Interactive Computer System Research Articles

RETRACTED ARTICLE: Development of hand gesture recognition system using machine learning

Human computer interaction (HCI) systems are increasing due to the demand for non-intrusive methods for communicating with machines. In this research article, vision based hand gesture recognition (HGR) System has been proposed using machine learning. This proposed system consists of three stages: segmentation, feature extraction and classification. The developed system is to be trained and tested using Sebastian Marcel static hand posture database which is available online. Discrete wavelet transform (DWT) along with modified Speed Up Robust Feature extraction technique has been used to extract rotation and scale invariant key descriptors. Then Bag of Word technique is used to develop the fixed dimension input vector that is required for the support vector machine. The classification accuracy of class 2 and class 4 which corresponds to the ‘No’ and ‘grasp’ gesture has reached 98%. The overall classification accuracy of the HGR system using SVM classifier is 96.5% with a recognition time of 0.024 s. Due to fast recognition time, this system can be employed in real time gesture image recognition system. Our HGR system addresses the complex background problem and also improves the robustness of hand gesture recognition.

Attitudes Toward Computers Moderate the Effect of Computerized Cognitive Trainings in Oldest-Old Senior Living Center Residents

Computerized cognitive interventions (CCIs) have been increasingly implemented among older adults with mild cognitive impairment (MCI). However, older individuals' attitudes toward technology may limit CCI engagement. This exploratory-developmental study examined whether a "multi-functional interactive computer system" (MICS), which provides pleasurable activities via computer, would improve attitudes toward computers and in turn increase the efficacy of a subsequent CCI. A phase one double-blind trial randomized 49 seniors with MCI to a MICS + CCI condition or a CCI-only condition. Attitudes toward technology use was assessed using The Attitudes Toward Computers Questionnaire (ATCQ), and cognition was assessed using episodic memory and executive function composite scores at baseline, the ends of MICS and CCI phases, and 3-month follow-up. The MICS + CCI group did not show significantly greater improvement in cognition than the CCI only group. Secondary analyses indicated that improvement in executive function from baseline occurred in both groups. Participants who did show improved attitudes toward computers, whether through MICS or simply computer exposure itself, showed improvement in executive function. Participants in the MICS + CCI group used MICS less than expected. A more structured and supervised approach may be needed to facilitate MICS exposure. Improved attitudes toward computers regardless of MICS exposure may benefit candidates for CCI.

Avaliação automática de acessibilidade do portal da UFRN

Resumo: A inclusão de pessoas com deficiência no ensino superior tem destacado a necessidade da acessibilidade nos sistemas computacionais interativos utilizados nesse ambiente. A nova versão do Portal da Universidade Federal do Rio Grande do Norte acompanhou esse movimento, considerando seu uso com leitores de tela e VLibras. Entretanto, sua acessibilidade ainda não havia sido avaliada. Este trabalho tem por objetivo identificar problemas de acessibilidade nesse Portal para subsidiar melhorias futuras na experiência de uso de pessoas com deficiência. Em junho de 2019, foi realizada uma avaliação por inspeção com os avaliadores automáticos de acessibilidade AInspector, HTML Code Sniffer e Siteimprove Accessibility Checker. Foram analisadas quantidade total, classificação como erros e avisos, princípios e critérios violados e o local de ocorrência dos problemas encontrados. Foi possível identificar oportunidades de melhoria do Portal nos aspectos de percepção, operação, compreensão e robustez de acessibilidade nas quatro páginas analisadas: “home”, “acadêmico”, “todas as notícias” e na página de uma notícia detalhada.
 Palavras-chave: acessibilidade. avaliação de interface. avaliador automático. wcag

A Glimpse into the Past, Present, and Future of Engineering Interactive Computing Systems

The area of Engineering Interactive Computing Systems (EICS) is defined as the intersection between Software Engineering (SE) and Human-Computer Interaction (HCI). In this paper, we provide an overview of what EICS is and how it is positioned with respect to other venues in HCI, such as CHI, UIST, and IUI, highlighting its legacy and paying homage to past scientific events from which EICS emerged. We also take this opportunity to deliver a comparative analysis of the past, present, and perhaps future research and development questions raised in EICS by analyzing the EICS papers published from its first edition to this one, based on global and chronological word clouds, phrase nets, and topic modelling.

Eyeball Movement based Cursor Control using Raspberry Pi and OpenCV

In this study, a specific human computer interaction system using eyeball movement is presented. Conventionally, computer system uses mouse as one of the data input devices. But in this system, we use eyes instead of mouse which provides a unique way of operating the computer with the help of eyeball movements. The implementation work underlying this system for pupil identification uses raspberry pi board to control the cursor of the personal computer and moreover Eye Aspect Ratio technique is ascertained along with OpenCV to detect the pupil. This system tracks the eye movements of the user with an IP cam (Internet Protocol camera) and simulates the eye movements into mouse cursor movements on screen and also detects user’s eye staring on icon and will translate it into click operation on screen. The main aim of this system is to help the user to control the cursor without the use of hands and is of great use especially for the people with disability.

A survey on speech synthesis techniques in Indian languages

The text to speech technology has achieved significant progress during the past decade and is an active area of research and development in providing different human–computer interactive systems. Even though a number of speech synthesis models are available for different languages focusing on the domain requirements with many motive applications, a source of information on current trends in Indian language speech synthesis is unavailable till date making it difficult for the beginners to initiate research for the development of TTS systems for the low-resourced languages. This paper provides a review of the contributions made by different researchers in the field of Indian language speech synthesis along with a study on the Indian language characteristics and the associated challenges in designing TTS systems. A set of available applications and tools results out of different projects undertaken by different organizations along with a set of possible future developments are also discussed to provide a single reference to an important strand of research in speech synthesis which may benefit anyone interested to initiate research in this area.

Mixed Integer Programming Technique according to Traffic Light for Autonomous Collision Free Ground based on Traffic System

Abstract Human-computer interaction (HCI) is the study of how people design, implement, and use interactive computer systems and how computers affect individuals, organizations and society. This encompasses not only ease of use but also new interaction techniques for supporting user tasks, providing better access to information, and creating more powerful forms of communication. It involves input and output devices and the interaction techniques that use them; how information is presented and requested; how the computer’s actions are controlled and monitored; all forms of help, documentation, and training; the tools used to design, build, test, and evaluate user interfaces; and the processes that developers follow when creating Interfaces. This paper is an attempt to highlights the study of interaction between people (users) and computers.

Co-Clustering to Reveal Salient Facial Features for Expression Recognition

Facial expressions are a strong visual intimation of gestural behaviors. The intelligent ability to learn these non-verbal cues of the humans is the key characteristic to develop efficient human computer interaction systems. Extracting an effective representation from facial expression images is a crucial step that impacts the recognition accuracy. In this paper, we propose a novel feature selection strategy using singular value decomposition (SVD) based co-clustering to search for the most salient regions in terms of facial features that possess a high discriminating ability among all expressions. To the best of our knowledge, this is the first known attempt to explicitly perform co-clustering in the facial expression recognition domain. In our method, Gabor filters are used to extract local features from an image and then discriminant features are selected based on the class membership in co-clusters. Experiments demonstrate that co-clustering localizes the salient regions of the face image. Not only does the procedure reduce the dimensionality but also improves the recognition accuracy. Experiments on CK plus, JAFFE and MMI databases validate the existence and effectiveness of these learned facial features.

Studying the Effect of Display Type and Viewing Perspective on User Experience in Virtual Reality Exergames.

Background: Physical inactivity has been identified as the fourth leading cause of death globally. It is now well established that a sedentary lifestyle is a unique risk factor for several diseases such as type 2 diabetes and cardiovascular disease, which account for about 30% of global mortality. Diabetes is a major preventable cause of costly and debilitating renal failure, heart disease, lower limb amputation, and avoidable blindness. In recent years, the idea of using interactive computing systems that leverage gamification to promote physical activity has been widely researched. Prior studies have shown that exergames, that is those that encourage physical activity, can increase enjoyment and intrinsic motivation compared with conventional exercises; as such, they can be effective in promoting physical and mental health. There has been some research on immersive virtual reality (VR) exergames; however, to the best of our knowledge, it is limited and preliminary. This work aims at filling the gap and investigates the effect of display type (DT) and viewing perspective (VP) on players' exertion, engagement, and overall game experience in immersive VR exergames. Objective: This article aims at examining whether DT and VP can affect gameplay performance, players' exertion, game experience, cybersickness, and electroencephalography (EEG) engagement index when playing a gesture-based (i.e., body motion) exergame. Materials and Methods: Study 1 employed a one-way between-subjects design with 24 participants equally distributed in two groups (immersive VR and 50-inch TV) to perform 12 pre-defined gestures. The main outcome measures were National Aeronautics and Space Administration-Task Load Index (NASA-TLX) workload for each group as well as 7 Likert scale and EEG engagement index for each gesture. Study 2 included 16 participants in playing a game with the gestures selected from study 1. All participants played 4 versions based on combinations of DT (immersive VR and 50-inch TV) and VP (first-person and third-person) to assess exertion (%HRmax, calories consumption, and Borg RPE 6-20), game experience, cybersickness, and EEG engagement index. Results: Study 1 results showed that DT had no effect on the ratings of the gestures, NASA-TLX workload, and EEG engagement index. Study 2 results showed that immersive VR not only resulted in a significantly higher exertion (%HRmax, calories consumption, and Borg RPE) but also helped achieve better positive game experience in challenge, flow, sensory and imaginative immersion, as well as lower negative affect. We also found that nausea and oculomotor were significantly higher in immersive VR. Conclusion: This pilot study demonstrates that youth who played gesture-based exergame in immersive VR had a higher level of exertion (%HRmax, calories consumption, and Borg RPE), although the number of performed gestures were not significantly different. They also felt that immersive VR was much more challenging, immersive (flow, sensory and imaginative immersion), and had a lower negative affect than a 50-inch TV; however, immersive VR was more likely to make youth have higher cybersickness.

Vision-aware target recognition toward autonomous robot by Kinect sensors

Target recognition is a key module in modern human–computer interaction (HCI) and computer vision systems It is pervasively used in many domains like autonomous vehicles and robot, remote operation, and video surveillance. However, due to the complicated environment and object occlusion, target recognition is still a challenging task. In this paper, we propose a novel target recognition algorithm toward autonomous robot by leveraging the Kinect sensors. More specifically, we utilize the Kinect sensors to capture scenario image in real-time. Subsequently, we present an improved HSV-based image segmentation algorithm to decompose the captured image, where morphological operation is employed for foreground target extraction. Afterward, we leverage Spatial Pyramid (SP)-based scheme for visual feature extraction. Then, we adopt a new distance metric for target matching. Comprehensive experimental results have shown the effectiveness of our proposed method.

Fusing pyramid histogram of gradients and optical flow for hand gesture recognition

Human computer interaction systems based on hand gestures catch the eye of the research community for implementing natural communication between man and machines. However, different persons perform the same gestures differently in terms of velocity and motion scale. This poses a challenging issue in minimising the variations between different persons and maximises the coherence of the same gestures. In this paper, the original pyramid histogram of gradients in three orthogonal planes combining with the dense optical flow to create dynamic descriptor is explored in to discriminate features for recognition of hand gestures. The shape and motion features of images in a video sequence are captured to obtain the geometric and illumination invariant spatio-temporal feature descriptor for classification. A multiclass support vector machine classifier is used to recognise the hand gestures. The proposed method gives an excellent recognition rate and excels the existing approaches.

Robust Head Pose Estimation Using Extreme Gradient Boosting Machine on Stacked Autoencoders Neural Network

Head pose estimation is an important sign in helping robots and other intelligence machines understand human. It plays a vital role in designing human computer interaction systems because many applications rely on precise results of head pose angles such as human behavior analysis, gaze estimation, 3D head reconstruction etc. This study presents a robust approach for estimating the head pose angles in a single image. More specifically, the proposed system first encodes the global features extracted from Histogram of Oriented Gradients in a multi stacked autoencoders neural network. Based on the hidden nodes in deep layers, Autoencoder has been proposed for feature reduction while maintaining the key information of data. A scalable gradient boosting machine is then employed to train and classify the embedded features. Experiences have evaluated on the Pointing 04 dataset and show that the proposed approach outperforms the state-of-the-art methods with the low head pose angle errors in pitch and yaw as 6.16° and 7.17°, respectively.

Robust human gesture recognition by leveraging multi-scale feature fusion

In the state-of-the-art human–computer interaction (HCI) systems, gestures feature is more intuitive, natural and, easy-to-acquire than the other visual features. Due to the high descriptiveness of hand movements and the scarcity of labeled human gesture samples, gesture recognition models is prone to overfitting in practice. In order to handle this problem, to facilitate the model parameter learning during training, this paper introduces a disturbing IoU strategy to alleviate overfitting during the training stage from a ROI discriminative network. Moreover, during the deep gesture feature extraction, feature maps with the same size output by different layers are intelligently fused. This strategy can effectively preserve the key information in a small scale, reduce the information loss, and improve the generalization ability of the high-level gesture features. Comprehensive experimental results on the extended VIVA dataset and the NTU dataset have shown that the proposed model achieves a better mAP performance than its competitors

Promotion of prehospital emergency care through clinical decision support systems: opportunities and challenges.

Clinical decision support systems are interactive computer systems for situational decision making and can improve decision efficiency and safety of care. We investigated the role of these systems in enhancing prehospital care. This narrative review included full-text articles published since 2000 that were available in databases/e-journals including Web of Science, PubMed, Science Direct, and Google Scholar. Search keywords included “clinical decision support system,” “decision support system,” “decision support tools,” “prehospital care,” and “emergency medical services.” Non-journal articles were excluded. We revealed 14 relevant studies that used such a support system in prehospital emergency medical service. Owing to the dynamic nature of emergency situations, decision timing is critical. Four key factors demonstrated the ability of clinical decision support systems to improve decision-making, reduce errors, and improve the safety of prehospital emergency activity: computer-based, offer support as a natural part of the workflow, provide decision support in the time and place of decision making, and offer practical advice. The use of clinical decision support systems in prehospital care resulted in accurate diagnoses, improved patient triage and patient outcomes, and reduction of prehospital time. By improving emergency management and rescue operations, the quality of prehospital care will be enhanced.

Persuasive Technology in The Islamic Perspective: The Principles and Strategies

The employment of persuasive technology in education, computing, sales, health, and environment is dramatically increasing. Persuasive technology is powerful in changing the attitudes and behaviours of end users. This paper begins by presenting the ethics of persuasive technology which are relevant to Islamic values and beliefs, and how the concept of persuasion had been applied in Islam practices to influence people. It explores how persuasive technology and its design factors presented in FBM are related to the Islamic practices proven in the Quran and Hadith. Additionally, this paper discusses persuasive technology strategy tools and their activities from Islamic prospective. The paper also examines in depth how Islamic concepts improve the perception of persuasive technology as an interactive computing system which is able to modify attitudes and behaviours. Essentially, this paper also demonstrates how practices and principles of the design factors and strategy tools of persuasive technology have been identified and utilized in early Islamic age. Those principles and strategies are further analyzed from Quran verses and Hadith that are of particular relevance. The conceptual results claim that Islamic principles are a contemporary and universal religion that takes care of the persuasive technology aspects and view of the critically of persuasive technology to Muslim society

Time is money: Dynamic-model-based time series data-mining for correlation analysis of commodity sales

The correlation analysis of commodity sales is very important in cross-marketing. A means of undertaking dynamic-model-based time series data-mining was proposed to analyze the sales correlations among different commodities. A dynamic model comprises some distance models in different observation windows for a time series database that is transformed from a commodities transaction database. There are sales correlations in two time series at different times, and this may produce valuable rules and knowledge for those who wish to practice cross-marketing and earn greater profits. It means that observation time points denoting the time at which the sales correlation occurs constitute important information. The dynamic model that leverages the techniques inherent in time series data-mining can uncover the kinds of commodities that have similar sales trends and how those sales trends change within a particular time period, which indicates that the “right” commodities can be commended to the “right” customers at the “right” time. Moreover, some of the time periods used to pinpoint similar sales patterns can be used to retrieve much more valuable information, which can in turn be used to increase the sales of the correlated commodities and improve market share and profits. Analysis results of retail commodities datasets indicate that the proposed method takes into consideration the time factor, and can uncover interesting sales patterns by which to improve cross-marketing quality. Moreover, the algorithm can be regarded as an intelligent component of the recommendation and marketing systems so that human–computer interaction system can make intelligent decision.

Design and development of an EOG-based simplified Chinese eye-writing system

Eye movement information provides an alternative way for patients with muscular and neurological disorders to communicate with other people or devices. Electrooculography (EOG) is an effective eye movement recording method that has been widely applied to design human–computer interaction systems. In this paper, we propose a simplified Chinese eye-writing system based on EOG (EsCew) by identifying the basic strokes of Chinese characters. Specifically, we first use a bandpass digital filter to preprocess the raw EOG signals to suppress noise interference. Then, we determine the effective eye movement segments according to different strokes by detecting blink signals using the sliding window technique. On this basis, we establish basic stroke templates in terms of the handwriting characteristics of Chinese characters. To reduce the computational complexity, the DTW algorithm is adopted to classify the EOG segments. Finally, we match the stroke sequence with the encoded Chinese characters to obtain the final recognition results. In the lab environment, the recognition experiments are performed on 10 most representative Chinese characters, i.e., ▪. The average accuracies of the basic strokes and Chinese characters are 93.998% and 94.52%, respectively. The experimental results validate the feasibility of the proposed EsCew system. [Display omitted]

Research on acquisition and recognition of Naxi speaker's speech information

The human–machine dialogue system allows people and the machine to communicate basically. This technology is also attracting more and more attention. It is conducive to the realisation of resource sharing, inheritance, and protection of Dongba culture worldwide to analyse the Naxi speech recognition for the protection and inheritance needs of Naxi Dongba culture. The model was established for Naxi speaker recognition, and the method is studied with text-independent speaker speech voice recognition. The practical analysis method was used to compare the application effect of Gaussian mixture model (GMM) and hidden Markov model (HMM) in the processing of speaker recognition in Naxi speaker's pronunciation. It is very useful to provide a theoretical basis for the establishment of a Naxi language-based human–computer interaction system to find out the speaker's voice characteristics, personality differences, and different regions. This is a theoretically necessary analysis of ethnic minority languages and the protection of ethnic and folk cultures. It can also provide practical significance for carrying out ethnic cultural differences analysis.

Design of computer interactive system for sports training based on artificial intelligence and improved support vector

Design of computer interactive system for sports training based on artificial intelligence and improved support vector

Designing for the Mindbody in Technology-Mediated Music-Making

The mind and body have an important joint role to play in how we experience, understand and create music. Our bodily experiences in the world shape how we comprehend music. In turn, our mind's music cognition and intentionality, which are based on our bodily experiences, are mediated through the body when we make music. This mind-body interaction has provided important contributions to the intelligibility, relatability and embodiment of our musical experiences, yet this dynamic is not always intact in today's technology-mediated music-making. Employing practice-based and practice-led methodologies, my research explores the potential of considering the mindbody in the design of interactive computer systems for music-making. This paper outlines the motivation, context, research methodology, and completed and future projects of my research towards a Doctor of Philosophy in Music with a specialization in Computational Media Design.