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    Determination of stress obtained from sensor-based wearable measurements using VGG16 deep learning model
    (Frontier Scientific Publishing, 2024) Yücelbaş, Cüneyt; Yücelbaş, Şule
    There are many researches carried out for different purposes on human-computer interactions. One of them is related to stress-related activity detection. Today, in people with disorders whose activity is not understood or misunderstood, the correct detection of the relevant movement can be vital in some cases. It may be more advantageous to use physiological signals in the body in determining the type of activity. Due to these important situations, two different research applications were carried out within the scope of this study in order to automatically detect four different types of stress, namely Neutral, Emotional, Mental, and Physical. For both applications, the data was first converted to images as a preprocessing. In the first stage of the research, the images of the standard dataset were presented to the VGG16 deep learning model. As a result, the highest accuracy rate was obtained as 67% for class 1 Neutral activation. In the second part of the study, an application was performed using the Isolation Forest Algorithm on the existing image data to remove outliers. The new dataset obtained were presented to the same model and detailed analyses were made. Accordingly, the maximum accuracy value was 97% in Physical activity. In the same application, the average rate for all activities was 82.5%. Briefly, the research contributes to the literature by demonstrating the significant impact of outliers on system performance through image transformations of existing time series physiological signals. © 2024 by author(s).
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    Examining the Success of Information Gain, Pearson Correlation, and Symmetric Uncertainty Ranking Methods on 3D Hand Posture Data for Metaverse Systems
    (Sakarya University, 2023) Yücelbaş, Cüneyt; Yücelbaş, Şule
    Metaverse is a hardware and software interface space that can connect people's social lives as in the real-natural world and provide the feeling of being there at the maximum level. In order for metaverse systems to be efficient, many independent accessories have to work holistically. One of these accessories is wearable gloves called meta gloves and equipped with sensors. Thanks to it, an important stage of metaverse systems is completed with the detection of 3-dimensional (3D) hand postures. In this study, the success of Information Gain, Pearson’s Correlation, and Symmetric Uncertainty ranking methods on 3D hand posture data for metaverse systems were investigated. For this purpose, various preprocessing was performed on the 3D data, and a dataset consisting of 15 features in total was created. The created dataset was ranked by 3 different methods mentioned and the features that the methods determined effectively were classified separately. Obtained results were interpreted with various statistical evaluation criteria. According to the experimental results obtained, it has been seen that the Symmetric Uncertainty ranking algorithm produces successful results for metaverse systems. As a result of the classification made with the active features determined using this method, there has been an increase in statistical performance criteria compared to other methods. In addition, it has been proven that time loss can be avoided in the classification of big data similar to the data used. © 2023, Sakarya University. All rights reserved.
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    Person Recognition from Gait Analysis for Smart Spaces by using MLP-based DNN model
    (2023) Yücelbaş, Cüneyt
    In smart fields, security measures are taken to protect people against threats that may arise by using technology and to provide crisis management, and the functions of measuring area security and ensuring its effectiveness are carried out. As an element of this measurement, it is thought that person recognition may be the most important factor in the future. It is seen that deep learning-based algorithms, which can provide fast and high-accuracy results with many data, will be an integral part of this sector in the future as they are today. However, when the literature is examined, it is understood that the number of research in which Deep learning algorithms are used in order to increase the success of the studies in this direction and the system practicality is insufficient. For this reason, in this study, deep learning was used to recognize people by using the walking data of 15 people obtained thanks to wearable sensors. Since the increase in the diversity of the data will positively affect the learning of the created model, data augmentation has been made and these data have been classified in the MLP-based DNN model. The results were statistically analyzed and showed that this model exhibited excellent performance in person recognition from walking data. In addition, the ACC rate was found to be 100%, and it proved that the method used to increase the data also produced successful results in walking data. It is thought that the success of the study can provide important perspective support to new studies for smart fields in the literature.