Person: AŞCI, GÜVEN
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AŞCI
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GÜVEN
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Publication Deep Learning Approaches for Phantom Movement Recognition(2019-11-03) Güngör, Faray; Tarakçı, Ela; Aydın, Muhammed Ali; Zaim, Abdül Halim; AKBULUT, AKHAN; AŞCI, GÜVEN; 116056; 285689; 277179; 101760; 176402; 8693Phantom limb pain has a negative effect on the life of individuals as a frequent consequence of limb amputation. The movement ability on the lost extremity can still be maintained after the amputation or deafferentation, which is called the phantom movement. The detection of these movements makes sense for cybertherapy and prosthetic control for amputees. In this paper, we employed several deep learning approaches to recognize phantom movements of the three different amputation regions including above-elbow, below-knee and above-knee. We created a dataset that contains 25 healthy and 16 amputee participants’ surface electromyography (sEMG) readings via a wearable device with 2-channel EMG sensors. We compared the results of three different deep learning methods, respectively, Multilayer Perceptron, Convolutional Neural Network, and Recurrent Neural Network with the accuracies of two well-known shallow methods, k Nearest Neighbor and Random Forest. Our experiments indicate, Convolutional Neural Network-based model achieved an accuracy of 74.48% in recognizing phantom movements of amputees.Publication Transcranial direct current stimulation may enhance decision making in gambling disorder: A preliminary double-blind sham-controlled study(2017) Aksu, Serkan; Soyata, Ahmet Zihni; İşçen, P. A.; Saçar, K. T.; Karamürsel, Sacit; AŞCI, GÜVEN; ŞANDOR, SERRA; 112898; 194229; 107162; 19597Publication A Novel Input Set for LSTM based Transport Mode Detection(2019-03) Güvensan, M.Amaç; AŞCI, GÜVEN; 285689The capability of mobile phones are increasing with the development of hardware and software technology. Especially sensors on smartphones enable to collect environmental and personal information. Thus, smartphones become the key components of ambient intelligence. Human activity recognition and transport mode detection (TMD) are the main research areas for tracking the daily activities of a person. This study aims to introduce a novel input set for daily activities mainly for transportation modes in order to increase the detection rate. In this study, the frame-based novel input set consisting of time-domain and frequency-domain features are fed to LSTM network. Thus, the classification ratio on HTC public dataset is climbed up to 97% which is 2% more than the state-of-the-art method in the literature.Publication A Wearable Device for Virtual Cyber Therapy of Phantom Limb Pain(2018-09) Tarakçı, Ela; Aydın, Muhammed; Zaim, Abdul Halim; AKBULUT, AKHAN; AŞCI, GÜVEN; 285689; 116056; 101760; 176402; 8693Phantom limb pain (PLP) is the condition most often occurs in people who have had a limb amputated and it is may affect their life severely. When the brain sends movement signals to the phantom limb, it returns and causes a pain. Many medical approaches aim to treat the PLP, however the mirror therapy still considered as the base therapy method. The aim of this research is to develop a wearable device that measures the EMG signals from PLP patients to classify movements on the amputated limb. These signals can be used in virtual reality and augmented reality environments to realize the movements in order to reduce pain. A data set was generated with measurements taken from 8 different subjects and the classification accuracy achieved as 90% with Neural Networks method that can be used in cyber therapies.This type of therapy provides strong visuals which make the patient feel he/she really have the limb. The patient will have great therapy session time with comparison to the other classical therapy methods that can be used in home environments.