Scopus İndeksli Yayınlar Koleksiyonu

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Author: search.filters.author.Erdas, Cagatay Berkesearch.filters.applied.f.rights: search.filters.rights.info:eu-repo/semantics/closedAccess

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    Human Activity Recognition by Using Different Deep Learning Approaches for Wearable Sensors
    (2021) Erdas, Cagatay Berke; Guney, Selda; 0000-0003-3467-9923
    With the spread of wearable sensors, the solutions to the task of activity recognition by using the data obtained from the sensors have become widespread. Recognition of activities owing to wearable sensors such as accelerometers, gyroscopes, and magnetometers, etc. has been studied in recent years. Although there are several applications in the literature, differently in this study, deep learning algorithms such as Convolutional Neural Networks, Convolutional LSTM, and 3D Convolutional Neural Networks fed by Convolutional LSTM have been used in human activity recognition task by feeding with data obtained from accelerometer sensor. For this purpose, a frame was formed with raw samples of the same activity which were collected consecutively from the accelerometer sensor. Thus, it is aimed to capture the pattern inherent in the activity and due to preserving the continuous structure of the movement.
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    Neurodegenerative disease detection and severity prediction using deep learning approaches
    (2021) Erdas, Cagatay Berke; Sumer, Emre; Kibaroglu, Seda; 0000-0002-3964-268X; AAJ-2956-2021; AGA-5711-2022
    Neurodegenerative diseases (NDDs) such as amyotrophic lateral sclerosis (ALS), Huntington's disease (HD), and Parkinson's disease (PD) can manifest themselves anatomically by degeneration in the brain as well as motor symptoms. The motor symptoms can affect walking dynamics in a disease-specific fashion; characteristically they disrupt gait. As the severity of the disease increases, walking ability deteriorates. We examined the effect of NDDs such as ALS, HD, and PD on gait and developed a convolutional long short-term memory (ConvLSTM) and threedimensional convolutional learning network (3D CNN)-based approach to detecting neurodegenerative conditions and predicting disease severity.