human computer interface
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Abstract We investigate the interaction between a human and a miniature autonomous blimp using a wand as pointing device. The wand movement generated by the human is followed by the blimp through a tracking controller.The Vector Integration to Endpoint (VITE) model, previously applied to human-computer interface (HCI), has been applied to model the human generated wand movement when interacting with the blimp. We show that the closed-loop human-blimp dynamics are exponentially stable. Similar to HCI using computer mouse, overshoot motion of the blimp has been observed. The VITE model can be viewed as a special reset controller used by the human to generate wand movements that effectively reduce the overshoot of blimp motion. Moreover, we have observed undershoot motion of the blimp due to its inertia, which does not appear in HCI using computer mouse. The asymptotic stability of the human-blimp dynamics is beneficial towards tolerating the undershoot motion of the blimp.

AbstractIntelligent agents (IAs) are permeating both business and society. However, interacting with IAs poses challenges moving beyond technological limitations towards the human-computer interface. Thus, the knowledgebase related to interaction with IAs has grown exponentially but remains segregated and impedes the advancement of the field. Therefore, we conduct a systematic literature review to integrate empirical knowledge on user interaction with IAs. This is the first paper to examine 107 Information Systems and Human-Computer Interaction papers and identified 389 relationships between design elements and user acceptance of IAs. Along the independent and dependent variables of these relationships, we span a research space model encompassing empirical research on designing for IA user acceptance. Further we contribute to theory, by presenting a research agenda along the dimensions of the research space, which shall be useful to both researchers and practitioners. This complements the past and present knowledge on designing for IA user acceptance with potential pathways into the future of IAs.

With advances in HCI and AI, and increasing prevalence of commercial social robots and chatbots, humans are communicating with computer interfaces for various applications in a wide range of settings. Kissenger is designed to bring HCI to the populist masses. In order to investigate the role of robotic kissing using the Kissenger device in HCI, the authors conducted a modified version of the imitation game described by Alan Turing by including the use of the kissing machine. Results show that robotic kissing has no effect on the winning rates of the male and female players during human-human communication, but it increases the winning rate of the female player when a chatbot is involved in the game.

Human Computer Interface (HCI) requires proper coordination and definition of features that serve as input to the system. The parameters of a saccadic and smooth eye movement tracking are observed and a comparison is drawn for HCI. This methodology is further incorporated with Pupil, OpenCV and Microsoft Visual Studio for image processing to identify the position of the pupil and observe the pupil movement direction in real-time. Once the direction is identified, it is possible to determine the accurate cruise position which moves towards the target. To quantify the differences between the step-change tracking of saccadic eye movement and incremental tracking of smooth eye movement, the test was conducted on two users. With the help of incremental tracking of smooth eye movement, an accuracy of 90% is achieved. It is found that the incremental tracking requires an average time of 7.21s while the time for step change tracking is just 2.82s. Based on the observations, it is determined that, when compared to the saccadic eye movement tracking, the smooth eye movement tracking is over four times more accurate. Therefore, the smooth eye tracking was found to be more accurate, precise, reliable, and predictable to use with the mouse cursor than the saccadic eye movement tracking.

Human-computer interfaces (HCI) allow people to control electronic devices, such as computers, mouses, wheelchairs, and keyboards, by bypassing the biochannel without using motor nervous system signals. These signals permit communication between people and electronic-controllable devices. This communication is due to HCI, which facilitates lives of paralyzed patients who do not have any problems with their cognitive functioning. The major plan of this study is to test out the feasibility of nine states of HCI by using modern techniques to overcome the problem faced by the paralyzed. Analog Digital Instrument T26 with a five-electrode system was used in this method. Voluntarily twenty subjects participated in this study. The extracted signals were preprocessed by applying notch filter with a range of 50 Hz to remove the external interferences; the features were extracted by applying convolution theorem. Afterwards, extracted features were classified using Elman and distributed time delay neural network. Average classification accuracy with 90.82% and 90.56% was achieved using two network models. The accuracy of the classifier was analyzed by single-trial analysis and performances of the classifier were observed using bit transfer rate (BTR) for twenty subjects to check the feasibility of designing the HCI. The achieved results showed that the ERNN model has a greater potential to classify, identify, and recognize the EOG signal compared with distributed time delay network for most of the subjects. The control signal generated by classifiers was applied as control signals to navigate the assistive devices such as mouse, keyboard, and wheelchair activities for disabled people.

In this context, the uses of computers, the Human-computer interface (HCI) system, can assist interaction on-demand services. HCI method facilitates ventilated patients to interact with computers about their needs using their brain’s electrical activity. To accomplish this, an HCI framework is developed in this research to facilitate visual feedback system (VFS) using an augmentative communication approach. Augmentative communication (AC.) or icon-based services are incorporated with a portable monitor placed in front of a patient; they can look at the screen to select (ask) their appropriate needs-related icons. The services have been achieved by capturing and processing patients’ electromagnetic brain activates during the icon selection by their eye flickering moment recording using wearable Electroencephalogram (EEG). The flickering icons on the screen conveying an appropriate message to the monitoring unit computer, and the monitoring unit can respond to the patient’s request using VFS. The HCI system is comprised of the following methodologies to achieve augmentative communication-based services such as EEG signals acquisition, filtering, partition-based feature extraction, and fusion and fish swarm optimized Deep Hopfield neural network FSODHNN based classifier. The evolution results of the VSF based HCI framework are demonstrated successfully. It obtained the highest accuracy of 99.11%, specificity of 99.05%, the sensitivity of 99.09%, and the lowest RMSE of 0.98, MSE of 0.92 in icon identification/selection.

In the past few decades, wearable sensors and devices have evolved into central technologies that have significantly impacted next-generation healthcare solutions in the previous decade. It is an age of incredibly cut-throat competition, even the youth. This technology is Hand-free, eyephone-operated telephone technology. It measures where the user’s eyes are located on the phone’s display employing a camera attached to the device. The typical work the eye bones assigned to eye tracking, eye blinking, detecting, etc., by way of eye-tracking, users can take care of their email, calendar, phone, etc. The leading technologies in use are a Human-Computer Interface (HCI) and a Human Phone Interface (HPI). It helps those with disabilities greatly. The IT Index is an expanded form of HCI. The use of mobile devices like smartphones and tablets is on the rise, and, to some extent, it could displace the use of desktops and laptops. Human-computer interaction is interested in the interaction between people and the computer system and between software and applications. Our focus is on this revolutionary new type of technology called the eye-phone.

All human activity is associated with the generation of electrical signals. These signals are collectively referred to as electrical physiology (EP) signals (e.g., electrocardiogram, electroencephalogram, electromyography, electrooculography, etc.), which can be recorded by electrodes. EP electrodes are not only widely used in the study of primary diseases and clinical practice, but also have potential applications in wearable electronics, human–computer interface, and intelligent robots. Various technologies are required to achieve such goals. Among these technologies, adhesion and stretchable electrode technology is a key component for the rapid development of high-performance sensors. In recent years, remarkable efforts have been made in the development of flexible and high-adhesive EP recording systems and preparation technologies. Regarding these advancements, this review outlines the design strategies and related materials for flexible and adhesive EP electrodes, and briefly summarizes their related manufacturing techniques.