We are proud to announce that Associate Professor Dr. Özgür Atalay, one of the co-founders of our lab, has been nominated for the prestigious Study UK Alumni Awards 2025 in the Science and Sustainability category. This recognition highlights his outstanding contributions to the field of smart textiles, wearable electronics, and soft robotics.

Assoc. Prof. Dr. Özgür Atalay, our cofounder and recipient of an ERC Starting Grant, became the only researcher from our country to be awarded support under the ERC 2024 Proof of Concept call with his project titled "TexSoRVA: Textile-Based Soft Robotics for Virtual Reality Applications."

Our research team attended the “2025 IEEE/SICE International Symposium on System Integration” held in Germany between January 21-24, 2025. In the “Textiles and Robotics Workshop” session of the symposium; Assoc. Prof. Dr. Özgür ATALAY made his presentation titled “The Manufacturing of Textile-Based Soft Robotics for Wearable Applications” and Asst. Prof. Dr. Aslı TUNÇAY ATALAY made her presentation titled “Textile-Based Sensors for Soft Robotic Applications”. In the demo session, developed technologies were shown to the attendees.

Members of the Soft Sensors Lab, which specializes in electronic textiles and soft robotics, represented our university at the STRATCOM event organized by the Directorate of Communications of the Republic of Turkey. Dr. Kadir Özlem, a research assistant in our faculty, and Çağatay Gümüş, a research assistant in the Department of Textile Engineering, participated in the event, where they showcased their cloud-based telerehabilitation application.

You can access the details of our interview with Anadolu Agency about “STEWART - Sports acTivitiEs With weARable Technology”, one of the projects carried out in our laboratory, from the link below

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

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.

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.

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.