variations to produce accurate results. To detect cursing and talking with repetitive sentences we extend word sense disambiguation and sequential pattern mining algorithms. The solutions have applicability to monitoring dementia patients, for online video sharing applications, human computer interaction (HCI) systems, home safety, and other health care applications. A comprehensive performance evaluation across multiple domains includes audio clips collected from 34 real dementia patients, audio data from controlled environments, movies and Youtube clips, online data repositories, and healthy residents in real homes. The results show significant improvement over baselines and high accuracy for all 5 vocal events.

This paper presents a systematic technical review of the human-computer interaction (HCI), Shared Control Systems (SCS), and pervasive System of Systems (SoS) for safe indoor maneuvering in a closed environments using Smart Power Wheelchairs designed for individuals with limited abilities and the elderly. Techniques from HCI, SCS and SoS employed by different researchers in the development of smart wheelchairs are studied and examined. Their advantages and drawbacks in terms of development and ease of operation are discussed and tabulated. Finally, an exemplary architecture for the HCI and SCS of pervasive SoS technologies is proposed, along with key components and shared control system models for the design and development of smart power wheelchairs.