Support vector machines to detect physiological patterns for EEG and EMG-based human-computer interaction:a review

Support vector machines (SVMs) are widely used classifiers for detecting physiological patterns in human-computer interaction (HCI). Their success is due to their versatility, robustness and large availability of free dedicated toolboxes. Frequently in the literature, insufficient details about the SVM implementation and/or parameters selection are reported, making it impossible to reproduce study analysis and results. In order to perform an optimized classification and report a proper description of the results, it is necessary to have a comprehensive critical overview of the applications of SVM. The aim of this paper is to provide a review of the usage of SVM in the determination of brain and muscle patterns for HCI, by focusing on electroencephalography (EEG) and electromyography (EMG) techniques. In particular, an overview of the basic principles of SVM theory is outlined, together with a description of several relevant literature implementations. Furthermore, details concerning reviewed papers are listed in tables and statistics of SVM use in the literature are presented. Suitability of SVM for HCI is discussed and critical comparisons with other classifiers are reported

The Effect of Green Software: A Study of Impact Factors on the Correctness of Software

Unfortunately, sustainability is an issue very poorly used when developing software and hardware systems. Lately, and in order to contribute to the earth sustainability, a new concept emerged named Green software which is computer software that can be developed and used efficiently and effectively with minimal or no impact to the environment. Currently, new teaching methods based on students’ learning process are being developed in the European Higher Education Area. Most of them are oriented to promote students’ interest in the course’s contents and offer personalized feedback. Online judging is a promising method for encouraging students’ participation in the e-learning process, although it still has to be researched and developed to be widely used and in a more efficient way. The great amount of data available in an online judging tool provides the possibility of exploring some of the most indicative attributes (e.g., running time, memory) for learning programming concepts, techniques and languages. So far, the most applied methods for automatically gathering information from the judging systems are based on statistical methods and, although providing reasonable correlations, these methods have not been proven to provide enough information for predicting grades when dealing with a huge amount of data. Therefore, the great novelty of this paper is to develop a data mining approach to predict program correctness as well as the grades of the students’ practices. For this purpose, powerful data mining technologies taken from the artificial intelligence domain have been used. In particular, in this study, we have used logistic regression, decision trees, artificial neural network and support vector machines; which have been properly identified as the most suitable ones for predicting activities in the e-learning domains. The results have achieved an accuracy of around 74%, both in the prediction of the program correctness as well as in the practice grades’ prediction. Another relevant issue provided in this paper is a comparison among these four techniques to obtain the best accuracy in predicting grades based on the availability of data as well as their taxonomy. The Decision Trees classifier has obtained the best confusion matrix, and time and memory efficiency were identified as the most important predictor variables. In view of these results, we can conclude that the development of green software leads programmers to implement correct software.This work has been funded by the Spanish Ministry of Economy and Competitiveness (MINECO/FEDER) under the granted project SEQUOIA-UA (TIN2015-63502-C3-3-R), project GINSENG-UMU (TIN2015-70259-C2-2-R) supported by the Spanish Ministry of Economy, Industry and Competitiveness and European FEDER funds. This work has also been partially funded by University of Alicante, under project GRE14-10 and by the Generalitat Valenciana, Spain, under project GV/2016/087

Facial neuromuscular signal classification by means of least square support vector machine for MuCI

Facial neuromuscular signal has recently drawn the researchers' attention to its outstanding potential as an efficient medium for Muscle Computer Interface (MuCI) applications. The proper analysis of such electromyogram (EMG) signals is essential in designing the interfaces. In this article, a multiclass least-square support vector machine (LS-SVM) is proposed for classification of different facial gestures EMG signals. EMG signals were captured through three bi-polar electrodes from ten participants while gesturing ten different facial states. EMGs were filtered and segmented into non-overlapped windows from which root mean square (RMS) features were extracted and then fed to the classifier. For the purpose of classification, different models of LS-SVM were constructed while tuning the kernel parameters automatically and manually. In the automatic mode, 48 models were formed while parameters of linear and radial basis function (RBF) kernels were tuned using different optimization techniques, cost functions and encoding schemes. In the manual mode, 8 models were shaped by means of the considered kernel functions and encoding schemes. In order to find the best model with a reliable performance, constructed models were evaluated and compared in terms of classification accuracy and computational cost. Results reported that the model including RBF kernel which was tuned manually and encoded by one-versus-all scheme provided the highest classification accuracy (93.10%) and consumed 0.98 s for training. It was indicated that automatic models were outperformed since they required too much time for tuning the parameters without any meaningful improvement in the final classification accuracy. The robustness of the selected LS-SVM model was evaluated through comparison with Support Vector Machine, fuzzy C-Means and uzzy Gath-Geva clustering technique

Affective state recognition in Virtual Reality from electromyography and photoplethysmography using head-mounted wearable sensors.

The three core components of Affective Computing (AC) are emotion expression recognition, emotion processing, and emotional feedback. Affective states are typically characterized in a two-dimensional space consisting of arousal, i.e., the intensity of the emotion felt; and valence, i.e., the degree to which the current emotion is pleasant or unpleasant. These fundamental properties of emotion can not only be measured using subjective ratings from users, but also with the help of physiological and behavioural measures, which potentially provide an objective evaluation across users. Multiple combinations of measures are utilised in AC for a range of applications, including education, healthcare, marketing, and entertainment. As the uses of immersive Virtual Reality (VR) technologies are growing, there is a rapidly increasing need for robust affect recognition in VR settings. However, the integration of affect detection methodologies with VR remains an unmet challenge due to constraints posed by the current VR technologies, such as Head Mounted Displays. This EngD project is designed to overcome some of the challenges by effectively integrating valence and arousal recognition methods in VR technologies and by testing their reliability in seated and room-scale full immersive VR conditions. The aim of this EngD research project is to identify how affective states are elicited in VR and how they can be efficiently measured, without constraining the movement and decreasing the sense of presence in the virtual world. Through a three-years long collaboration with Emteq labs Ltd, a wearable technology company, we assisted in the development of a novel multimodal affect detection system, specifically tailored towards the requirements of VR. This thesis will describe the architecture of the system, the research studies that enabled this development, and the future challenges. The studies conducted, validated the reliability of our proposed system, including the VR stimuli design, data measures and processing pipeline. This work could inform future studies in the field of AC in VR and assist in the development of novel applications and healthcare interventions

BGSU 1987-1988-1989 Undergraduate Catalog

Bowling Green State University undergraduate catalog for 1987-1988-1989.https://scholarworks.bgsu.edu/catalogs/1016/thumbnail.jp

Smoking and Second Hand Smoking in Adolescents with Chronic Kidney Disease: A Report from the Chronic Kidney Disease in Children (CKiD) Cohort Study

The goal of this study was to determine the prevalence of smoking and second hand smoking [SHS] in adolescents with CKD and their relationship to baseline parameters at enrollment in the CKiD, observational cohort study of 600 children (aged 1-16 yrs) with Schwartz estimated GFR of 30-90 ml/min/1.73m2. 239 adolescents had self-report survey data on smoking and SHS exposure: 21 [9%] subjects had “ever” smoked a cigarette. Among them, 4 were current and 17 were former smokers. Hypertension was more prevalent in those that had “ever” smoked a cigarette (42%) compared to non-smokers (9%), p\u3c0.01. Among 218 non-smokers, 130 (59%) were male, 142 (65%) were Caucasian; 60 (28%) reported SHS exposure compared to 158 (72%) with no exposure. Non-smoker adolescents with SHS exposure were compared to those without SHS exposure. There was no racial, age, or gender differences between both groups. Baseline creatinine, diastolic hypertension, C reactive protein, lipid profile, GFR and hemoglobin were not statistically different. Significantly higher protein to creatinine ratio (0.90 vs. 0.53, p\u3c0.01) was observed in those exposed to SHS compared to those not exposed. Exposed adolescents were heavier than non-exposed adolescents (85th percentile vs. 55th percentile for BMI, p\u3c 0.01). Uncontrolled casual systolic hypertension was twice as prevalent among those exposed to SHS (16%) compared to those not exposed to SHS (7%), though the difference was not statistically significant (p= 0.07). Adjusted multivariate regression analysis [OR (95% CI)] showed that increased protein to creatinine ratio [1.34 (1.03, 1.75)] and higher BMI [1.14 (1.02, 1.29)] were independently associated with exposure to SHS among non-smoker adolescents. These results reveal that among adolescents with CKD, cigarette use is low and SHS is highly prevalent. The association of smoking with hypertension and SHS with increased proteinuria suggests a possible role of these factors in CKD progression and cardiovascular outcomes