Academic literature on the topic 'Attentiveness classification'
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Journal articles on the topic "Attentiveness classification"
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Peng, Chia-Ju, Yi-Chun Chen, Chun-Chuan Chen, Shih-Jui Chen, Barthélemy Cagneau, and Luc Chassagne. "An EEG-Based Attentiveness Recognition System Using Hilbert–Huang Transform and Support Vector Machine." Journal of Medical and Biological Engineering 40, no. 2 (November 26, 2019): 230–38. http://dx.doi.org/10.1007/s40846-019-00500-y.
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Abstract Purpose Attentiveness recognition benefits the detection of the mental state and concentration when humans perform specific tasks. Hilbert–Huang transform (HHT) is useful for the analysis of nonlinear or nonstationary bio-signals including brainwaves. In this work, a method is proposed for the characterization of attentiveness levels by using electroencephalogram (EEG) signals and HHT analysis. Methods Single-channel EEG signals from the frontal area were acquired from participants at different levels of attentiveness and were decomposed into a set of intrinsic mode functions (IMF) by empirical mode decomposition (EMD). Hilbert transform analysis was applied to each IMF to obtain the marginal frequency spectrum. Then the band powers and spectral entropies (SEs) were selected as the attributes of a support vector machine (SVM) for a two-class classification task. Results Compared with the predictive models of approximate entropy (ApEn) and fast Fourier transform (FFT), the results show that the band powers extracted from IMF2 to IMF5 of $$\alpha$$α and $$\beta$$β waves and their SE can best discriminate between attentive and relaxed states with the average classification accuracy of 84.80%. Conclusion In conclusion, this integrated signal processing method is capable of attentiveness recognition that can offer efficient differentiation and may be used in a clinical setting for the detection of attention deficit.
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Balconi, Michela, and Laura Angioletti. "Inter-Brain Hemodynamic Coherence Applied to Interoceptive Attentiveness in Hyperscanning: Why Social Framing Matters." Information 14, no. 2 (January 17, 2023): 58. http://dx.doi.org/10.3390/info14020058.
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Grossberg’s classification of adaptive resonance mechanisms includes the cognitive-emotional resonances that support conscious feelings and recognition of them. In this regard, a relevant question concerns the processing of signals deriving from the internal body and their contribution to interpersonal synchronization. This study aims to assess hemodynamic inter-subject coherence in the prefrontal cortex (PFC) through functional near-infrared spectroscopy (fNIRS) hyperscan recording during dyadic synchronization tasks proposed with or without a social frame and performed in two distinct interoceptive conditions: focus and no focus on the breathing condition. Individuals’ hemodynamic data (oxygenated and de-oxygenated hemoglobin (O2Hb and HHb, respectively)) were recorded through fNIRS hyperscanning, and coherence analysis was performed. The findings showed a significantly higher O2Hb coherence in the left PFC when the dyads performed the synchronization tasks with a social frame compared with no social frame in the focus condition. Overall, the evidence suggests that the interoceptive focus and the presence of a social frame favor the manifestation of a left PFC interpersonal tuning during synchronization tasks.
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Akhtar, Zain Ul Abiden, Hafiz Faiz Rasool, Muhammad Asif, Wali Ullah Khan, Zain ul Abidin Jaffri, and Md Sadek Ali. "Driver’s Face Pose Estimation Using Fine-Grained Wi-Fi Signals for Next-Generation Internet of Vehicles." Wireless Communications and Mobile Computing 2022 (May 5, 2022): 1–18. http://dx.doi.org/10.1155/2022/7353080.
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Driver’s behavior and gesture recognition are most significant in the emerging next-generation vehicular technology. Driver’s face may provide important cues about his/her attention and fatigue behavior. Therefore, driver’s face pose is one of the key indicators to be considered for automatic driver monitoring system in next-generation Internet of Vehicles (IoV) technology. Driver behavior monitoring is most significant in order to reduce road accidents. This paper aims to address the problem of driver’s attentiveness monitoring using face pose estimation in a nonintrusive manner. The proposed system is based on wireless sensing, leveraging channel state information (CSI) of WiFi signals. In this paper, we present a novel classification algorithm that is based on the combination of support vector machine (SVM) and K nearest neighbor (KNN) to enhance the classification accuracy. Experimental results demonstrate that the proposed device-free wireless implementation can localize a driver’s face very accurately with an average recognition rate of 91.8 % .
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Jeong, Dong-Hwa, and Jaeseung Jeong. "In-Ear EEG Based Attention State Classification Using Echo State Network." Brain Sciences 10, no. 6 (May 26, 2020): 321. http://dx.doi.org/10.3390/brainsci10060321.
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It is important to maintain attention when carrying out significant daily-life tasks that require high levels of safety and efficiency. Since degradation of attention can sometimes have dire consequences, various brain activity measurement devices such as electroencephalography (EEG) systems have been used to monitor attention states in individuals. However, conventional EEG instruments have limited utility in daily life because they are uncomfortable to wear. Thus, this study was designed to investigate the possibility of discriminating between the attentive and resting states using in-ear EEG signals for potential application via portable, convenient earphone-shaped EEG instruments. We recorded both on-scalp and in-ear EEG signals from 6 subjects in a state of attentiveness during the performance of a visual vigilance task. We have designed and developed in-ear EEG electrodes customized by modelling both the left and right ear canals of the subjects. We use an echo state network (ESN), a powerful type of machine learning algorithm, to discriminate attention states on the basis of in-ear EEGs. We have found that the maximum average accuracy of the ESN method in discriminating between attentive and resting states is approximately 81.16% with optimal network parameters. This study suggests that portable in-ear EEG devices and an ESN can be used to monitor attention states during significant tasks to enhance safety and efficiency.
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Xue, Rui, and Hokun Yi. "Advancement in Physical Education Teaching Using Improved Energy Efficient Scalable Routing Algorithm-Based Wireless Network." Wireless Communications and Mobile Computing 2022 (January 7, 2022): 1–10. http://dx.doi.org/10.1155/2022/2308255.
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Physical education (PE) is a crucial topic in higher coaching that individually points motor abilities in health-enhancing activities. Conventional PE in institutions struggles to pique graduates’ attentiveness in sports, proceeding in low task involvements rates, and incapacity to exercise the body. Innovative teaching concepts and methodologies, coaching techniques and procedures, and coaching assessment techniques in physical education are all accompanied to developing the physical education study hall climate and successfully boosting physical education efficacy. Each element of regular living, especially education, is being influenced by wireless internet innovations. We will provide extra help to students by predicting academic endurance or dropout. We can improve the wireless platform’s potential utility in sports applications and change the character of PE, including visualization and repetition by incorporating it into PE teaching. Based on the concept of wireless network technology, this paper proposes an Improved Energy Efficient Scalable Routing Algorithm (IEESRA) for physical education advancement. Initially, the physical education dataset is preprocessed using normalization. The aspects are removed using the scale-invariant feature transform (SIFT) method. The data is transferred using a wireless network using Improved Energy Efficient Scalable Routing Algorithm (IEESRA). The classification is done using random forest (RF) classifier. The results of the analysis reveal that wireless network-based PE may increase graduates’ strength, speed, and qualities providing a more important reference and reference for enhancing the success of PE. The proposed strategy has the potential to enhance actual attention to PE teaching to 90% with raising students’ engagement to 70%.
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Chatterjee, Indranath, Virendra Kumar, Sahil Sharma, Divyanshi Dhingra, Bharti Rana, Manoj Agarwal, and Naveen Kumar. "Identification of brain regions associated with working memory deficit in schizophrenia." F1000Research 8 (January 30, 2019): 124. http://dx.doi.org/10.12688/f1000research.17731.1.
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Background: Schizophrenia, a severe psychological disorder, shows symptoms such as hallucinations and delusions. In addition, patients with schizophrenia often exhibit a deficit in working memory which adversely impacts the attentiveness and the behavioral characteristics of a person. Although several clinical efforts have already been made to study working memory deficit in schizophrenia, in this paper, we investigate the applicability of a machine learning approach for identification of the brain regions that get affected by schizophrenia leading to the dysfunction of the working memory. Methods: We propose a novel scheme for identification of the affected brain regions from functional magnetic resonance imaging data by deploying group independent component analysis in conjunction with feature extraction based on statistical measures, followed by sequential forward feature selection. The features that show highest accuracy during the classification between healthy and schizophrenia subjects are selected. Results: This study reveals several brain regions like cerebellum, inferior temporal gyrus, superior temporal gyrus, superior frontal gyrus, insula, and amygdala that have been reported in the existing literature, thus validating the proposed approach. We are also able to identify some functional changes in the brain regions, such as Heschl gyrus and the vermian area, which have not been reported in the literature involving working memory studies amongst schizophrenia patients. Conclusions: As our study confirms the results obtained in earlier studies, in addition to pointing out some brain regions not reported in earlier studies, the findings are likely to serve as a cue for clinical investigation, leading to better medical intervention.
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Read, Blair, Lukas Wolters, and Adam J. Berinsky. "Racing the Clock: Using Response Time as a Proxy for Attentiveness on Self-Administered Surveys." Political Analysis, September 15, 2021, 1–20. http://dx.doi.org/10.1017/pan.2021.32.
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Abstract Internet-based surveys have expanded public opinion data collection at the expense of monitoring respondent attentiveness, potentially compromising data quality. Researchers now have to evaluate attentiveness ex-post. We propose a new proxy for attentiveness—response-time attentiveness clustering (RTAC)—that uses dimension reduction and an unsupervised clustering algorithm to leverage variation in response time between respondents and across questions. We advance the literature theoretically arguing that the existing dichotomous classification of respondents as fast or attentive is insufficient and neglects slow and inattentive respondents. We validate our theoretical classification and empirical strategy against commonly used proxies for survey attentiveness. In contrast to other methods for capturing attentiveness, RTAC allows researchers to collect attentiveness data unobtrusively without sacrificing space on the survey instrument.
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"Classification of Malignant Melanoma and Benign Lung Cancer by using Deep Learning Based Neural Network." International Journal of Innovative Technology and Exploring Engineering 9, no. 3 (January 10, 2020): 2958–63. http://dx.doi.org/10.35940/ijitee.k1704.019320.
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Humanoid Tumor is one of the utmost hazardous syndromes which is mostly affected by heritable uncertainty of manifold molecular modifications. Midst numerous methods of humanoid tumor, Lung cancer is the utmost communal one. To classify Lung cancer at an initial phase and examine them over several procedures entitled as segmentation and feature extraction. Here, in this scheme is suggested to emphasis extraordinary attentiveness of Melanoma Heir which bases the Lung Cancer. This development is based on samples replica skill is used for malignant melanoma Lung tumor recognition. In this scheme dissimilar stage for melanoma Lung cancer lesion classification i.e., first the Image Gaining Method, preprocessing, separation, define piece for Lung cancer Feature Collection regulates lesion description, classification methods. In the Feature abstraction by numerical image treating method includes, regularity detection, Border Detection, color, and width discovery and also we used GLCM for excerpt the surface based features. Here we planned the Neural Network to categorize the benign or malignant stage.
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Wang, Shaoming, Lindsey J. Tepfer, Adrienne A. Taren, and David V. Smith. "Functional parcellation of the default mode network: a large-scale meta-analysis." Scientific Reports 10, no. 1 (September 30, 2020). http://dx.doi.org/10.1038/s41598-020-72317-8.
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Abstract The default mode network (DMN) consists of several regions that selectively interact to support distinct domains of cognition. Of the various sites that partake in DMN function, the posterior cingulate cortex (PCC), temporal parietal junction (TPJ), and medial prefrontal cortex (MPFC) are frequently identified as key contributors. Yet, it remains unclear whether these subcomponents of the DMN make unique contributions to specific cognitive processes and health conditions. To address this issue, we applied a meta-analytic parcellation approach used in prior work. This approach used the Neurosynth database and classification methods to quantify the association between PCC, TPJ, and MPFC activation and specific topics related to cognition and health (e.g., decision making and smoking). Our analyses replicated prior observations that the PCC, TPJ, and MPFC collectively support multiple cognitive functions such as decision making, memory, and awareness. To gain insight into the functional organization of each region, we parceled each region based on its coactivation pattern with the rest of the brain. This analysis indicated that each region could be further subdivided into functionally distinct subcomponents. Taken together, we further delineate DMN function by demonstrating the relative strengths of association among subcomponents across a range of cognitive processes and health conditions. A continued attentiveness to the specialization within the DMN allows future work to consider the nuances in sub-regional contributions necessary for healthy cognition, as well as create the potential for more targeted treatment protocols in various health conditions.
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Namee, Khanista, Jantima Polpinij, and Bancha Luaphol. "A Hybrid Approach for Aspect-based Sentiment Analysis: A Case Study of Hotel Reviews." Current Applied Science and Technology 23, no. 2 (July 19, 2022). http://dx.doi.org/10.55003/cast.2022.02.23.008.
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This study presents a method of aspect-based sentiment analysis for customer reviews related to hotels. The considered hotel aspects are staff attentiveness, room cleanliness, value for money and convenience of location. The proposed method consists of two main components. The first component is used to assemble relevant sentences for each hotel aspect into relevant clusters of hotel aspects using BM25. We developed a corpus of keywords called the Keywords of Hotel Aspect (KoHA) Corpus, and the keywords of each aspect were used as queries to assemble relevant sentences of each hotel aspect into relevant clusters. Finally, customer review sentences in each cluster were classified into positive and negative classes using sentiment classifiers. Two algorithms, Support Vector Machines (SVM) with a linear and a RBF kernel, and Convolutional Neural Network (CNN) were applied to develop the sentiment classifier models. The model based on SVM with a linear kernel returned better results than other models with an AUC score of 0.87. Therefore, this model was chosen for the sentiment classification stage. The proposed method was evaluated using recall, precision and F1 with satisfactory results at 0.85, 0.87 and 0.86, respectively. Our proposed method provided an overview of customer feelings based on score, and also provided reasons why customers liked or disliked each aspect of the hotel. The best model from the proposed method was used to compare with a state-of-the-art model. The results show that our method increased recall, precision, and F1 scores by 2.44%, 2.50% and 1.84%, respectively.
Dissertations / Theses on the topic "Attentiveness classification"
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Pohling, Rico. "Moralische Sensitivität – Die Grundlage für die Wahrnehmung gesellschaftlicher Verantwortung in Organisationen." Universitätsverlag der Technischen Universität Chemnitz, 2017. https://monarch.qucosa.de/id/qucosa%3A20909.
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Die vorliegende Dissertation beschäftigte sich theoretisch und empirisch mit moralischer Sensitivität, der Fähigkeit zu erkennen, ob Handlungen das Wohlergehen anderer Lebewesen beeinflussen. Im Theorieteil wurden dazu die einschlägigen theoretischen Ansätze gesichtet und in einer neuen integrativen Definition verdichtet. Aus der theoretischen Aufarbeitung der Konzepte und bisherigen empirischen Befunde folgte, dass verschiedene Persönlichkeitsmerkmale existieren, die maßgeblich determinieren, ob und wie stark ein Individuum moralisch sensitiv ist. Zu diesen Determinanten moralischer Sensitivität gehören folgende Persönlichkeitsmerkmale: Empathie, moralische Achtsamkeit, moralische Identität und Ungerechtigkeitssensibilität. Im empirischen Teil der Arbeit wurden die Zusammenhänge dieser Persönlichkeitsmerkmale mit moralischem Entscheiden und Handeln untersucht, sowie verschiedene studienspezifische Hypothesen und Fragestellungen beleuchtet. Einzelne Aspekte der empirischen Arbeit wurden schließlich in einer interkulturellen Studie auf ihre kulturübergreifende Generalisierbarkeit geprüft. Die Dissertationsschrift schließt mit einer umfassenden Diskussion der empirischen Ergebnisse und gibt praktische Handlungsempfehlungen. Unternehmen und andere Organisationen können die in der vorliegenden Dissertation gewonnenen Erkenntnisse nutzen, um ihr Human Ressource Management hinsichtlich der Auswahl und Förderung ethisch-kompetenter Fach- und Führungskräfte zu optimieren.The present dissertation theoretically and empirically investigated the concept of moral sensitivity – the ability to recognize whether actions influence the well-being of other living beings. In the theoretical part, the relevant theoretical approaches were reviewed and condensed in a new integrative definition. The review of the theoretical accounts of moral sensitivity and the empirical findings revealed that various personality traits exist that decisively determine whether and how strongly an individual is morally sensitive. These determinants of moral sensitivity include the following personality traits: empathy, moral attentiveness, moral identity, and justice sensitivity. In the empirical part of the work, the relationships of these personality traits with moral decision-making and action and various study-specific hypotheses were examined. The cross-cultural generalizability of some empirical findings of the dissertation was finally examined in an intercultural study. The dissertation concludes with a comprehensive discussion of the empirical results and gives practical recommendations for organizations. Companies and other organizations can use the knowledge gained in this dissertation to optimize their human resource management in terms of selection and promotion of ethical competent employees.
Conference papers on the topic "Attentiveness classification"
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Negron, Timothy P., and Corey A. Graves. "Classroom Attentiveness Classification Tool (ClassACT): The system introduction." In 2017 IEEE International Conference on Pervasive Computing and Communications: Workshops (PerCom Workshops). IEEE, 2017. http://dx.doi.org/10.1109/percomw.2017.7917513.
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T. Gapi, Kevin, Ronald Matthew G. Magbitang, and Jocelyn F. Villaverde. "Classification of Attentiveness on Virtual Classrooms using Deep Learning for Computer Vision." In ICBET '21: 2021 11th International Conference on Biomedical Engineering and Technology. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3460238.3460244.
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Yerofeyeva, Alina, and Tatyana Kokhanover. "THE RELEVANCE OF THE DEVELOPMENT OF PERCEPTUAL ABILITIES IN CHILDREN OF PRIMARY SCHOOL AGE." In Modern pedagogical technologies in foreign language education: trends, transformations, vectors of development. ACCESS Press, 2021. http://dx.doi.org/10.46656/proceeding.2021.foreign.language(35).
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Article is devoted to the study of the peculiarities and importance of the development of perceptual abilities of children studying in primary school. First of all, the concept of perception, classification and properties are considered for further understanding of the topic. The article includes a comparison of perception by age groups and shows by the example of an experiment how much perception skills affect the lives of primary school children. The peculiarities of the psyche of children aged 7-10 years, such as concentration time, attentiveness, perception of time, were taken into account. Basically, the article touches on certain types of perceptual abilities, such as visual and auditory perceptions. These types of perception skills are of the greatest importance in the development of children.