Apr 2022
Great Support to Our Faculty Member from European Union

Horizon Europe European Research Council (ERC) Starting Grant will support our Textile Technologies and Design Faculty Member Assoc. Prof. Dr. Özgür Atalay’s project titled “TEXWEAROTS”.

Nov 2021
Assoc. Dr. Ozgur Atalay was Announced among the Most Influential Scientists in the World

According to the latest list of the world's most influential scientists, created annually by scientists from Stanford University, was recently published by Elsevier, 50 academicians from ITU were included. Assoc. Dr. Özgür Atalay is also involved in the list. We congratulate him on his outstanding achievement.

Oct 2021
TexRobots Team won the first place in the Biotechnology and Innovation Competition conducted by Teknofest.

In the Biotechnology and Innovation Competition conducted by Teknofest, the TexRobots Team (Kadir Özlem, Ayşe Feyza Yılmaz, Fidan Khalilbayli, Ömür Fatmanur Erzurumluoğlu) won the first prize with their “Texrobot for Hand Paralysis Rehabilitation” project.

Aug 2021
Master Degree Student has Presented her Thesis Project

Master degree student Fidan Khalilbayli has successfully presented her thesis project.

Oct 2020
Uğur Ayvaz Presents His Work at BODYNETS 2020

Abstract: Real-time human activity recognition is a popular and challenging topic in sensor systems. Inertial measurement units, vision-based systems, and wearable sensor systems are mostly used for gathering motion data. However, each system has drawbacks such as drift error, illumination, occlusion, etc. Therefore, under certain circumstances, they are not efficient alone in activity estimation. To overcome this, hybrid sensor systems were used as an alternative approach in the last decade. In this study, a human activity recognition system is proposed using textile-based capacitive sensors. The aim of the system is to recognize the basic human actions in real-time such as walking, running, squatting, and standing. The sensor system proposed in this study is used to collect human activity data from the participants with different anthropometrics and create an activity recognition system. The performance of the machine learning models is tested on unseen activity data. The obtained results showed the effectiveness of our approach by achieving high accuracy up to 83.1% on selected human activities in real-time.

Oct 2020
Ezgi Paket and Kadir Ozlem Present Their Work at SIU 2020

Abstract: CardioVascular Diseases (CVDs) have a significant share over all medical problems. From this point of view, many studies have been conducted on heart diseases and different heartbeat monitoring systems have been developed. Although Electro-CardioGraphy (ECG) is the most widely used technique among other monitoring systems, ECG measurement with conventional electrodes have also many disadvantages that can be overcome if replaced with textile electrodes. This study involves creation of textile based ECG electrodes, related circuitry designs, signal processing, implementations of peak detection and heart rate calculation algorithms and finally, a real time ECG monitoring application. Moreover, Beat Per Minute (BPM) calculation and comparison of these values with existing ECG devices have been investigated.

Aug 2020
Hasbi Sevinc Presents His Work at IEEE FLEPS 2020

Abstract: One of the main challenges of navigation systems is the inability of orientation and insufficient localization accuracy in indoor spaces. There are situations where navigation is required to function indoors with high accuracy. One such example is the task of safely guiding visually impaired people from one place to another indoors. In this study, to increase localization performance indoors, a novel method was proposed that estimates the step length of the visually impaired person using machine learning models. Thereby, once the initial position of the person is known, it is possible to predict their new position by measuring the length of their steps. The step length estimation system was trained using the data from three separate devices; capacitive bend sensors, a smart phone, and WeWALK, a smartcane developed to assist visually impaired people. Out of the various machine learning models used, the best result obtained using the K Nearest Neighbor model, with a score of 0.945 R^2 . These results support that indoor navigation will be possible through step length estimation.