Advanced Human-computer Interaction Research Articles

Face Recognition as a Biometric Application

A facial recognition system is a technology capable of identifying or verifying a person from a digital image or a video frame from a video source. There are multiple methods in which facial recognition systems work, but in general, they work by comparing selected facial features from the given image with faces within a database. It is also described as a Biometric Artificial Intelligence-based application that can uniquely identify a person by analyzing patterns based on the person's facial textures and shape. While initially a form of computer application, it has seen wider uses in recent times on mobile platforms and in other forms of technology, such as robotics. It is typically used as access control in security systems and can be compared to other biometrics such as fingerprint or eye iris recognition systems. Although the accuracy of facial recognition system as biometric technology is lower than iris recognition and fingerprint recognition, it is widely adopted due to its contactless and non-invasive process. Recently, it has also become popular as a commercial identification and marketing tool. Other applications include advanced human-computer interaction, video surveillance, automatic indexing of images and video database, among others. The use of facial recognition has recently become a very debatable subject and has been criticized more and more because it was considered an unethical tool used to spy on the public. The reason for such criticism is, however, largely due to the lack of information and regulation of this technology. Used proportionally and responsibly, facial recognition can and should be beneficial. It has the capacity to do much more to increase security in the future-from street crime to airport security. Armed war crime has dominated UK titles throughout the year. Recent statistics indicate that the number of people who benefited from the emergency assistance due to armed attacks increased by almost 40% compared to the two years ago, while the number of children under 18 years of age with stab wounds is increasing by 86 % in only four years. Face recognition has become a normal activity in many airports around the world. Many people today have a so-called biometric passport that allows them to go faster to the gate without having to be controlled. The facial recognition used in this way has significantly reduced waiting times for passport control but also has the ability to increase security in and around airports. Face recognition thus allows officers to identify an individual more quickly and accurately than the human eye. While some critics may worry about technology-related confidentiality issues, airports have shown that the use of facial recognition has improved security as well as speeding up processes such as check-in and in the future, even procedures boarding. When used correctly and proportionally, facial recognition can help protect the public and improve national security on multiple fronts. Similarly, advanced technology can recognize a person seen on CCTV security systems at the crime scene, justifying a person's stop and search. The ability to check in real time whether a person is on the list of criminal investigations adds an added advantage to the decision-making process before stopping and searching, thus lowering the probability of discrimination.

Research on Key Technologies of Equipment IETM

The development of equipment IETM is a complex project because of the difficulties in data and interaction technologies. The paper comprehensively applies computer technology to research data of equipment IETM. The conceptual data model was established. Data identification and storage were studied, and data programming was studied. Advanced human-computer interaction is applied to equipment IETM. The interactive interface of IETM is designed. Data interaction technology was studied, which provides effective basis for the development of the Equipment IETM.

Cognitive evaluation for the diagnosis of Alzheimer's disease based on Turing Test and Virtual Environments

Alzheimer's screening tests are commonly used by doctors to diagnose the patient's condition and stage as early as possible. Most of these tests are based on pen-paper interaction and do not embrace the advantages provided by new technologies. This paper proposes novel Alzheimer's screening tests based on virtual environments and game principles using new immersive technologies combined with advanced Human Computer Interaction (HCI) systems. These new tests are focused on the immersion of the patient in a virtual room, in order to mislead and deceive the patient's mind. In addition, we propose two novel variations of Turing Test proposed by Alan Turing as a method to detect dementia. As a result, four tests are introduced demonstrating the wide range of screening mechanisms that could be designed using virtual environments and game concepts. The proposed tests are focused on the evaluation of memory loss related to common objects, recent conversations and events; the diagnosis of problems in expressing and understanding language; the ability to recognize abnormalities; and to differentiate between virtual worlds and reality, or humans and machines. The proposed screening tests were evaluated and tested using both patients and healthy adults in a comparative study with state-of-the-art Alzheimer's screening tests. The results show the capacity of the new tests to distinguish healthy people from Alzheimer's patients.

External Resource Annotation Framework and its applications in E-learning

Rich Internet Applications that enable new ways of interaction with digital objects within multimedia scenes become important for educational communities. Such new ways of advanced human-computer interaction, in its different forms match the needs of formal education as well as lifelong learning in medicine, architecture, construction or cultural heritage management. This paper presents innovative multimedia annotation methods based on advanced human-computer interaction and (a)synchronous interpersonal collaboration features, focusing on e-learning activities. They have been implemented within eRAF (External Resource Annotation Framework) with the scope of going beyond the existing solutions by introducing new annotation types using external resources. Thus, anyone can easily annotate the own resources with additional information such as real-time handwriting, text and animation, or even audio/video sequences stored into public data repositories, or recorded ad-hoc, in order to create value-added interactive content.

The ergonomics of virtual reality: human factors in developing clinical-oriented virtual environments.

Virtual Reality (VR) is usually described as a collection of technological hardware: a computer capable of 3D real-time animation, a head-mounted display, data gloves equipped with one or more position trackers. However, this focus on technology is disappointing for clinicians interested in developing virtual environments to be used in assessment and therapy. To overcome this limitation this chapter describes VR as an advanced communication tool: a communication medium in the case of multi-user VR and a communication interface in single-user VR. Two are the core characteristics of VR as communication tool: the perceptual illusion of nonmediation and the sense of community. The first characteristic of a satisfying virtual environment is the disappearance of mediation, a level of experience where both the VR system and the physical environment disappear from the user's phenomenal awareness. The second characteristic is the sense of community developed by interaction. Through interaction made possible by multi-user VR, individuals find or form groups that share interests. So, information exchange becomes the carrier for expressing self-concept and eliciting emotional support. Within this view, experiencing presence and telepresence does not depend so much on the faithfulness of the reproduction of 'physical' aspects of 'external reality'--which is also a social production, and not a primitive or 'natural' fact--as on the capacity of simulation to produce a context in which social actors may communicate and cooperate. The consequences of this approach for the design and the development of clinical oriented VR systems are presented, together with the methodological and technical implications for the study of advanced human-computer interaction.

Yüz ifadelerinin otomatik analizi üzerine bir literatür çalışması

Facial expressions are universal symbols of emotions that provide cohesion to interpersonal communication. At the same time, the changes in facial expressions are considered to be the most important hints in the psychology of emotion. Facial expression analysis has widespread range of application in areas such as analysis of human behaviors, human-human interaction and human-computer interaction. While humans do not have any difficulty for recognizing and interpreting facial expressions, it is challenging problem for the computers. However, especially with advances in human-computer interaction, understanding the human emotions by computer has become an indispensable issue. Furthermore, analysis and recognition of facial expressions has pervaded in various areas such as security, psychology, health, robotics and virtual reality. For these reasons, the analyzing of facial expressions quickly and the recognition of facial expressions correctly according to the analyzed facial expressions play a critical role for many software systems in different applications. In this study, the methods used in the analysis of facial expressions and the studies in the literature on emotion recognition were investigated.

Designing engaging intelligent tutoring systems in an age of cognitive computing

Advances in human computer interaction (HCI) are enabling increasingly more human-like interactions with computers. In this position paper, we explore the impact of several such advances in HCI on the design of an intelligent tutoring system (ITS), with the hypothesis that such systems may drive deeper engagement and hence improve learning outcomes. Researchers have made claims regarding learning gains resulting from self-explanations, frequent addressing of errors and impasses, rich natural language understanding and dialog, appropriate degree of interactivity, and use of multiple representations. However, many studies on current ITSs that have embodied one or more of the above features are showing little to no discernible impact on learning. This is possibly partly due to the poor user experience. Our tutoring system is aimed at addressing learning challenges for K-12 students, by integrating a suite of differentiating technologies around interactivity, dialog, automated question generation, and learning analytics. In this paper, we first review learning theories and insights gleaned from prior research on ITSs that have inspired our design. We then describe the functional architecture of our tutoring system, followed by a preliminary report on the status of the prototype currently being built.

A layered architecture for probabilistic complex pattern recognition to detect user preferences

The recognition of complex patterns is nowadays one of the most challenging tasks in machine learning, and it promises to be of great benefit for many applications, e.g. by allowing advanced human computer interaction to access the user’s situative context. This work examines a layered architecture that operates on different temporal granularities to infer complex patterns of user preferences. Classical hidden Markov models (HMM), conditioned HMM (CHMM) and fuzzy CHMM (FCHMM) are compared to find the best configuration in the lower architecture layers. In the uppermost layer, a Markov logic network (MLN) is applied to infer the user preference in a probabilistic rule-based manner. For each layer a comprehensive evaluation is given. We provide empirical evidence showing that the layered architecture using FCHMM and MLN is well-suited to recognize patterns on different layers.

Development of Stimulator Based on Audio-Visual Feedback Signal

To improve the muscle function handicap and enhance the body movement function of the stroke patients, a new medical instrument, based on audio-visual feedback, is developed. The designed stimulator regards sound (voice) and light signal (flash lamp) as the command signals. With remind of both voice and visualization signal, trainers can take the initiative to participate in training and try their best to generate a weak electromyographic signal. It provides a new treatment platform for stroke patients, which can play a positive role in the rebuilding of cerebral nerve net, the rehabilitation of body movement function diseases, the protection of brain function and psychological rehabilitation. The real-time monitoring and regulating function for the stimulating current was innovatively achieved. With the above functions, it’s easy for doctors to set up different stimulating intensities for different patients. This stimulator also has advantages of safer noninvasive, easy to carry and advanced human-computer interaction function, all of these make contributions to building up the muscle strength and the rehabilitation of body movement.

Adaptive Human Action Recognition With an Evolving Bag of Key Poses

Vision-based human action recognition allows to detect and understand meaningful human motion. This makes it possible to perform advanced human-computer interaction, among other applications. In dynamic environments, adaptive methods are required to support changing scenario characteristics. Specifically, in human-robot interaction, smooth interaction between humans and robots can only be performed if these are able to evolve and adapt to the changing nature of the scenarios. In this paper, an adaptive vision-based human action recognition method is proposed. By means of an evolutionary optimization method, adaptive and incremental learning of human actions is supported. Through an evolving bag of key poses, which models the learned actions over time, the current learning memory is developed to recognize increasingly more actions or actors. The evolutionary method selects the optimal subset of training instances, features and parameter values for each learning phase, and handles the evolution of the model. The experimentation shows that our proposal achieves to adapt to new actions or actors successfully, by rearranging the learned model. Stable and accurate results have been obtained on four publicly available RGB and RGB-D datasets, unveiling the method's robustness and applicability.

Advancements in violin-related human-computer interaction

Finesse is required while performing with many traditional musical instruments, as they are extremely responsive to human inputs. The violin is specifically examined here, as it excels at translating a performer’s gestures into sound in manners that evoke a wide range of affective qualities. This type of rich responsiveness is simultaneously what makes it so challenging to play, what keeps it interesting to practice for long periods of time, and what makes overcoming these difficulties worthwhile to performer and audience alike. The capability of an instrument to render audible the complexity of human intelligence and emotion is at the core of the musical interface technology design space, MITDS. This is a framework that endeavours to retain and enhance such traits of traditional instruments in the design of interactive live performance interfaces. Utilising the MITDS, advanced human-computer interaction technologies for the violin are developed in order to allow musicians to explore new methods of creating music. Through this process, the aim is to provide musicians with control systems that let them transcend the interface itself, and focus on musically compelling performances.

Towards Intelligent Maintenance Systems: Rescuing Human Operator and Context Factors

Current technologies have used embedded devices for degradation monitoring based on Condition-Based Maintenance (CBM) paradigm for critical parts of equipments. Intelligent Maintenance Systems (IMS) have been proposed in order to autonomously provide failure predictions through data acquisition from sensors. This paper intends to discuss how to capture the operator skill and environment conditions into the IMS. We have adapted the data flow in CBM classical architecture in order to consider the resultant information from maintenance operations associated with the technical teams and their contexts. For this purpose was used a Cyber-Physical Systems (CPS) approach to consider human and context factors. Considering the CPS as system that combines and coordinates physical and computational elements, the CPS incorporates the ability to act in the physical world with the intelligence from cyber or virtual world. In this paper, we propose to use a CPS for taking account human/context factors applied maintenance strategies. The proposed tool, called Toogle-IMS, aims at developing an advanced Human-Computer Interaction (HCI) applied to Intelligent Maintenance System (IMS). We have applied the Toogle-IMS in two case studies associated with: i. mobile robots in maintenance and ii. mobile trucks in shipyards.

ASIS&T annual meeting plenary speaker: Edward chang: Mobile opportunities

AbstractEditor's SummaryAt a plenary session of the 2012 ASIS&T Annual Meeting, Edward Chang touched on recent information technology developments and explored emerging innovations. Vice president of research at hTC Corporation and past research director at Google in China, Chang recalled Google's page ranking for Web 1.0 and subsequent efforts to support mobile access, user‐generated content and pushing information for Web 2.0. Advances in human computer interaction, especially sensors in cellphones, enable a higher level of location‐based computation. Natural language processing backed by massive data and computation power supports interpretation and even translation of spoken queries to yield semantically and situationally relevant returns. Advances in location‐based services permit highly precise data delivery. Cellphone sensors can even monitor stress, detect health changes and provide security alerts. Though privacy is a critical outstanding issue, the combined capabilities of cloud computing, infrastructure, big data, social networks and cellphone sensors are leading to evolutionary and revolutionary changes.

A Novel approach for Graphical User Interface development and real time Object and Face Tracking using Image Processing and Computer Vision Techniques implemented in MATLAB

Image Processing is an advancement in the Signal processing domain where the input is an image or video and output can be image, video or in any other forms. With daily advances in Human-Computer interaction, Image Processing, along with computer vision has become an integral part of any intelligent system. They are mainly based on the workings of the visual perception function of the human brain. As such most of the algorithms are designed based around this central concept. The major application of Computer Vision is in the field of intelligent machines and robotic visions, where machines are made intelligent by being able to sense their surrounding visually and optimize themselves accordingly. In case of robots, through computer vision, they are made capable of sensing their surroundings, just like a human.

Human Pose Estimation and Activity Recognition From Multi-View Videos: Comparative Explorations of Recent Developments

This paper presents a review and comparative study of recent multi-view approaches for human 3D pose estimation and activity recognition. We discuss the application domain of human pose estimation and activity recognition and the associated requirements, covering: advanced human–computer interaction (HCI), assisted living, gesture-based interactive games, intelligent driver assistance systems, movies, 3D TV and animation, physical therapy, autonomous mental development, smart environments, sport motion analysis, video surveillance, and video annotation. Next, we review and categorize recent approaches which have been proposed to comply with these requirements. We report a comparison of the most promising methods for multi-view human action recognition using two publicly available datasets: the INRIA Xmas Motion Acquisition Sequences (IXMAS) Multi-View Human Action Dataset, and the i3DPost Multi-View Human Action and Interaction Dataset. To compare the proposed methods, we give a qualitative assessment of methods which cannot be compared quantitatively, and analyze some prominent 3D pose estimation techniques for application, where not only the performed action needs to be identified but a more detailed description of the body pose and joint configuration. Finally, we discuss some of the shortcomings of multi-view camera setups and outline our thoughts on future directions of 3D body pose estimation and human action recognition.

LXIO: The Mood Detection Robopsych

Words and languages are the direct representations of our thoughts and perceptions. Accordingly, the only access to an individual’s thoughts and psyche is through the language that he/ she speaks or writes [1]. Psychological disorders and mood states such as depression and PTSD therefore, can be identified and predicted by continuously analyzing a patient’s discourse. In doing so, we utilize the method of predictive linguistics [2], which determines cognitive mood states by computationally modeling the notions of ‘mind axiology’ and ‘emotional states’. Our proposed system expresses cognitive states in terms of axiology, i.e., the system of positive and negative values associated with concepts that are universally accepted. Axiological notions provide more insights about the various cognitive states because a patient’s words are only intelligible if axiological elements like conception, perception, and intention are taken into consideration. The framework for our analytics engine consists of multiple modules responsible for coherently and systematically retrieving, parsing and processing a patient’s discourse. The backbone of this system relies on a learning algorithm that accounts for various valuation criteria such as time-based, intrinsic, consequent and contextual value analysis, while also building a nested network of mood states and their dependencies. By executing this mechanism and correlating our computational data to results calculated by a psychological assessment testing, we are better positioned to classify a patient’s mood states – e.g., as being depressive or not. This work will have implications in the area of cognition research and practical applications such as automated neurological and psychiatrical assessments, advanced human computer interaction (HCI), as well as others.

A model for production, perception, and acquisition of actions in face-to-face communication

The concept of action as basic motor control unit for goal-directed movement behavior has been used primarily for private or non-communicative actions like walking, reaching, or grasping. In this paper, literature is reviewed indicating that this concept can also be used in all domains of face-to-face communication like speech, co-verbal facial expression, and co-verbal gesturing. Three domain-specific types of actions, i.e. speech actions, facial actions, and hand-arm actions, are defined in this paper and a model is proposed that elucidates the underlying biological mechanisms of action production, action perception, and action acquisition in all domains of face-to-face communication. This model can be used as theoretical framework for empirical analysis or simulation with embodied conversational agents, and thus for advanced human-computer interaction technologies.

EMOTION RECOGNITION BASED ON THE ANALYSIS OF FACIAL EXPRESIONS: A SURVEY

Face expression recognition is an active area of research with several fields of applications, ranging from emotion recognition for advanced human computer interaction to avatar animation for the movie industry. This paper presents a review of the state-of-the-art emotion recognition based on the visual analysis of facial expressions. We cover the main technical approaches and discuss the issues related to the gathering of data for the validation of the proposed systems.

A real-time full body tracking and humanoid animation system

Non-intrusive human body tracking is a key issue in advanced human–computer interaction, with applications ranging from virtual reality to videoconference and telepresence. This paper describes a system for vision-based tracking of body posture. The system is explicitly designed to provide a robust yet simple and inexpensive solution to real-time body tracking through a careful choice of visual and kinematic models. Human posture representation is fully compatible with the MPEG-4 standard. Results of system application to a computer graphics scenario (animation of 3D avatars) are presented and discussed.

On stage: the reactable and other musical tangibles go real

This paper explores one of the application domains in which tangible and tabletop interfaces have currently shown more positive results, studying and unveiling the essential reasons that turn live music performance and tabletop interaction into promising and exiting fields of multi-disciplinary research and experimentation. The paper is structured in three parts. The first one exposes the main reasons that turn live music performance into an ideal test-bed for tangible interaction and advanced human-computer interaction. Reciprocally, the second part studies why tabletop interfaces promise remarkable new musical instruments. The third part describes the main design issues that lead to the development of the reactable, a tabletop musical instrument that has been conceived based on many of the criteria exposed on the previous two parts.