Human-computer Interaction Techniques Research Articles

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

Assessment of Requirements Engineering and Human-Computer Interaction Techniques in the Production Process of Business Intelligence Applications

Assessment of Requirements Engineering and Human-Computer Interaction Techniques in the Production Process of Business Intelligence Applications

On the Adoption of Requirements Engineering and Human-Computer Interaction Techniques to Produce Business Intelligence Applications

On the Adoption of Requirements Engineering and Human-Computer Interaction Techniques to Produce Business Intelligence Applications

On the Adoption of Requirements Engineering and Human-Computer Interaction Techniques to Produce Business Intelligence Applications

On the Adoption of Requirements Engineering and Human-Computer Interaction Techniques to Produce Business Intelligence Applications

On the Adoption of Requirements Engineering and Human-Computer Interaction Techniques to Produce Business Intelligence Applications

On the Adoption of Requirements Engineering and Human-Computer Interaction Techniques to Produce Business Intelligence Applications

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.

Rumor surveillance methods in outbreaks: A systematic literature review

Background: The spreading of health-related rumors can profoundly put society at risk, and the investigation of strategies and methods can efficiently prevent the dissemination of hazardous rumor is necessary, especially during a public health emergency including disease outbreaks. In this article we review the studies that implicated the surveillance system in identifying rumors and discuss the different aspects of current methods in this field. Methods: We searched PubMed, EMBASE, Scopus, and Web of Science databases for relevant publications in English from 2000 to 2020. The PICOS approach was used to select articles, and two reviewers extracted the data. Findings were categorized as a source of rumors, type of systems, data collection, and data transmission methods. The quality of the articles was assessed using the Mixed Method Appraisal Tool (MMAT) checklist. Results: Five studies that presented the methods used for rumor detection in different outbreaks were included in the critical appraisal process. Findings were grouped into four categories: source of rumors, type of systems, data collection, and data transmission methods. The source of rumors in most studies was media, including new social and traditional media. The most used data collection methods were human-computer interaction technique, and automatic and manual methods each were discussed in one study. Also, the data transmission method was asynchronous in the majority of studies. Conclusion: Based on our findings, the most common rumor detection systems used in the outbreaks were manual and/or human-computer methods which are considered to be time-consuming processes. Due to the ever-increasing amount of modern social media platforms and the fast-spreading of misinformation in the times of outbreaks, developing the automatically and real-time tools for rumor detection is a vital need.

An overview on human-computer interaction

Human-Computer Interaction (HCI) is a multidisciplinary area of research that focuses on computer technology design and, in particular, the interaction between people (users) and computers. The use of computers had always raised the issue of interfacing. The methods by which humans interact with computers have progressed significantly. HCI began with computers and has now grown to include virtually all aspects of information technology design. This paper's goal is to provide a broad overview of the field of Human-Computer Interaction. The fundamental concepts and terminology are covered, as well as a review of current technologies and recent developments in the area, typical architectures used in the design of HCI systems, including unimodal and multimodal configurations, and HCI applications. In addition, this paper provides a complete list of references for each HCI idea, technique, and application.

Towards human-computer interaction on smart metering systems

New technologies associated with the fourth industrial revolution are transforming the world in which we live, and the power grid is no exception since it has been provided with intelligence. One of its best-known applications is smart metering systems that allow real-time energy consumption/production to be known, as well as other benefits such as outages and reconnections automatically. The new generations of smart meters have more computing capacity allowing new applications. This work shows some considerations in the design of smart meters using human-computer interaction techniques. The results aim to improve the end-user’s experience and satisfaction and can help to mitigate the reluctance to use smart metering systems in Mexico.

Development and Acceptability of a Tablet-Based App to Support Men to Link to HIV Care: Mixed Methods Approach.

BackgroundThe poor engagement of men with HIV care is attributed to a number of factors: fear of stigma, masculine representations, concerns related to confidentiality, and the time commitment needed to visit public health clinics. Digital technologies are emerging as an approach to support the engagement of men with care.ObjectiveThis study aims to deliver a usable and engaging tablet-based app, called EPIC-HIV 2 (Empowering People through Informed Choices for HIV 2), to support men in making informed decisions about engaging with HIV care in rural KwaZulu Natal, South Africa.MethodsWe employed a mixed methods, iterative, and three-phased design that was guided by self-determination theory (SDT), a person-based approach, and human-computer interaction techniques. We reviewed related literature and conducted secondary analyses of existing data to identify barriers and facilitators to linkage to care and inform content development and design principles and used focus group discussions with members of the community advisory board and general community to evaluate a PowerPoint prototype of the app; used observations and guided questions with a convenience sample of potential users from the intervention community to iteratively test and refine a functioning interactive app; and conducted qualitative interviews and satisfaction surveys with actual users to evaluate acceptability.ResultsPhase 1 identified supply- and demand-side barriers to linkage to care. Specifically, clinics were feminized spaces unattractive to men with high social costs of attendance. Men did not feel vulnerable to HIV, preferred traditional medicine, and were afraid of the consequences of being HIV positive. Thus, the app needed to allow men to identify the long-term health benefits to themselves and their families of starting antiretroviral therapy early and remaining on it, and these benefits typically outweigh the social costs of attending and being seen at a clinic. SDT led to content design that emphasized long-term benefits but at the same time supported the need for autonomy, competence, and relatedness and informed decision making. Phase 2 indicated that we needed to use simpler text and more images to help users understand and navigate the app. Phase 3 indicated that the app was acceptable and likely to encourage men to link to care.ConclusionsWe found that iteratively developing the app with potential users using local narratives ensured that EPIC-HIV 2 is usable, engaging, and acceptable. Although the app encouraged men to link to HIV care, it was insufficient as a stand-alone intervention for men in our sample to exercise their full autonomy to link to HIV care without other factors such as it being convenient to initiate treatment, individual experiences of HIV, and support. Combining tailored digital interventions with other interventions to address a range of barriers to HIV care, especially supply-side barriers, should be considered in the future to close the present linkage gap in the HIV treatment cascade.

Measuring recognition of body changes over time: A human-computer interaction tool using dynamic morphing and body ownership illusion.

Measuring body image is crucial at both personal and social levels. Previous studies have attempted to quantitatively measure body image but methods for measuring body change recognition over time have not yet been established. The present study proposes a novel human-computer interaction technique using dynamic morphing and body ownership illusion, and we conducted a user study to investigate how body ownership illusion and gender would affect to body change recognition. The results showed that a participant’s body change recognition was weak when the body ownership illusion was strong. In addition, female participants were less sensitive than male participants. With our proposed technique, we demonstrated that we were able to quantitatively measure body change recognition and our empirical data indicated that body change recognition varied depending on body ownership illusion and gender, suggesting that our methodology could not only be used in future body image studies but also in eating disorder treatments.

Recognition of three different imagined movement of the right foot based on functional near-infrared spectroscopy

Brain-computer interface (BCI) based on functional near-infrared spectroscopy (fNIRS) is a new-type human-computer interaction technique. To explore the separability of fNIRS signals in different motor imageries on the single limb, the study measured the fNIRS signals of 15 subjects (amateur football fans) during three different motor imageries of the right foot (passing, stopping and shooting). And the correlation coefficient of the HbO signal during different motor imageries was extracted as features for the input of a three-classification model based on support vector machines. The results found that the classification accuracy of the three motor imageries of the right foot was 78.89%±6.161%. The classification accuracy of the two-classification of motor imageries of the right foot, that is, passing and stopping, passing and shooting, and stopping and shooting was 85.17%±4.768%, 82.33%±6.011%, and 89.33%±6.713%, respectively. The results demonstrate that the fNIRS of different motor imageries of the single limb is separable, which is expected to add new control commands to fNIRS-BCI and also provide a new option for rehabilitation training and control peripherals for unilateral stroke patients. Besides, the study also confirms that the correlation coefficient can be used as an effective feature to classify different motor imageries.

Data-Driven Personas for Enhanced User Understanding: Combining Empathy with Rationality for Better Insights to Analytics

Persona is a common human-computer interaction technique for increasing stakeholders’ understanding of audiences, customers, or users. Applied in many domains, such as e-commerce, health, marketing, software development, and system design, personas have remained relatively unchanged for several decades. However, with the increasing popularity of digital user data and data science algorithms, there are new opportunities to progressively shift personas from general representations of user segments to precise interactive tools for decision-making. In this vision, the persona profile functions as an interface to a fully functional analytics system. With this research, we conceptually investigate how data-driven personas can be leveraged as analytics tools for understanding users. We present a conceptual framework consisting of (a) persona benefits, (b) analytics benefits, and (c) decision-making outcomes. We apply this framework for an analysis of digital marketing use cases to demonstrate how data-driven personas can be leveraged in practical situations. We then present a functional overview of an actual data-driven persona system that relies on the concept of data aggregation in which the fundamental question defines the unit of analysis for decision-making. The system provides several functionalities for stakeholders within organizations to address this question.

Software Implementation Analysis of Fingerprint Identification System

With the fast development of E-time, the scope of Human-Computer Interaction Techniques’ application has become broader and broader. At the same time, the information security and identity verification have drawn increasing attention. Fingerprint recognition technology, which has played an important role in finance, internet, medicine and security fields, develops first, and is relatively more mature in these three. This paper mainly discusses the software part of the fingerprint recognition system. The extract of fingerprint’s image is directly from existing fingerprint database. Then a series of preprocessing, such as the grid, segment, smooth and converge of images, and so on, will be done. By doing these, image features could be extracted, so as to match the images of fingerprint. This paper using an implementation method, based on VC6.0 environment, in the sections of image’s preprocessing and feature matching, which contains 2 easy-operation function modules.

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.

DARPA's Explainable Artificial Intelligence Program

Dramatic success in machine learning has led to a new wave of AI applications (for example, transportation, security, medicine, finance, defense) that offer tremendous benefits but cannot explain their decisions and actions to human users. DARPA's explainable artificial intelligence (XAI) program endeavors to create AI systems whose learned models and decisions can be understood and appropriately trusted by end users. Realizing this goal requires methods for learning more explainable models, designing effective explanation interfaces, and understanding the psychologic requirements for effective explanations. The XAI developer teams are addressing the first two challenges by creating ML techniques and developing principles, strategies, and human‐computer interaction techniques for generating effective explanations. Another XAI team is addressing the third challenge by summarizing, extending, and applying psychologic theories of explanation to help the XAI evaluator define a suitable evaluation framework, which the developer teams will use to test their systems. The XAI teams completed the first of this 4‐year program in May 2018. In a series of ongoing evaluations, the developer teams are assessing how well their XAM systems' explanations improve user understanding, user trust, and user task performance.

Human-computer interaction and artificial intelligence: from competition to integration

Human-computer interaction and artificial intelligence (AI) are the two most important information technologies in the era of intelligent information. Through the developing histories of human computer interaction and AI, it was found that the relation between human-computer interaction and AI has changed from a competitive (in the past) to mutual promotion (in the present) state. Some intelligent user interface combining both human-computer interaction and AI techniques has gradually become commercial application from the research perspective, such as natural gesture interaction, emotional computing, and voice dialog system. This paper reviews the development process of human-computer interaction and summarizes its relation with AI. Human-computer interaction provides research ideas for AI; moreover, human intelligence drives the development of human-computer interaction and human-machine interaction. In the future, we anticipate that the present mutual promotion and driving relation existing between AI and human-machine interaction will continue, and will even increase.

Augmented Reality Based Educational Design for Children

Human-computer interaction (HCI) has developed rapidly in recent years, and more and more researchers are interested in applying HCI techniques into education. Compared with traditional approaches in the real world, gesture recognition is considered as a reasonable alternative since it is vivid and flexible. However, most of educational equipment nowadays achieves the function of augmented reality, without any interaction. This paper implemented a prototype, not only based on augmented reality system, but also especially we think about the interactive design. Accessibility is achieved by mobile devices and the dynamic switch of gesture recognition. By this interactive method, children are able to interact with the virtual objects easily and naturally. Consequently, children can have a profound and deep understanding of what they learn, and the quality of education will be improved.

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.