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

Our lab has been granted a patent certificate from the Turkish Patent and Trademark Office under the number TR 2022 004079 B with his invention titled "Textile-Based Wide Area Pressure Sensing Arrays".

The Soft Sensors Lab team participated in SAHA Expo 2024 from October 22 to 26, where they showcased their innovative products at the Istanbul Technical University (İTÜ) booth. The event offered an excellent opportunity to display their advancements in electronic textiles and IoT technologies to a diverse audience.

The patent titled “Textile Based Wide Area Pressure Sensing Arrays” was the result of the joint work of the founders of the Soft Sensors Laboratory, Assoc. Prof. Dr. Özgür ATALAY from ITU Textile Engineering Department and Assoc. Prof. Dr. Gökhan İNCE from ITU Computer Engineering Department won a bronze medal at the ISIF’24 9th International Invention Fair.

At the “2024-2025 Higher Education Academic Year Opening Ceremony”; the doctoral thesis “An Architectural Research Framework for the Neuroscience of Human Experience” was completed by Dr. Tülay Karakaş under the supervision of Assoc. Prof. Dr. Gökhan İnce, one of Soft Sensors Lab founders, and Assoc. Prof. Dr. Dilek Yıldız Özkan was deemed worthy of the “Fine Arts and Architecture Award” at the YÖK Outstanding Achievement Awards.

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.