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Advances in Human-Computer Interaction is an interdisciplinary journal that publishes theoretical and applied papers covering the broad spectrum of interactive systems.
Advances in Human-Computer Interaction maintains an Editorial Board of practicing researchers from around the world, to ensure manuscripts are handled by editors who are experts in the field of study.
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Controlling an Anatomical Robot Hand Using the Brain-Computer Interface Based on Motor Imagery
More than one billion people face disabilities worldwide, according to the World Health Organization (WHO). In Sri Lanka, there are thousands of people suffering from a variety of disabilities, especially hand disabilities, due to the civil war in the country. The Ministry of Health of Sri Lanka reports that by 2025, the number of people with disabilities in Sri Lanka will grow by 24.2%. In the field of robotics, new technologies for handicapped people are now being built to make their lives simple and effective. The aim of this research is to develop a 3-finger anatomical robot hand model for handicapped people and control (flexion and extension) the robot hand using motor imagery. Eight EEG electrodes were used to extract EEG signals from the primary motor cortex. Data collection and testing were performed for a period of 42 s timespan. According to the test results, eight EEG electrodes were sufficient to acquire the motor imagery for flexion and extension of finger movements. The overall accuracy of the experiments was found at 89.34% (mean = 22.32) at the 0.894 precision. We also observed that the proposed design provided promising results for the performance of the task (grab, hold, and release activities) of hand-disabled persons.
Edge Artificial Intelligence: Real-Time Noninvasive Technique for Vital Signs of Myocardial Infarction Recognition Using Jetson Nano
The history of medicine shows that myocardial infarction is one of the significant causes of death in humans. The rapid evolution in autonomous technologies, the rise of computer vision, and edge computing offers intriguing possibilities in healthcare monitoring systems. The major motivation of the work is to improve the survival rate during a cardiac arrest through an automatic emergency recognition system under ambient intelligence. We present a novel approach to chest pain and fall posture-based vital sign detection using an intelligence surveillance camera to address the emergency during myocardial infarction. A real-time embedded solution persuaded from “edge AI” is implemented using the state-of-the-art convolution neural networks: single shot detector Inception V2, single shot detector MobileNet V2, and Internet of Things embedded GPU platform NVIDIA’s Jetson Nano. The deep learning algorithm is implemented for 3000 indoor color image datasets: Nanyang Technological University Red Blue Green and Depth, NTU RGB + D dataset, and private RMS dataset. The research mainly pivots on two key factors in creating and training a CNN model to detect the vital signs and evaluate its performance metrics. We propose a model, which is cost-effective and consumes low power for onboard detection of vital signs of myocardial infarction and evaluated the metrics to achieve a mean average precision of 76.4% and an average recall of 80%.
A Perspective View of Cotton Leaf Image Classification Using Machine Learning Algorithms Using WEKA
Cotton is one of the major crops in India, where 23% of cotton gets exported to other countries. The cotton yield depends on crop growth, and it gets affected by diseases. In this paper, cotton disease classification is performed using different machine learning algorithms. For this research, the cotton leaf image database was used to segment the images from the natural background using modified factorization-based active contour method. First, the color and texture features are extracted from segmented images. Later, it has to be fed to the machine learning algorithms such as multilayer perceptron, support vector machine, Naïve Bayes, Random Forest, AdaBoost, and K-nearest neighbor. Four color features and eight texture features were extracted, and experimentation was done using three cases: (1) only color features, (2) only texture features, and (3) both color and texture features. The performance of classifiers was better when color features are extracted compared to texture feature extraction. The color features are enough to classify the healthy and unhealthy cotton leaf images. The performance of the classifiers was evaluated using performance parameters such as precision, recall, F-measure, and Matthews correlation coefficient. The accuracies of classifiers such as support vector machine, Naïve Bayes, Random Forest, AdaBoost, and K-nearest neighbor are 93.38%, 90.91%, 95.86%, 92.56%, and 94.21%, respectively, whereas that of the multilayer perceptron classifier is 96.69%.
Analyzing the Synergy between HCI and TRIZ in Product Innovation through a Systematic Review of the Literature
The boundary between tangible and digital products is getting more fused while rapidly evolving systems for interaction require novel processes that allow for rapidly developed designs, evaluations, and interaction strategies to facilitate efficient and unique user interactions with computer systems. Accordingly, the literature suggests combining creativity enhancement tools or methods with human-computer interaction (HCI) design. The TRIZ base of knowledge appears to be one of the viable options, as shown in the fragmental indications reported in well-acknowledged design textbooks. The goal of this paper is to present a systematic review of the literature to identify and analyze the published approaches and recommendations to support the synergy between HCI and TRIZ from the perspective of product innovation related to HCI, with the aim of providing a first comprehensive classification and discussing about observable differences and gaps. The method followed is the guidelines related to systematic literature review methods. As results, out of 444 initial results, only 17 studies reported the outcomes of the synergy between HCI and TRIZ. The 7 of these studies explored the feasibility of the combination of HCI and TRIZ. The 10 studies attempted to combine and derive approaches in these two fields, and the outcomes defined 3 different integration strategies between HCI and TRIZ. Some conclusions achieved are that the generic solutions to support the synergy between HCI and TRIZ are still rare in the literature. The extraction and combination of different tools caused the randomization of the evaluation criteria, and the performance of the proposals has not been comprehensively evaluated. However, the findings can help inform future developments and provide valuable information about the benefits and drawbacks of different approaches.
Examining Serendipitous Encounters and Self-Determination in Twitter-Enabled Innovation
Serendipity refers to unexpected encounters with ideas or insights and their intentional application to achieve favorable outcomes. Despite extensive prior studies, the concept lacks theoretical logic and empirical validation regarding the role of an intentional act in the relationship between serendipitous encounters and their favorable outcomes. Drawing from self-determination theory, we develop a model that highlights the role of needs satisfaction in explaining this relationship. Positioning the empirical context to fortunate discoveries of information and social connections in professional use of Twitter, we validate the model by a cross-sectional survey study of 473 users. The model builds on the observation that individuals’ serendipitous encounters are associated with Twitter-enabled innovation, that is, a contextualized form of task innovation. The study findings support the research model revealing that serendipitous encounters are positively associated with needs satisfaction and that needs satisfaction is positively associated with Twitter-enabled innovation. In other words, fortunate discoveries of new information and contacts increase Twitter users’ intent to utilize the platform in new ways to accomplish work when the three key psychological needs of autonomy, competence, and relatedness are satisfied.
Development and Alpha Testing of EzHifz Application: Al-Quran Memorization Tool
Learning to memorize the Quran presents a challenge. This paper reports the development and alpha testing of a mobile application called “EzHifz” for Quran memorization based on the VARK learning style. The application received positive feedback for user acceptance testing and heuristic testing. The Fleiss kappa coefficient (κ) results for user acceptance testing show a very good level of agreement (κ = 0.850). Heuristic testing results show that κ = 0.731 for content, manual guide, memorization activities, performance information, and tasmik assessment attributes, while κ = 0.727 for presentation design, interactivity, multimedia elements, attraction, and motivation attributes. These results show a good level of agreement, which indicates that the EzHifz application meets the requirements of design and development based on the attributes evaluated. A combination of memorizing techniques in the application helps strengthen the use of preferred VARK learning styles. The techniques support the use of multiple senses that could facilitate the process of memorizing the Quran independently. This study contributes to the novel design and evaluation of the Quran memorization application based on the Quran memorization model. The application supports the teaching and learning of Quran memorization where it allows students to select their preferred VARK learning style with the technique of memorizing the Quran. This mobile application learning approach based on VARK’s learning style has the potential to be implemented in the process of memorizing the Quran as well as retaining memory through the use of memory senses in support of the learning materials developed.