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AKBULUT, FATMA PATLAR

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Dr. Öğr. Üyesi

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AKBULUT

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FATMA PATLAR

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2014 - 201952020 - 202423
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Now showing 1 - 10 of 28
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    PublicationOpen Access
    Stress Detection Using Experience Sampling: A Systematic Mapping Study
    (MDPI, 2022) DOĞAN, GÜLİN; AKBULUT, FATMA PATLAR; Çatal, Çağatay; Mishra, Alok
    Stress has been designated the "Health Epidemic of the 21st Century" by the World Health Organization and negatively affects the quality of individuals' lives by detracting most body systems. In today's world, different methods are used to track and measure various types of stress. Among these techniques, experience sampling is a unique method for studying everyday stress, which can affect employees' performance and even their health by threatening them emotionally and physically. The main advantage of experience sampling is that evaluating instantaneous experiences causes less memory bias than traditional retroactive measures. Further, it allows the exploration of temporal relationships in subjective experiences. The objective of this paper is to structure, analyze, and characterize the state of the art of available literature in the field of surveillance of work stress via the experience sampling method. We used the formal research methodology of systematic mapping to conduct a breadth-first review. We found 358 papers between 2010 and 2021 that are classified with respect to focus, research type, and contribution type. The resulting research landscape summarizes the opportunities and challenges of utilizing the experience sampling method on stress detection for practitioners and academics.
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    Estimation of beat-to-beat interval from wearable photoplethysmography sensor on different measurement sites during daily activities
    (2018) Lawless, Kevin; Tanneeru, Akhilesh; Rao, Smriti; Lee, Bongmook; Misra, Veena; AKBULUT, FATMA PATLAR
    In this study, we present an algorithm to detect beat-to-beat interval from PPG in the presence of motion artifacts. Our approach includes splitting slowly varying DC components, statistical detrending, and Bessel filtering and Fast Fourier Transform with square window to reduce motion artifacts dependent on spectrum analysis. The algorithm segments beat intervals with a spectrogram to find the characteristic points of the waveform such as systolic and diastolic points. Interbeat intervals (IBI) are determined from these characteristic points to calculate heart rate. The PPG IBI algorithm is validated against ECG RR intervals from five different measurement sites during three daily activities. The results show that the most accurate IBI and HR detection from a wearable PPG device during regular user activity is from the upper arm or finger.
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    PublicationOpen Access
    NeuroBioSense: A Multidimensional Dataset for Neuromarketing Analysis
    (Elsevier, 2024) KOCAÇINAR, BÜŞRA; İNAN, PELİN; ZAMUR, ELA NUR; ÇALŞİMŞEK, BUKET; AKBULUT, FATMA PATLAR; Çatal, Çağatay
    In the context of neuromarketing, sales, and branding, the investigation of consumer decision-making processes presents complex and intriguing challenges. Consideration of the effects of multicultural influences and societal conditions from a global perspective enriches this multifaceted field. The application of neuroscience tools and techniques to international marketing and consumer behavior is an emerging interdisciplinary field that seeks to understand the cognitive processes, reactions, and selection mechanisms of consumers within the context of branding and sales. The NeuroBioSense dataset was prepared to analyze and classify consumer responses. This dataset includes physiological signals, facial images of the participants while watching the advertisements, and demographic information. The primary objective of the data collection process is to record and analyze the responses of human subjects to these signals during a carefully designed experiment consisting of three distinct phases, each of which features a different form of branding advertisement. Physiological signals were collected with the Empatica e4 wearable sensor device, considering non-invasive body photoplethysmography (PPG), electrodermal activity (EDA), and body temperature sensors. A total of 58 participants, aged between 18 and 70, were divided into three different groups, and data were collected. Advertisements prepared in the categories of cosmetics for 18 participants, food for 20 participants, and cars for 20 participants were watched. On the emotion evaluation scale, 7 different emotion classes are given: Joy, Surprise, anger, disgust, sadness, fear, and neutral. This dataset will help researchers analyse responses, understand and develop emotion classification studies, the relationship between consumers and advertising, and neuromarketing methods.
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    PublicationOpen Access
    Wearable Sensor Device for Posture Monitoring and Analysis During Daily Activities: A Preliminary Study
    (Dr. Ceyhun YILMAZ, 2022) AKBULUT, FATMA PATLAR; ÖZGÜL, GİZEM
    The increase in technological advancements in recent years has led to the emergence of a new lifestyle. Although being assisted by machines for small-scale tasks in daily housework makes daily life easier, this has caused people to reduce their daily active movements and negatively affects human health. Especially during the COVID-19 pandemic, with the conversion of the working style to the home environment, working hours spent at the desk are more than ever. Due to the prolongation of the working time, the employees stay in the same position more inactive, thus their muscles weaken and they start to have muscle disease. Weaknesses in the muscles have occurred to the formation of postural problems in people. In our study, a smart vest system was developed to detect and control posture disorders. The proposed system is designed to recommend the most suitable exercises to avoid any physical discomforts. It is also aimed to detect hunched posture by collecting data on the person wearing the vest through sensors. Besides, it is encouraged to correct the posture disorder by warning the person audibly during the hunched posture. The experiments conducted with eight participants showed that the proposed system warns the users with necessary posture corrections, proving its potential use.
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    A smart wearable system for short-term cardiovascular risk assessment with emotional dynamics
    (Elsevier Sci Ltd, The Boulevard, Langford Lane, Kidlington, Oxford Ox5 1Gb, Oxon, England, 2018-11) Akan, Aydın; AKBULUT, FATMA PATLAR; 2918
    Recent innovative treatment and diagnostic methods developed for heart and circulatory system disorders do not provide the desired results as they are not supported by long-term patient follow-up. Continuous medical support in a clinic or hospital is often not feasible in elderly or aging populations; yet, collecting medical data is still required to maintain a high-quality of life. In this study, a smart wearable system design called Cardiovascular Disease Monitoring (CVDiMo), which provides continuous medical monitoring and creates a health profile with the risk of disease over time. Systematic tests were performed with analysis of six different biosignals from two different test groups with 30 participants. In addition to examining the biosignals of patients, using the physical activity results and stress levels deduced from the emotional state analysis achieved a higher performance in risk estimation. In our experiments, the highest accuracy of determining the short-term health status was obtained as 96%.
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    Restoring Fluorescence Microscopy Images by Transfer Learning From Tailored Data
    (IEEE-Inst Electrical Electronics Engineers Inc., 2022) AKBULUT, FATMA PATLAR; TÜREYEN, EZGİ DEMİRCAN; Kamasak, Mustafa E.
    In fluorescence microscopy imaging, noise is a very usual phenomenon. To some extent, it can be suppressed by increasing the amount of the photon exposure; however, it is not preferable since this may not be tolerated by the subjected specimen. Thus, a sophisticated computational method is needed to denoise each acquired micrograph, so that they become more adequate for further feature extraction and image analysis. However, apart from the difficulties of the denoising problem itself, one main challenge is that the absence of the ground-truth images makes the data-driven techniques less applicable. In order to tackle this challenge, we suggest to tailor a dataset by handpicking images from unrelated source datasets. Our tailoring strategy involves exploring some low-level view-based features of the candidate images, and their similarities to those of the fluorescence microscopy images. We pretrain and fine-tune the well-known feed-forward denoising convolutional neural networks (DnCNNs) on our tailored dataset and a very limited amount of fluorescence images, respectively to ensure both the diversity and the content-awareness. The quantitative and visual experimentation show that our approach is able to curate a dataset, which is significantly superior to the arbitrarily chosen source images, and well-approximates to the fluorescence images. Moreover, the combination of the tailored dataset with a few fluorescence data through the use of fine-tuning offers a good balance between the generalization capability and the content-awareness, on the majority of considered scenarios.
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    Analysis of the Lingering Effects of COVID-19 on Distance Education
    (Springer Science and Business Media Deutschland GmbH, 2023) KOCAÇINAR, BÜŞRA; QARIZADA, NASIBULLAH; DİKKAYA, CİHAN; AZGUN, EMİRHAN; ELİF, YILDIRIM; AKBULUT, FATMA PATLAR
    Education has been severely impacted by the spread of the COVID-19 virus. In order to prevent the spread of the COVID-19 virus and maintain education in the current climate, governments have compelled the public to adopt online platforms. Consequently, this decision has affected numerous lives in various ways. To investigate the impact of COVID-19 on students’ education, we amassed a dataset consisting of 10,000 tweets. The motivations of the study are; (i) to analyze the positive, negative, and neutral effects of COVID-19 on education; (ii) to analyze the opinions of stakeholders in their tweets about the transition from formal education to e-learning; (iii) to analyze people’s feelings and reactions to these changes; and (iv) to analyze the effects of different training methods on different groups. We constructed emotion recognition models utilizing shallow and deep techniques, including Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long-short Term Memory (LSTM), Random Forest (RF), Naive Bayes (NB), Support Vector Machine (SVM), and Logical Regression (LR). RF algorithms with a bag-of-words model outperformed with over 80% accuracy in recognizing emotions.
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    PublicationRestricted
    Hybrid Deep Convolutional Model-Based Emotion Recognition Using Multiple Physiological Signals
    (Taylor & Francis Ltd., 2022) AKBULUT, FATMA PATLAR
    Emotion recognition has become increasingly utilized in the medical, advertising, and military domains. Recognizing the cues of emotion from human behaviors or physiological responses is encouraging for the research community. However, extracting true characteristics from sensor data to understand emotions can be challenging due to the complex nature of these signals. Therefore, advanced feature engineering techniques are required for accurate signal recognition. This study presents a hybrid affective model that employs a transfer learning approach for emotion classification using large-frame sensor signals which employ a genuine dataset of signal fusion gathered from 30 participants using wearable sensor systems interconnected with mobile devices. The proposed approach implements several learning algorithms such as Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and several other shallow methods on the sensor input to handle the requirements for the traditional feature extraction process. The findings reveal that the use of deep learning methods is satisfactory in affect recognition when a great number of frames is employed, and the proposed hybrid deep model outperforms traditional neural network (overall accuracy of 54%) and deep learning approaches (overall accuracy of 76%), with an average classification accuracy of 93%. This hybrid deep model also has a higher accuracy than our previously proposed statistical autoregressive hidden Markov model (AR-HMM) approach, with 88.6% accuracy. Accuracy assessment was performed by means of several statistics measures (accuracy, precision, recall, F-measure, and RMSE).
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    PublicationOpen Access
    Deep Learning-Based User Experience Evaluation in Distance Learning
    (Springer, 2023) SADIGOV, RAHIM; YILDIRIM, ELİF; KOCAÇINAR, BÜŞRA; AKBULUT, FATMA PATLAR; Çatal, Çağatay
    The Covid-19 pandemic caused uncertainties in many different organizations, institutions gained experience in remote working and showed that high-quality distance education is a crucial component in higher education. The main concern in higher education is the impact of distance education on the quality of learning during such a pandemic. Although this type of education may be considered effective and beneficial at first glance, its effectiveness highly depends on a variety of factors such as the availability of online resources and individuals' financial situations. In this study, the effectiveness of e-learning during the Covid-19 pandemic is evaluated using posted tweets, sentiment analysis, and topic modeling techniques. More than 160,000 tweets, addressing conditions related to the major change in the education system, were gathered from Twitter social network and deep learning-based sentiment analysis models and topic models based on latent dirichlet allocation (LDA) algorithm were developed and analyzed. Long short term memory-based sentiment analysis model using word2vec embedding was used to evaluate the opinions of Twitter users during distance education and also, a topic model using the LDA algorithm was built to identify the discussed topics in Twitter. The conducted experiments demonstrate the proposed model achieved an overall accuracy of 76%. Our findings also reveal that the Covid-19 pandemic has negative effects on individuals 54.5% of tweets were associated with negative emotions whereas this was relatively low on emotion reports in the YouGov survey and gender-rescaled emotion scores on Twitter. In parallel, we discuss the impact of the pandemic on education and how users' emotions altered due to the catastrophic changes allied to the education system based on the proposed machine learning-based models.
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    PublicationOpen Access
    Analysis of Facial Emotion Expression in Eating Occasions Using Deep Learning
    (Springer, 2023) ELİF, YILDIRIM; AKBULUT, FATMA PATLAR; Çatal, Çağatay
    Eating is experienced as an emotional social activity in any culture. There are factors that influence the emotions felt during food consumption. The emotion felt while eating has a significant impact on our lives and affects different health conditions such as obesity. In addition, investigating the emotion during food consumption is considered a multidisciplinary problem ranging from neuroscience to anatomy. In this study, we focus on evaluating the emotional experience of different participants during eating activities and aim to analyze them automatically using deep learning models. We propose a facial expression-based prediction model to eliminate user bias in questionnaire-based assessment systems and to minimize false entries to the system. We measured the neural, behavioral, and physical manifestations of emotions with a mobile app and recognize emotional experiences from facial expressions. In this research, we used three different situations to test whether there could be any factor other than the food that could affect a person’s mood. We asked users to watch videos, listen to music or do nothing while eating. This way we found out that not only food but also external factors play a role in emotional change. We employed three Convolutional Neural Network (CNN) architectures, fine-tuned VGG16, and Deepface to recognize emotional responses during eating. The experimental results demonstrated that the fine-tuned VGG16 provides remarkable results with an overall accuracy of 77.68% for recognizing the four emotions. This system is an alternative to today’s survey-based restaurant and food evaluation systems.