Maximizing Performance with Minimal Resources for Real-Time Transition Detection

Assistive devices, such as exoskeletons and prostheses, have revolutionized the field of rehabilitation and mobility assistance. Efficiently detecting transitions between different activities, such as walking, stair ascending and descending, and sitting, is crucial for ensuring adaptive control and enhancing user experience. We here present an approach for real-time transition detection, aimed at optimizing the processing-time performance. By establishing activity-specific threshold values through trained machine learning models, we effectively distinguish motion patterns and we identify transition moments between locomotion modes. This threshold-based method improves real-time embedded processing time performance by up to 11 times compared to machine learning approaches. The efficacy of the developed finite-state machine is validated using data collected from three different measurement systems. Moreover, experiments with healthy participants were conducted on an active pelvis orthosis to validate the robustness and reliability of our approach. The proposed algorithm achieved high accuracy in detecting transitions between activities. These promising results show the robustness and reliability of the method, reinforcing its potential for integration into practical applications.Comment: Submitted for a conference. 7 pages including references, 8 figures, 3 table

Indoor UAV Exploration Method with UWB Localization

Bu çalışmada önceden bilinmeyen iç mekânları keşfedecek ve haritalayacak otonom bir insansız hava aracı (İHA) sunulmuştur. Dört rotorlu hava aracı lazer mesafe ölçücü, sonar mesafe algılayıcı, atalet ölçüm ünitesi (IMU) ve Ultra geniş bant alıcısı (UWB) ile donatılmıştır. Basit bir engelden kaçınma algoritması ve açıklık bulma algoritması gibi çeşitli algoritmalar sunulmuştur. Bu algoritmalar kullanılarak çarpışma olmadan ilerleme sağlayan özgün bir yol bulma/keşif algoritması geliştirilmiştir. Örnek bir iç mekân Gazebo simülatör paketi kullanılarak modellenmiştir. Son olarak geliştirilen keşif algoritmasıyla duvar takip algoritması zamana karşı keşfedilen alan bakımından karşılaştırılmıştır. Tüm simülasyonlar Gazebo’nun Robot İşletim Sistemi (ROS) ile birleştirilmesi ile gerçekleştirmiştir.In this paper, an autonomous UAV which explores and maps unknown indoor environments is presented. The quadrotor is equipped with a laser range finder, a sonar ranger, inertial measurement unit (IMU) and Ultra wideband receiver. Ultra wideband technology is used in the localization and navigation of quadrotor. Several algorithms, including simple obstacle avoidance and opening finder are presented. Therefore, a novel collision free path finding/exploration algorithm is implemented. A sample indoor environment is modeled in Gazebo. Finally, our exploration algorithm is compared with wall-following algorithm in the sample environment in terms of time versus explored area. All the simulations are performed by incorporating Gazebo with Robot Operating System (ROS)

Cystic fibrosis in Turkey: First data from the national registry.

Background Cystic fibrosis (CF) care has been implemented in Turkey for a long time; however, there had been no patient registry. For this purpose, the Turkish National CF Registry was established. We present the first results of registry using data collected in 2017. Methods The data were collected using a data-entry software system, which was accessed from the internet. Demographic and annually recorded data consisted of 15 and 79 variables, respectively. Results There were 1170 patients registered from 23 centers; the estimated coverage rate was 30%. The median age at diagnosis was 1.7 years (median current age: 7.3 years); 51 (4.6%) patients were aged over 18 years. Among 293 patients who were under 3 years of age, 240 patients (81.9%) were diagnosed through newborn screening. Meconium ileus was detected in 65 (5.5%) patients. Genotyping was performed in 978 (87.4%) patients and 246 (25.2%) patients' mutations were unidentified. The most common mutation was deltaF508 with an allelic frequency of 28%, followed by N1303K (4.9%). The median FEV1% predicted was 86. Chronic colonization with Pseudomonas aeruginosa was seen in 245 patients. The most common complication was pseudo-Bartter syndrome in 120 patients. The median age of death was 13.5 years in a total of 15 patients. Conclusions Low coverage rate, lack of genotyping, unidentified mutations, and missing data of lung functions are some of our greatest challenges. Including data of all centers and reducing missing data will provide more accurate data and help to improve the CF care in Turkey in the future

Epidemiological, Clinical, and Laboratory Features of Children With COVID-19 in Turkey

Objectives: The aim of this study is to identify the epidemiological, clinical, and laboratory features of coronavirus disease 2019 (COVID-19) in children