Computer Interaction Techniques Research Articles

Intelligent System for Cognitive Study on Student Work Network Based on the Information of Public Opinion with the Background of IOT

Abstract Neurological, behavioral, and cognitive problems in teenage students are rising day by day. It is very important to handle these issues by gathering information from public opinion. The distribution of public opinion information in student work is unsystematic, and it is difficult to extract the needed information intelligently. An intelligent extraction algorithm of public opinion data from student work is proposed in this research and is based on human–computer interaction, machine learning, and computational techniques. In the student work, a fuzzy semantic autocorrelation mapping feature set of public opinion information is developed together with a spatial structural model of data semantic distribution features. The statistical feature quantity of semantic similarity of the public opinion data is retrieved from the student work, and the semantic ambiguity is decreased. Analyses using adaptive learning and machine understanding and human–computer interaction are used to process it. In the human–computer interaction machine understanding center, the processor adjusts the grid partition of public opinion information of the student work according to the difference of statistical features, then constructs the feature decomposition model of student behavior, and then performs context mapping. Finally, the semantic analysis is carried out to analyze the student behavior based on the cognitive study. The simulation outcomes demonstrate that the proposed method is computationally inexpensive, has low time complexity, and has real-time monitoring ability of public opinion information on student behavior.

Data Analytics and Visualization Application for Asset Health Monitoring

Much of the research on predictive maintenance has focused on statistical and machine learning techniques, while there has been significantly less focus on the human computer interaction or visualization aspects of PHM. Human computer interaction and visualization techniques can quickly help identify interesting data sub-domains, both time periods and sensors, provided the data can be queried, retrieved and displayed in a timely manner. Augmenting visualization and interaction with a visual, aggregative fleet-based query system adds a further dimension, highlighting the ability of the fleet to carry out its mission. In the age of big data, effectively visualizing data from an asset with a multitude of sensors can be difficult, and that difficulty is compounded as you add additional assets.
 In this paper, we propose a scalable framework that is capable of visualizing past, current, and prognosticated health from the individual sensor up to the fleet or group level. In addition to viewing near real time sensor data, maintenance logs, fault information, and data aggregations will be merged with the sensor data to make the analysis and visualizations more valuable. This framework is scalable in regard to how much data can be collected, stored, and processed, as well as to the different organizational levels within a fleet of assets. The framework is built as a web-application primarily using the following visualizations: a collapsible tree graph for asset information; 2D charts for temporal sensor data, fault data, and maintenance data; and 3D digital twins of critical components. 
 The current dataset used to build and demonstrate the capability of the web-application has data from over 3000 vehicles and contains approximately 9TB of data. Vehicle information such as model, make, sub-component, and fleet organization are presented in a configurable, collapsible tree structure. This allows the user to visualize the fleet and to select the asset and sensor combinations needed to display the temporal sensor data that will answer the question at hand. Information regarding each vehicle’s health status is displayed then aggregated and displayed for each of the higher tree nodes. A 3D digital twin is also available to highlight sensor locations and current health status of the component. These component models can be viewed and manipulated with and without a virtual reality headset. As health status monitoring for asset subcomponents are developed, they can be added to the system, allowing for complete health status reporting.

Toward Robust Device-Free Gesture Recognition Based on Intrinsic Spectrogram of mmWave Signals

Device-free gesture recognition is a potential noncontact human–computer interaction technique. It leverages the unique influence of the conducted gesture on surrounding wireless signals to accomplish gesture recognition. Existing methods usually leverage doppler spectrogram of the influenced wireless signals to characterize the motion pattern of gestures. These methods have achieved satisfactory accuracy when the gestures are conducted in a relatively fixed location, direction, and speed. However, when gestures are conducted in a different scenario, the recognition accuracy will drop dramatically. In this article, we try to solve this issue by characterizing the gesture motion pattern using a novel robust intrinsic spectrogram, which is independent of the conducted scenario. Specifically, we create a virtual coordinate system in which the coordinates of a gesture trajectory remain unchanged no matter where and how the gesture is conducted. Then, we design a coordinate transformation method to transform the raw doppler spectrogram into the robust intrinsic spectrogram to characterize the intrinsic motion pattern of the gesture. We further feed the intrinsic spectrogram into a deep network to realize gesture recognition. Extensive evaluations on a 77-GHz mmWave testbed show that the proposed method could achieve an average recognize accuracy of 88.4% with ten types of gestures.

A Systematic Review of Human–Computer Interaction (HCI) Research in Medical and Other Engineering Fields

This article provides a systematic review of research related to Human–Computer Interaction techniques supporting training and learning in various domains including medicine, healthcare, and engineering. The focus is on HCI techniques involving Extended Reality (XR) technology which encompasses Virtual Reality, Augmented Reality, and Mixed Reality. HCI-based research is assuming more importance with the rapid adoption of XR tools and techniques in various training and learning contexts including education. There are many challenges in the adoption of HCI approaches, which results in a need to have a comprehensive and systematic review of such HCI methods in various domains. This article addresses this need by providing a systematic literature review of a cross-section of HCI approaches involving proposed so far. The PRISMA-guided search strategy identified 1156 articles for abstract review. Irrelevant abstracts were discarded. The whole body of each article was reviewed for the remaining articles, and those that were not linked to the scope of our specific issue were also eliminated. Following the application of inclusion/exclusion criteria, 69 publications were chosen for review. This article has been divided into the following sections: Introduction; Research methodology; Literature review; Threats of validity; Future research and Conclusion. Detailed classifications (pertaining to HCI criteria and concepts, such as affordance; training, and learning techniques) have also been included based on different parameters based on the analysis of research techniques adopted by various investigators. The article concludes with a discussion of the key challenges for this HCI area along with future research directions. A review of the research outcomes from these publications underscores the potential for greater success when such HCI-based approaches are adopted during such 3D-based training interactions. Such a higher degree of success may be due to the emphasis on the design of user-friendly (and user-centric) training environments, interactions, and processes that positively impact the cognitive abilities of users and their respective learning/training experiences. We discovered data validating XR-HCI as an ascending method that brings a new paradigm by enhancing skills and safety while reducing costs and learning time through replies to three exploratory study questions. We believe that the findings of this study will aid academics in developing new research avenues that will assist XR-HCI applications to mature and become more widely adopted.

Data Inventories for the Modern Age? Using Data Science to Open Government Data

This article describes how data science techniques—machine learning and natural language processing—can be used to open the black box of government data. It then describes how an incentive structure can be established—using human–computer interaction techniques —to create a new and sustainable data ecosystem. The particular focus is on the United States and on scientific researchers, who are major users of government data. However, the approach can be deployed to other use cases, such as data mentions in newspapers and government reports, and many other countries

Virtual and Augmented Reality in the Disaster Management Technology: A Literature Review of the Past 11 years

This study presents a systematic review of the literature on virtual reality (VR), augmented reality (AR) and Mixed Reality (MR) used in disaster management. We consider the factors such as publication type, publication year, application domain, and technology used. We surveyed papers from 2009 to 2019 available in the Web of Science and Google Scholar database, and 84 research articles were selected for the review study. After an extensive review of the literature, it was found that the XR technology is applied extensively in computer simulation modeling, interaction techniques, training, infrastructure assessment and reconnaissance, and public awareness areas of disaster management. We found diverse advantages, opportunities, and challenges of XR usage for disaster management, which are discussed in detail. Furthermore, current research gaps in the field of XR technology for disaster management technology, which are needed to better support disaster management, are identified and discussed in an effort to provide direction to the future research.

FREE GUY- A Hand Gesture Game Based on ML

Human Computer Interaction techniques became a bottleneck within the effective utilization of the obtainable info flow. The development of user interfaces affects changes in the Human Computer Interaction (HCI). The naturalness and intuitiveness of the hand gesture has been a major motivating factor for researchers in the HCI field to invest their efforts in researching and developing the most promising means of Human-Computer Interaction. This paper will let you know about the creation of hand gesture recognition program and implementation of its techniques in the computer game by controlling the movements of character introduced. The machine learning part is accomplished by the libraries like Tensorflow and OpenCV for setting up the detection, recognition and image processing techniques.

The optimization algorithm for application in directional drilling trajectories of energy field

Recently, the directional drilling technology (DDT) has been extensive used in many energy field such as coal geological exploration, gas drainage and artificial freezing engineering etc. The theoretical model of directional drilling trajectories is insufficient leading to drilling trajectories control is almost uncertain. In the present study, a theoretical model of directional drilling trajectories is proposed, which is simplified as a combination of many alternants constant curvatures smoothing quasi-helical segments. The essential condition of nonlinear piecewise systems is proved by maximal principle. Multimodal objective function is figure out by optimization algorithm. The directional drilling trajectories software is programmed, and the optimum trajectory for directional drilling is designed. The maximum directional drilling deviation on the vertical direction is 0.38 m, and horizontal direction is 0.63 m. The optimization algorithm is more intuitive and reliable than repetition test and traditional human–computer interaction techniques.

Magic-hand: Turn a smartwatch into a mouse

We present the design, implementation and evaluation of a smartwatch-based, freehand human–computer interaction system, called Magic-Hand, which consists of two modules: the operation detection module and the motion tracking module. In the operation detection module, five gestures are selected to represent typical operations including right-click, left-click and double-click, and we carefully design the detection algorithm to identify gestures even when the hand is in motion. In the motion tracking module, we map the movement of the hand to the screen cursor movement with the Sigmoid function. We verify the performance of Magic-Hand by comparing it with two existing human–computer interaction techniques: the mouse and the AirMouse. The experimental results show that the gesture recognition in the operation detection module can reach an accuracy of 95%, and the motion tracking is comparable with the benchmarks. Our work facilitates the use of the smartwatch as an effective interaction device in ubiquitous display environments.

PENGGALIAN KEBUTUHAN MENGGUNAKAN METODE PERSONA PADA SISTEM PENILAIAN LMS

The development of a software is very necessary to understand the users/users of the system. The aspect of user convenience in using the system is a very important aspect so that the system that has been created is in accordance with the purpose of its manufacture and according to user needs. Persona is a technique of Human Computer Interaction by collecting information from users as a reference in understanding the needs of users. The purpose of this study is to apply the persona technique by adjusting the validation process to the user personas of our system users. The method used in this research is the Human Computer Interaction technique. User persona is used as a reference so that a system is made based on user needs so that the purpose of making the system can be met. The result of this research is to use the user persona technique in the elicitation of needs which will facilitate understanding related to the development of a system.Keywords: Human Computer Interaction, Persona, Elicitation of Needs

A CNN-LSTM based ensemble framework for in-air handwritten Assamese character recognition

In-air handwriting is a contemporary human computer interaction (HCI) technique which enables users to write and communicate in free space in a simple and intuitive manner. Air-written characters exhibit wide variations depending upon different writing styles of users and their speed of articulation, which presents a great challenge towards effective recognition of linguistic characters. So, in this paper we have proposed an ensemble model for in-air handwriting recognition which is based on convolutional neural network (CNN) and a long short-term memory neural network (LSTM-NN). The method collaborates overall character trajectory appearance modeling and temporal trajectory feature modeling for efficient recognition of varied types of air-written characters. In contrast to two-dimensional handwriting, in-air handwriting generally involves writing of characters interlinked by a continuous stroke, which makes segregation of intended writing activity from insignificant connecting motions an intricate task. So, a two-stage statistical framework is incorporated in the system for automatic detection and extraction of relevant writing segments from air-written characters. Identification of writing events from a continuous stream of air-written data is accomplished by formulating a Markov Random Field (MRF) model, while the segmentation of writing events into meaningful handwriting segments and redundant parts is performed by implementation of a Mahalanobis distance (MD) classifier. The proposed approach is assessed on an air-written character dataset comprising of Assamese vowels, consonants and numerals. The experimental results connote that our hybrid network can assimilate more information from the air-writing patterns and hence offer better recognition performance than the state-of-the-art approaches.

Evaluation Techniques for Chatbot Usability: A Systematic Mapping Study

Background: The use of chatbots has increased considerably in recent years. These are used in different areas and by a wide variety of users. Due to this fact, it is essential to incorporate usability in their development. Aim: Our objective is to identify the state-of-the-art in chatbot usability and applied human–computer interaction techniques, to analyze how to evaluate chatbot usability. Method: We have conducted a systematic mapping study, by searching the main scientific databases. The search retrieved 170 references and 21 articles were retained as primary studies. Results: The works were categorized according to four criteria: usability techniques, usability characteristics, research methods and type of chatbots. Conclusions: Chatbot usability is still a very incipient field of research where the published studies are mainly surveys, usability tests, and rather informal experimental studies. Hence, it becomes necessary to perform more formal experiments to measure user experience, and exploit these results to provide usability-aware design guidelines.

Design Tactile Interfaces with Vibration Patterns in HTML5 for Smartphone Users with Visual Impairments

This paper describes the procedure for creating tactile interfaces for Android smart phones. It uses the HTML5 Vibration API and the Javascript programming language to create vibration patterns in order to increase the interaction between visually impaired people with their smart phones. Apart from vision, audio and haptics are used to maximize the Human – Computer Interaction between the Android phones and users with visual impairments. Three methods are proposed for the creation of tactile interfaces in smart phones, one is with the use of the HTML image map tag, and the other with the Mask R-CNN Artificial Intelligence algorithm. The Mean Opinion Score procedure is used in order to evaluate the proposed vibration patterns for Human - Computer Interaction Techniques.

Design Tactile Interfaces with Vibration Patterns in HTML5 for Smartphone Users with Visual Impairments

This paper describes the procedure for creating tactile interfaces for Android smart phones. It uses the HTML5 Vibration API and the Javascript programming language to create vibration patterns in order to increase the interaction between visually impaired people with their smart phones. Apart from vision, audio and haptics are used to maximize the Human – Computer Interaction between the Android phones and users with visual impairments. Three methods are proposed for the creation of tactile interfaces in smart phones, one is with the use of the HTML image map tag, and the other with the Mask R-CNN Artificial Intelligence algorithm. The Mean Opinion Score procedure is used in order to evaluate the proposed vibration patterns for Human - Computer Interaction Techniques.

Implementation and early adaptation of patient-reported outcome measures into an electronic health record: A technical report.

We integrated and optimized patient-reported outcome measures into the electronic health record to provide quantitative, objective data regarding patients' health status, which is important for patient care, payer contracts, and research. With a multidisciplinary team from information technology, clinical informatics, population health, and physician champions, we used formal human-computer interaction techniques and user-centered design to integrate several technology platforms and computerized adaptive testing for the National Institutes of Health Patient-Reported Outcomes Measurement Information System. The patient-reported outcome measure system leverages software frequently used by health systems and provides data for research and clinical care via a mobile-responsive web application using Symfony, with REDCap for configuring assessments and de-identified data storage. The system incorporates Oracle databases and Epic flowsheets. Patients complete patient-reported outcome measures, with data viewable in MyChart and Epic Synopsis Reports. Researchers can access data portals. The highly usable, successful patient-reported outcome measures platform is acceptable to patients and clinicians and achieved 73 percent overall completion rates.

Tangible Interaction and Cultural Forms: Supporting Learning in Informal Environments

Designers who create computer-based learning experiences for places like museums, out-of-school programs, and homes face a number of challenges related to the informal nature of such settings. Designs must generally function on their own without the support of teachers or curriculum while at the same time engaging a diverse audience, supporting productive social interaction, and activating appropriate prior knowledge and skills. In this article, I present an approach to the design of informal learning experiences based on tangible interaction. The term tangible refers to a variety of human–computer interaction techniques that move beyond computer screens and create opportunities for people to interact with digital systems using their bodies and physical artifacts. I argue that tangible interaction creates unique opportunities for designers to shape objects and situations to evoke cultural forms of literacy, learning, and play. In particular, I propose a class of cultural forms called cueing forms that can invite participation into patterned social activity while cueing cognitive, physical, and emotional resources on the part of individuals. To illustrate these arguments, I describe 3 design cases that colleagues and I have created to support learning in museums and homes.

SOVA: Design of a Stakeholder Informed Social Media Website for Depressed Adolescents and Their Parents

ABSTRACTTwo moderated social media websites were designed for increasing likelihood for mental health treatment engagement for depressed adolescents (sova.pitt.edu) and for parents (wisesova.pitt.edu). This article describes iterative stakeholder interviews conducted with adolescents, young adults, parents, advocates, and clinicians and use of human computer interaction techniques to inform major design changes which: (a) underscored the role of online interaction to provide emotional support and information, (b) importance of anonymity, (c) desire to share positive media, and (d) need for frequent moderation. Future studies will examine acceptability and effectiveness of revised websites in helping depressed adolescents and their parents engage with treatment.

Retraction Note to: A survey on human–computer interaction technologies and techniques

Retraction Note to: A survey on human–computer interaction technologies and techniques

Development of an Efficient Hand Gesture Recognition system for human computer interaction

Human computer interaction technique has become a bottleneck in the effective utilization of the available information. The development of user interfaces influences the change in the human computer interaction (HCI). Dynamic hand posture recognition is one of the difficult tasks due to its complex background. The proposed method is implemented for hand postures taken in the natural environment. Challenges such as change of illumination and similar background are taken into consideration. Working system is based on two steps, namely hand detection and hand gesture recognition. In the hand detection process, normalized colour space skin locus is used to threshold the skin pixels from its varying background. Morphological filtering and canny edge detection is used efficaciously for removing object noise and obtain counter edges of hand gestures. Normalized cross correlation is used for pixel-wise comparison between the input image and the depository images and the best matching is considered as region of interest.

RETRACTED ARTICLE: A survey on human–computer interaction technologies and techniques

Considerable effort has been put toward the development of intelligent and natural interfaces between users and computer systems. In line with this endeavor, several modes of information (e.g., visual, audio, and pen) that are used either individually or in combination have been proposed. The use of gestures to convey information is an important part of human communication. Hand gesture recognition is widely used in many applications, such as in computer games, machinery control (e.g., crane), and thorough mouse replacement. Computer recognition of hand gestures may provide a natural computer interface that allows people to point at or to rotate a computer-aided design model by rotating their hands. Hand gestures can be classified into two categories: static and dynamic. The use of hand gestures as a natural interface serves as a motivating force for research on gesture taxonomy, its representations, and recognition techniques. This paper summarizes the surveys carried out in human–computer interaction (HCI) studies and focuses on different application domains that use hand gestures for efficient interaction. This exploratory survey aims to provide a progress report on static and dynamic hand gesture recognition (i.e., gesture taxonomies, representations, and recognition techniques) in HCI and to identify future directions on this topic.