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Normal ankle function provides a key contribution to everyday activities, particularly step/stair ascent and descent, where many falls occur. The rising to up-on-the-toes (UTT) 30 second test (UTT-30) is used in the clinical assessment of ankle muscle strength/function and endurance and is typically assessed by an observer counting the UTT movement completed. The aims of this study are: (i) to determine whether inertial measurement units (IMUs) provide valid assessment of the UTT-30 by comparing IMU-derived metrics with those from a force-platform (FP), and (ii) to describe how IMUs can be used to provide valid assessment of the movement dynamics/stability when performing a single UTT movement that is held for 5 s (UTT-stand). Twenty adults (26.2 ± 7.7 years) performed a UTT-30 and a UTT-stand on a force-platform with IMUs attached to each foot and the lumbar spine. We evaluate the agreement/association between IMU measures and measures determined from the FP. For UTT-30, IMU analysis of peaks in plantarflexion velocity and in FP’s centre of pressure (CoP) velocity was used to identify each repeated UTT movement and provided an objective means to discount any UTT movements that were not completed ‘fully’. UTT movements that were deemed to have not been completed ‘fully’ were those that yielded peak plantarflexion and CoP velocity values during the period of rising to up-on-the-toes that were below 1 SD of each participant’s mean peak rising velocity across their repeated UTT. The number of UTT movements detected by the IMU approach (23.5) agreed with the number determined by the FP (23.6), and each approach determined the same number of ‘fully’ completed movements (IMU, 19.9; FP, 19.7). For UTT-stand, IMU-derived movement dynamics/postural stability were moderately-to-strongly correlated with measures derived from the FP. Our findings highlight that the use of IMUs can provide valid assessment of UTT test(s).

Myocardial infarction with non-obstructive coronary arteries (MINOCA) is defined as stenosis of less than 50% or no stenosis on coronary angiography in a patient diagnosed with myocardial infarction. Telomere length is expressed by studies that it acts as a biomarker, especially for biological aging and cardiovascular diseases. In this study, we aimed to investigate whether there is a relationship between circulating leukocyte telomere length (LTL) and serum lipid values in MINOCA patients. Forty-five newly diagnosed patients with MINOCA were included in the study, along with 45 healthy controls who matched the patients in terms of age and gender. We determined the LTL value using the RT-PCR method. As a result of the study, we found LTL (p< 0.001) and serum lipid values (HDL-cholesterol (p< 0.001), LDL-cholesterol (p< 0.001), triglycerides (p< 0.05), and total cholesterol (p< 0.05)) to be significantly higher in the MINOCA group than in the control group. When the correlation relationship between LTL and lipid values in the MINOCA group was evaluated, a negative correlation was determined only between LTL and HDL (p=0.014, r=-0.362). This is the first study to evaluate telomere length in MINOCA patients in Turkey. Our results support the existence of short telomere length in MINOCA patients.

Gait abnormalities are typically derived from neurological conditions or orthopaedic problems and can cause severe consequences such as limited mobility and falls. Gait analysis plays a crucial role in monitoring gait abnormalities and discovering underlying deficits can help develop rehabilitation programs. Contemporary gait analysis requires a multi-modal gait analysis approach where spatio-temporal, kinematic and muscle activation gait characteristics are investigated. Additionally, protocols for gait analysis are going beyond labs/clinics to provide more habitual insights, uncovering underlying reasons for limited mobility and falls during daily activities. Wearables are the most prominent technology that are reliable and allow multi-modal gait analysis beyond the labs/clinics for extended periods. There are established wearable-based algorithms for extracting informative gait characteristics and interpretation. This paper proposes a multi-layer fusion framework with sensor, data and gait characteristics. The wearable sensors consist of four units (inertial and electromyography, EMG) attached to both legs (shanks and thighs) and surface electrodes placed on four muscle groups. Inertial and EMG data are interpreted by numerous validated algorithms to extract gait characteristics in different environments. This paper also includes a pilot study to test the proposed fusion approach in a small cohort of stroke survivors. Experimental results in various terrains show healthy participants experienced the highest pace and variability along with slightly increased knee flexion angles (≈1°) and decreased overall muscle activation level during outdoor walking compared to indoor, incline walking activities. Stroke survivors experienced slightly increased pace, asymmetry, and knee flexion angles (≈4°) during outdoor walking compared to indoor. A multi-modal approach through a sensor, data and gait characteristic fusion presents a more holistic gait assessment process to identify changes in different testing environments. The utilisation of the fusion approach presented here warrants further investigation in those with neurological conditions, which could significantly contribute to the current understanding of impaired gait.

Wearables are objective tools for human activity recognition (HAR). Advances in wearables enable synchronized multi-sensing within a single device. This has resulted in studies investigating the use of single or multiple wearable sensor modalities for HAR. Some studies use inertial data, others use surface electromyography (sEMG) from multiple muscles and different post-processing approaches. Yet, questions remain about accuracies relating to e.g., multi-modal approaches, and sEMG post-processing. Here, we explored how inertial and sEMG could be efficiently combined with machine learning and used with post-processing methods for better HAR. This study aims recognition of four basic daily life activities; walking, standing, stair ascent and descent. Firstly, we created a new feature vector based on the domain knowledge gained from previous mobility studies. Then, a feature level data fusion approach was used to combine inertial and sEMG data. Finally, two supervised learning classifiers (Support Vector Machine, SVM, and the k-Nearest Neighbors, kNN) were tested with 5-fold cross-validation. Results show the use of inertial data with sEMG increased overall accuracy by 3.5% (SVM) and 6.3% (kNN). Extracting features from linear envelopes instead of bandpass filtered sEMG improves overall HAR accuracy in both classifiers. Clinical Relevance- Post-processing on sEMG signals can improve the performance of multimodal HAR.

This recent Turkish Myocardial Infarction registry reported that guidelines are largely implemented in patients with acute myocardial infarction (MI) in Turkey. We aimed to obtain up-to-date information for short- and midterm outcomes of acute MI. Fifty centers were selected using probability sampling, and all consecutive patients with acute MI admitted to these centers (between November 1 and 16, 2018) were enrolled. Among 1930 (mean age 62 ± 13 years, 26% female) patients, 1195 (62%) had non-ST segment elevation myocardial infarction (NSTEMI) and 735 (38%) had ST segment elevation myocardial infarction (STEMI). Percutaneous coronary intervention (PCI) was performed in 94.4% of patients with STEMI and 60.2% of those with NSTEMI. Periprocedural mortality occurred in 4 (0.3%) patients. In-hospital mortality was significantly higher in STEMI than in patients with NSTEMI (5.4% vs 2.9%, respectively; P = .006). However, the risk became slightly higher in the NSTEMI group at 1 year. Women with STEMI had a significantly higher in-hospital mortality compared with men (11.2% vs 3.8%; P < .001); this persisted at follow-up. In conclusion, PCI is performed in Turkey with a low risk of complications in patients with acute MI. Compared with a previous registry, in-hospital mortality decreased by 50% within 20 years; however, the risk remains too high for women with STEMI.

Gait assessment is emerging as a prominent way to understand impaired mobility and underlying neurological deficits. Various technologies have been used to assess gait inside and outside of laboratory settings, but wearables are the preferred option due to their cost-effective and practical use in both. There are robust conceptual gait models developed to ease the interpretation of gait parameters during indoor and outdoor environments. However, these models examine uni-modal gait characteristics (e.g., spatio-temporal parameters) only. Previous studies reported that understanding the underlying reason for impaired gait requires multi-modal gait assessment. Therefore, this study aims to develop a multi-modal approach using a synchronized inertial and electromyography (EMG) signals. Firstly, initial contact (IC), final contact (FC) moments and corresponding time stamps were identified from inertial data, producing temporal outcomes e.g., step time. Secondly, IC/FC time stamps were used to segment EMG data and define onset and offset times of muscle activities within the gait cycle and its subphases. For investigation purposes, we observed notable differences in temporal characteristics as well as muscle onset/offset timings and amplitudes between indoor and outdoor walking of three stroke survivors. Our preliminary analysis suggests a multi-modal approach may be important to augment and improve current inertial conceptual gait models by providing additional quantitative EMG data.

Wearable inertial measurement units (IMUs) are used in gait analysis due to their discrete wearable attachment and long data recording possibilities within indoor and outdoor environments. Previously, lower back and shin/shank-based IMU algorithms detecting initial and final contact events (ICs-FCs) were developed and validated on a limited number of healthy young adults (YA), reporting that both IMU wear locations are suitable to use during indoor and outdoor gait analysis. However, the impact of age (e.g., older adults, OA), pathology (e.g., Parkinson′s Disease, PD) and/or environment (e.g., indoor vs. outdoor) on algorithm accuracy have not been fully investigated. Here, we examined IMU gait data from 128 participants (72-YA, 20-OA, and 36-PD) to thoroughly investigate the suitability of ICs-FCs detection algorithms (1 × lower back and 1 × shin/shank-based) for quantifying temporal gait characteristics depending on IMU wear location and walking environment. The level of agreement between algorithms was investigated for different cohorts and walking environments. Although mean temporal characteristics from both algorithms were significantly correlated for all groups and environments, subtle but characteristically nuanced differences were observed between cohorts and environments. The lowest absolute agreement level was observed in PD (ICC2,1 = 0.979, 0.806, 0.730, 0.980) whereas highest in YA (ICC2,1 = 0.987, 0.936, 0.909, 0.989) for mean stride, stance, swing, and step times, respectively. Absolute agreement during treadmill walking (ICC2,1 = 0.975, 0.914, 0.684, 0.945), indoor walking (ICC2,1 = 0.987, 0.936, 0.909, 0.989) and outdoor walking (ICC2,1 = 0.998, 0.940, 0.856, 0.998) was found for mean stride, stance, swing, and step times, respectively. Findings of this study suggest that agreements between algorithms are sensitive to the target cohort and environment. Therefore, researchers/clinicians should be cautious while interpreting temporal parameters that are extracted from inertial sensors-based algorithms especially for those with a neurological condition.

The mechanical output at the ankle provides key contribution to everyday activities, particularly step/stair ascent and descent. Age-related decline in ankle functioning can lead to an increased risk of falls on steps and stairs. The rising up-on-the-toes (UTT) 30-second test (UTT-30) is used in the clinical assessment of ankle muscle strength/function and endurance; the main outcome being how many repetitive UTT movements are completed. This preliminary study describes how inertial measurement units (IMUs) can be used to assess the UTT-30. Twenty adults (26.2 ± 7.7 years) performed a UTT-30 at a comfortable speed, with IMUs attached to the dorsal aspect of each foot. Use of IMUs' angular velocity signal to detect the peak plantarflexion angular velocity (p-fAngVel

BACKGROUND: Atrial fibrillation (AF) prevalence in patients with acute myocardial infarction (MI) ranges from 3% to 25%. However demographic, clinical, and angiographic characteristics of AF patients who admitted with de novo MI are unclear. The aim of this study was to investigate the prevalence of patients presenting with de novo MI with AF. METHODS: The study was performed as a sub-study of the MINOCA-TR (Myocardial Infarction with Non-obstructive Coronary Arteries in Turkish Population) Registry, a multicenter, cross-sectional, observational, all-comer registry. MI patients without a known history of stable coronary artery disease and/or prior coronary revascularization were enrolled in the study. Patients were divided into AF and Non-AF groups according to presenting cardiac rhythm. RESULTS: A total of 1793 patients were screened and 1626 were included in the study. The mean age was 61.5 (12.5) years. 70.7% of patients were men. The prevalence of AF was 3.1% (51 patients). AF patients were older [73.4 (9.4) vs. 61.0 (12.4) years, p

Wearable technologies (wearables) have emerged as useful tools in the assessment of individuals within controlled settings. By utilizing wearables during assessments, objective digital data can be used to better inform clinical decisions. Of recent pragmatic interest has been the emergence of inertial sensor-based wearables (e.g., accelerometers, gyroscopes) that can provide high resolution temporal, spatial, and frequency-based outcomes that could be used to better quantify balance, gait, and other physical functioning tasks. This chapter explores the background to the instrumentation of physical functioning tasks, with the fundamentals required to capture and analysis inertial signals.

The Sports Concussion Assessment Tool (SCAT) is a pen and paper-based evaluation tool for use by healthcare professionals in the acute evaluation of suspected concussion. Here we present a feasibility study towards instrumented SCAT (iSCAT). Traditionally, a healthcare professional subjectively counts errors according to SCAT marking criteria matrix. It is hypothesized that an instrumented version of the test will be more accurate while providing additional digital-based parameters to better inform player management. The feasibility study focuses on the SCAT physical functioning tasks only: double leg stance, single-leg stance, tandem stance and tandem gait. Amateur university rugby players underwent iSCAT testing and data were recorded with 8 inertial units attached at different anatomical locations. Video data were gathered simultaneously as reference. An iSCAT algorithm was used to detect errors and quantify additional concussion-based time and frequency domain parameters to assess participant stability during balance and gait tasks. Future work aims to instrument other SCAT features such as hand-eye coordination while deploying methods within a large concussion project.

In this study, a CFD start-up model has been built after conducting the sensitivity studies to evaluate the self-starting behaviour of the H-type vertical axis wind turbines (VAWTs). The self-starting behaviour of a well-investigated VAWT is used for the model validation, and then the details of aerodynamics of the start-up process have been examined. Finally, the effect of the moment of inertia and the blade number on the aerodynamic behaviour of the self-starting and power performance of the H-type VAWT are analysed. It has been found that in the critical region, where TSR

Gait assessment is an essential tool for clinical applications not only to diagnose different neurological conditions but also to monitor disease progression as it contributes to the understanding of underlying deficits. There are established methods and models for data collection and interpretation of gait assessment within different pathologies. This narrative review aims to depict the evolution of gait assessment from observation and rating scales to wearable sensors and laboratory technologies and provide limitations and possible future directions in the field of gait assessment. In this context, we first present an extensive review of current clinical outcomes and gait models. Then, we demonstrate commercially available wearable technologies with their technical capabilities along with their use in gait assessment studies for various neurological conditions. In the next sections, a descriptive knowledge for existing inertial and EMG based algorithms and a sign based guide that shows the outcomes of previous neurological gait assessment studies are presented. Finally, we state a discussion for the use of wearables in gait assessment and speculate the possible research directions by revealing the limitations and knowledge gaps in the literature.

This paper presents a comparison of the success of Fuzzy Logic Control (FLC), Particle Swarm Optimization (PSO) based PID and classic PID over speed control of DC motors. Motors known as a component that convert electrical energy to mechanical energy are quite common in our devices. DC motors are one of the most used components of industry. DC motors have two important advantages as speed variation and torque. Generally, speed variation is performed by changing armature voltage. In this paper, speed control of a DC motor and mathematical model of DC motor were presented. The performances of the control methods used in this study in DC motor speed control have been examined and supported with graphs by testing under different inputs, noise and load at certain times. The performance measures of the controllers were evaluated taking into consideration the rise time, the settling time and the overshoot., Bu çalışma Bulanık kontrol, Parçacık Sürü Optimizasyon tabanlı PID ve klasik PID kontrol yöntemlerinin DA motorların hız kontrolü üzerindeki başarımlarının karşılaştırılması çalışmalarını sunmaktadır. Motorlar elektrik enerjisini mekanik enerjiye çeviren elemanlar olarak bilinirler ve kullanıdığımız bir çok cihazda yaygın bir şekilde bulunurlar. DA motorları endüstride yaygın olarak kullanılan elemanlardır. DA motorların, hız değişimi ve tork olarak iki önemli parametresi mevcuttur. Genellikle hız değişimi armatür voltajına bağlıdır. Bu çalışmada DA motorun hız kontorlü üzerinde çalışmalar yapılmış, matematiksel modeli gösterilmiş. Bu çalışmada kullanılan denetim yöntemlerinin DA motor hız kontrolündeki başarımları belirli zamanlarda farklı girişler, gürültü ve yük altında test edilerek incelenmiş ve grafiklerle desteklenmiştir. Denetleyicilerin performans ölçümleri yükselme zamanı, yerleşme zamanı ve aşım dikkate alınarak değerlendirilmiştir.

The aim of this study is to investigate the potential of a novel design considering low shear horizontal bioreactor (LSB-R) with counter rotation U-shape impellers. Bioreactors (LSB-R and STR) were compared under the same mixing time (23 ± 2 s) using Computational Fluid Dynamics analysis focusing on the shear stresses. The highest shear stress was around the impeller tips with an average shear stress found to reach up to 0.8 Pa for LSB-R a very small value compared to the 100-fold increase inside the STR. Cell wall deficient and motility impaired mutants of Chlamydomonas were used in the experiments to see the effects of shear stress on growth and morphology. Biomass productivities were almost doubled, and specific growth rates were increased by LSB-R. On the other hand, the motility impaired cells aligned as colonies to protect from shear forces in STR and a rapid death occurred in cells with flagella due to continuous deflagellation and cell damage. The results indicate that; LSB-R design has great advantage on lowering shear stress, eliminating excessive hydrodynamic forces, providing mild operation conditions. © 2021 Elsevier B.V., 118M596, The LSB-R design modified for animal cell culture has been patent pended (Turkish Patent Institute, Patent Number: 2015/14967). This study was done by Ege University Bioengineering Department Microalgal Bioprocess Research Group. LSB-R is now under on trial for the spheroid production from HepG2 cell lines for bioactivity testing and spheroid formation under low shear forces (TUBITAK, Project no: 118M596).

In this study, a real-time system which counts the number of people with the help of a camera was demonstrated. The system can send the number of people to a mobile application via Internet of Things (IoT) and monitor simultaneously. This work is carried out in the main library of Inonu University. Background subtraction method was used to recognize moving humans on the visual field of the camera. According to motion information of humans, a counter was used to count the number of people in the saloon by determining whether going inside or outside. The counter will inform the users about what percentage of the saloon is empty. Matlab and Thingspeak combination help to send counter information to internet environment. A mobile application was used to track the counter information from Android and iOS smartphones. The results were presented in Matlab environment and mobile application simultaneously. Thanks to this work, students will not have to look for a place to work when the library is crowded and will not bother other working students. It is believed that this project will serve all students.

This paper presents an application of human tracking suitcase with the help of a small camera. This application aimed to make the travels more comfortable. The heavy suitcases can be difficult to carry for most of us and disabled. Under favor of this application, people can travel without thinking about heavy suitcases anymore. This application contains two technical stages as image processing and control. Motor control of the suitcase has been done by using motion information. The suitcase can track colorful objects with the help of the camera which is located on the handlebar. In this project, we developed a tracking algorithm that gives better results than single color tracking. Distance sensors are also used to escape from obstacles. This travel mate is also trustable because when the unit cannot catch the person anymore, the alarm starts to warn the owner. As a result, when a suitcase combined with this designed unit, suitcases are no longer a problem to carry during the travel.

Today, one of the most common types of cancer is breast cancer. It is crucial to prevent the propagation of malign cells to reduce the rate of cancer induced mortality. Cancer detection must be done as early as possible for this purpose. Machine Learning techniques are used to diagnose or predict the success of treatment in medicine. In this study, four different machine learning algorithms were used to early detection of breast cancer. The aim of this study is to process the results of routine blood analysis with different ML methods and to understand how effective this method is for detection. Methods used can be listed as Artificial Neural Network (ANN), standard Extreme Learning Machine (ELM), Support Vector Machine (SVM) and K-Nearest Neighbor (k-NN). Dataset used were taken from UCI library. In this dataset age, body mass index (BMI), glucose, insulin, homeostasis model assessment (HOMA), leptin, adiponectin, resistin and chemokine monocyte chemoattractant protein 1 (MCP1) attributes were used. Parameters that have the best accuracy values were found by using four different Machine Learning techniques. For this purpose, hyperparameter optimization method was used. In the end, the results were compared and discussed.

Real-time robots are quite common in our daily life. These robots are working as a part of the process in industry or a medical assistance in hospitals to serve humanity. Designing the robots according to the desired referent and making the given tasks with high accuracy makes them more and more popular in these days. In this work, the designed two-wheeled balancing robots with integrated camera track object autonomously. This work has two important stages. The first stage is about balancing the robot with the angle information taken from IMU sensor and implementation of PID control. IMU sensors create lots of noisy signals because of its natural structures. Kalman filter was used to denoise these noisy signals to have a smooth signal for a better balance control. The second stage is about image processing and objects recognition. This section was completed by using Matlab Image Processing Toolbox which can be used Arduino microcontroller board synchronously. In this section, algorithm infers motion information of objects. Motors were controlled according to motion information of moving objects. In the end, an object tracker self-balance robot was constructed. Balance control of the robot was managed by PID controller and accelerometer signals were denoised by a Kalman Filter. It was clarified that using PID controller and Kalman Filter together have a positive effect to balance the robot on the desired angle.

This paper presents a method to control pointer with the help of camera for a person who is not able to control the pointer with hands. Physical problems result from brain paralysis are the most dominant obstacles for pointer control . This paper aims to control pointer with the help of human-computer interaction technologies instead of hands. The proposed design is similar to common pointer control. According to disability condition, people can choose the usage of the system because of the adjustability of the system. One can control the pointer by wearing a colored wristband or any item on body parts like arms, feet or any other moving part of the body. The designed system requires software, a camera, and a table lamp. The working principle of the system is inferring information from camera and transforming this information to pixels to control the pointer on the screen. In this paper, computer vision system was combined with Java class to control the pointer. When the camera gets the first snapshot from a real-time video, first, the software finds the location of the colored item on the screen then moves the pointer towards to location. In this real-time system, clicking is a problem because of the physical inabilities. In order to solve this problem, we use another colored item on the field vision of the camera. According to the location of the second object, the software decides about left or right clicks.

In this study, a real-time system design, which can track (RGB) targets in dynamic environments with an active camera, was implemented. Object tracking applications are quite important for military, surveillance system and operational robot applications and getting more important day by day. This design allows us tracking an object using fewer cameras. The design consists of three main parts that are object detection, mapping, tracking the object. When the camera catches the object, first it detects the shape of the object and creates bounding box, according to bounding box information, the algorithm calculates the centroid of the object. Object coordinates are determined using centroid of the object then tracking process works by activating motors via Arduino-MATLAB communication. The motors placed on a platform called pan-tilt platform. The platform can turn 270 and 180 degrees on×and y-axis respectively.

Elektromiyografi (EMG) kaslarimizin fiziksel durumu hakkinda bizleri bilgilendiren bir olcum teknigidir. Gunumuzde doktorlar, fizik tedaviciler hastalik teshislerinde ve protez kol yapiminda yaygin bir sekilde bu metodu kullanmaktadirlar. EMG sinyalleri kaslarin icerisindeki maddelerin iyon farklarindan kaynakli belirli bir potansiyel farka sahiptirler ve bu sinyaller (mV) seviyesindedir. Bu sinyaller cok kucuk olduklarindan dolayi hassastirlar ve gurultu kapma olasiliklari ortam kosullarina bagli olarak degismektedir. Kirik kablolar, kirli elektrotlar, floresan lamba gibi dis etkiler sinyallerde bozulmalara yol acabilmektedir. Bu yuzdendir ki EMG isaretlerinin filtrelenmesi oldukca onemlidir. Bu makalede filtreleme islemleri Cok Hizli En Kucuk Kareler Metodu ve Dalgacik Donusumu metotlari olmak uzere iki farkli yolla yapilmis ve sonuclar karsilastirilmistir