Elektrik-Elektronik Mühendisliği Bölümü / Department of Electrical and Electronics Engineering
Permanent URI for this collectionhttps://hdl.handle.net/11413/6818
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Publication Open Access IoT-Based Fire Detection: A Comparative Study of Machine Learning Techniques(Niğde Ömer Halisdemir Üniversitesi, 2024) AYRANCI, AHMET AYTUĞ; Erkmen, BurcuFires that cannot be detected quickly become uncontrollable. The fires that start to spread uncontrollably pose a significant danger to humans and natural life. Especially in public and crowded areas, fires can lead to possible loss of life and massive property damage. Because of this, it is necessary to detect fires as accurately and quickly as possible. Smoke detectors used with Internet of Things (IoT) technology can exchange data with each other. In this study, data collected from two different types of IoT-based smoke detectors were processed using machine learning algorithms. The k-Nearest Neighbor (k-NN), Multilayer Perceptron (MLP), Radial Basis Function (RBF) Network, Naïve Bayes (NB), Decision Tree (DT), Random Forest (RF), and Logistic Model Tree (LMT) algorithms were used. The data obtained from the smoke detectors were processed using machine learning algorithms to create a highly successful model design. The aim of the study is to design an artificial intelligence-based system that enables the early detection of fires occurring both indoors and outdoors.Publication Metadata only Analyzing the Operations at a Textile Manufacturer’s Logistics Center Using Lean Tools(Springer Science and Business Media Deutschland GmbH, 2024) GÜNAY, AHMET CAN; ÖZBEK ,ONUR; MUTLU, FİLİZ; AKTİN, AYŞE TÜLİNCompliance with delivery times is crucial for businesses in the logistics sector. Numerous research has been conducted to improve distribution performance. Many of these studies touch on lean production as well. The strategies used in lean manufacturing are often employed by businesses and have a positive impact on performance. This study focuses on the overseas shipping department of a textile company’s logistics center. Workflow starts with product acceptance from manufacturers and ends with shipment to customers abroad. After a thorough examination, some bottlenecks that increase delivery times are observed. Value Stream Mapping (VSM), which is a lean manufacturing technique, is chosen as the main method to be used. It aims to determine value added and non-value-added activities, resulting in minimizing or eliminating the non-value-added ones. Initially, necessary data are gathered through workshops and interviews, and observations on Current State VSM are made. During these workshops, various improvements are proposed and evaluated together with the company’s engineers. After takt time and cycle time calculations, label change station is identified as the bottleneck. In the next step, Kaizens are suggested for the stations, and some lean techniques are employed to solve different workflow problems. Finally, short-term applicability of proposed improvements is discussed, and Future State VSM is drawn. It can be concluded that significant improvements are achieved especially in lead time, changeover time, productivity rate and production speed. By reducing or eliminating non-value-added activities and identifying deficiencies that slow process flow, a standard, sustainable and developable process is proposed to the company. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Publication Restricted Edge Computing and Robotic Applications in Modern Agriculture(IEEE-Inst Electrical Electronics Engineers Inc., 2024) AYRANCI, AHMET AYTUĞ; Erkmen, BurcuThe modernization of agricultural practices prominently features robotics as a key technology. Efforts are concentrated on achieving automation and enhancing efficiency in agriculture through advancements in robotic applications. The widespread integration of remote sensing systems into agricultural areas facilitates real-time information acquisition, enabling drones and robots to operate with enhanced efficiency and effectiveness. Robotics plays a crucial role in the evolution of agriculture 4.0 and agriculture 5.0 strategies, marking significant strides in agricultural technology. Specifically designed robots for agricultural use are currently employed in tasks like planting, fertilizing, irrigating, pest controlling, and harvesting, proving a certain level of effectiveness. Edge computing is crucial in enhancing efficiency and sustainability within modern agricultural practices. Edge computing can instantly process data from numerous devices, mitigating network congestion effectively. In modern agricultural applications, it is possible to perform multiple tasks in a coordinated and effective manner by using Unmanned Aerial Vehicle (UAV) and Unmanned Ground Vehicle (UGV) together. These devices can serve as both data collection and edge devices in the network. Multiple agricultural robot applications and benefits of these applications are explained in the study. © 2024 IEEE.Publication Metadata only Sentiment Analysis of Tweets on Online Education During COVID-19(Springer Science and Business Media Deutschland GmbH, 2023) YAZGAN, HARUN; ÖZBEK, ONUR; GÜNAY, AHMET CAN; AKBULUT, FATMA PATLAR; ELİF, YILDIRIM; KOCAÇINAR, BÜŞRA; ŞENGEL, ÖZNURThe global coronavirus disease (COVID-19) pandemic has devastated public health, education, and the economy worldwide. As of December 2022, more than 524 million individuals have been diagnosed with the new coronavirus, and nearly 6 million people have perished as a result of this deadly sickness, according to the World Health Organization. Universities, colleges, and schools are closed to prevent the coronavirus from spreading. Therefore, distance learning became a required method of advancing the educational system in contemporary society. Adjusting to the new educational system was challenging for both students and instructors, which resulted in a variety of complications. People began to spend more time at home; thus, social media usage rose globally throughout the epidemic. On social media channels such as Twitter, people discussed online schooling. Some individuals viewed online schooling as superior, while others viewed it as a failure. This study analyzes the attitudes of individuals toward distance education during the pandemic. Sentiment analysis was performed using natural language processing (NLP) and deep learning methods. Recurrent neural network (RNN) and one-dimensional convolutional neural network (1DCNN)-based network models were used during the experiments to classify neutral, positive, and negative contents.Publication Metadata only VDIBA-Based Current-Mode PID Controller Design(World Scientific Publishing Co Pte Ltd., 2023) ORUÇOĞLU, UMUT CEM; Özer, Emre; Kaçar, FıratThis paper aims to bring a voltage differencing inverting buffered amplifier (VDIBA)based current-mode (CM) proportional integral derivative (PID) controller circuit. This CM PID controller is designed with a single VDIBA, three resistors, and two grounded capacitors. The proposed circuit is easy to design, and the control parameters can be tuned without changing the design configuration. A sensitivity analysis of the control parameters to electronic components has been conducted. The Simulation Program with Integrated Circuit Emphasis (SPICE) simulation has been performed using Taiwan Semiconductor Manufacturing Company (TSMC) 0.18 mu m complementary metal-oxide semiconductor (CMOS) technology parameters. An application circuit example is given to demonstrate the reliability of the proposed PID design. A comparison table of the PID controllers previously reported in the literature is also presented.Publication Restricted Quality Factor Based Transducer Power Gain Expression(Emerald Group Publishing Ltd., 2023) ŞENGÜL, METİNPurposeIn the literature, while designing broadband matching networks, transducer power gain (TPG) is used to measure the transferred power. Generally, in TPG expressions, load and back-end impedances of the matching network are used. This study aims to derive a new quality factor-based TPG expression. Design/methodology/approachIn deriving the new expression, narrowband L type-matching network design approach is used and the new expression in terms of back-end quality factor, load quality factor and output port quality factor is obtained. Then, a broadband-matching network design approach using the derived TPG expression is proposed. FindingsTwo broadband double-matching networks are designed by using the proposed design approach using the derived TPG expression. Performances of the designed-matching networks are compared with the performances of the matching networks designed by means of simplified real frequency technique which is a well-known technique in the literature, and it is shown that they are nearly the same. Originality/valueIn broadband-matching problems, generally an impedance-based TPG expression is used, and it must be satisfied by the designed broadband-matching networks. But, in the literature, there is no quality factor-based TPG expression that can be used in broadband-matching problems. So, this gap in the literature has been filled by this paper.Publication Restricted Decentral Smart Grid Control System Stability Analysis Using Machine Learning(Institute of Electrical and Electronics Engineers Inc., 2022) AYRANCI, AHMET AYTUĞ; İlhan, HacıElectrical Grid Systems transmit power produced from various facilities to end-users. Supply and demand must be in balance to achieve secure and stable use in the power grid. To ensure this stability, the amount of electricity fed into the system must always be the same as the amount of demand. High demand makes electrical grid systems' stability more important than ever. Current electrical infrastructures are hard to adapt to these needs. A smart grid system enables two-way electricity flow according to the demand from end-users. Digital communication in smart grid systems enables the system to detect demands, problems, and changes. Also collects information to ensure stability in the system. This study is using the Electrical Grid Stability data set shared at UC Irvine (UCI) Machine Learning repository. Multi-Layer Perceptron (MLP), Radial Basis Function (RBF) Network, K-Nearest Neighbors (K-NN), and Naïve Bayes (NB) Machine Learning (ML) algorithms were used to examine the stability performance of the Smart Grid system. Acquired performance metrics compared using Accuracy, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and F-Score. According to the results obtained, the system and its performance are interpreted. © 2022 IEEE.Publication Restricted Global Impact of the Pandemic on Education: A Study of Natural Language Processing(Institute of Electrical and Electronics Engineers Inc., 2022) AYAZ, TEOMAN BERKAY; USLU, MUHAMMED SAFA; AĞCABAY, İBRAHİM; AHMED, FARUK; KORKMAZ, ÖMER FARUK; KÜREKSİZ, MESUT; ULUÇAM, EMRE; YILDIRIM, ELİF; KOCAÇINAR, BÜŞRA; AKBULUT, FATMA PATLARSchool closures due to the Covid-19 pandemic have changed education forever and we have witnessed the rise of online learning platforms. The education units of the countries made great efforts to adapt to this new order. The expanding, quick spread of the virus and careful steps have prompted the quest for reasonable choices for continuing education to guarantee students get appropriate education and are not impacted logically or mentally. Different methods were attempted to understand how students were affected by this big change. In addition to the significance of traditional surveys and consulting services, the utilization of social media analysis is used as a supportive approach. This paper analyzes the feedback of students on social media via tweets. Deep sentiment analysis is employed to identify embedded emotions such as negative, neutral, and positive. We also aimed to classify irrelevant tweets as the fourth category. Our experiments showed that the tweets are mostly biased toward negative emotions. © 2022 IEEE.Publication Restricted Capacity Loss Analysis Using Machine Learning Regression Algorithms(IEEE, 2022) Atay, Sergen; AYRANCI, AHMET AYTUĞ; Erkmen, BurcuIn this study, time dependent measurements of the power capacitor, which is the main equipment of a compensation unit, are given. The power capacitor is actively working in an industrial facility. Six months of the data from this capacitor were recorded and tests were carried out using Machine Learning (ML) algorithms for its remaining useful life. ML algorithms were selected from the algorithms that used for regression problems. In the study, Support Vector Machine (SVM), Linear Regression (LR) and Regression Trees (RT) algorithms were used. The rated powers of the analyzed capacitor are 50kVAR and 25kVAR from the active plant. The data set was created by running the capacitor continuously for 6 months and the capacity loss was examined with using ML algorithms. The algorithm that gives the best result in the regression analyzes is the LR algorithm. With the results obtained, it is possible to analyze how long the useful life of capacitors with the same characteristics have under the same stress.Publication Restricted Heterofonik Türk Makam Müziginde İşitsel Melodi Kestirimi(Enstitute of Electrical and Electronics Engineers Inc., 2021) ŞİMŞEK, BERRAK ÖZTÜRKIn this study, the Improved Variable Mode Decomposition Method (IVMD) is proposed for the estimation of the audio melody in heterophonic works that constitute the general texture of Turkish maqam music. In our study, the fundamental frequencies of the records belonging to huzzam, kurdilihicazkar, ussak, and rast maqams were estimated by using the IVMD method. Since the basis of the heterophonic texture is that the same melody is performed by more than one instrument, the estimated fundamental frequencies are more than one for each time window. After the multiple frequency estimation, in order to obtain the audio melody of the music recording and therefore a single frequency line, the selection of the frequencies belonging to the audio melody line from the fundamental frequencies was made. The study has been compared with the methods widely used in the analysis of polyphonic music works such as YIN and MELODIA. When the comparisons were evaluated on the basis of maqam and mixture according to the MIREX criteria, successful results were obtained with the IVMD method. © 2021 IEEE.Publication Restricted Design and Realization of an Automatic Optical Inspection System for PCB Solder Joints(Institute of Electrical and Electronics Engineers Inc., 2021) ÇALIŞKAN, AYHAN; GÜRKAN, GÜRAYRecent developments in electronics has led to an increase in fabrication and assembly speeds of printed circuit boards (PCBs). In addition, the size of manufactured PCBs and electronic components (e.g. resistors, capacitors, transistors etc.) are becoming much smaller. By increased demand and production speed, the reliability and thus the inspection of manufactured PCB assemblies became an important issue. In the assembly PCB production process, detection of surface mount technology (SMT) solder defects is made with automatic optical inspection (AOI) devices using image processing methods. Besides these expensive device methods, the method by which the controls are made by the operators visually is a low-cost solution used by most of the companies that manufacture printed circuit board assembly. In addition, circuit defects and solder defects cannot be tested and controlled with 100% accuracy due to human error. This paper proposes to detect solder joint defects with machine learning methods using YOLO algorithm to speed up time and increase accuracy in assembly PCB production line. Approximately 40000 images were obtained from the real production line before training with the YOLOv4 algorithm for high accuracy rate. Detection of solder defects of SMT circuit elements in approximately 5K (4056x3040) images resolution can be achieved with 97% accuracy in around 4 seconds. As a result of the use of the system, this proposed method has been proven with the reports received from the production line and precision-recall curves. Thus, it has been observed that the production speed and accuracy rate are increased. © 2021 IEEE.Publication Metadata only Wide-Band Gain Enhancement of a Pyramidal Horn Antenna with a 3D-Printed Epsilon-Positive and Epsilon-Near-Zero Metamaterial Lens(Cambridge University Press, 2021) Keskin, Nesem; AKŞİMŞEK, HÜSEYİN SİNAN; Turker Tokan, NurhanIn this article, we present a simple, low-cost solution for the gain enhancement of a conventional pyramidal horn antenna using additive manufacturing. A flat, metamaterial lens consisting of three-layer metallic grid wire is implemented at the aperture of the horn. The lens is separated into two regions; namely epsilon-positive and epsilon-near-zero (ENZ) regions. The structure of the ENZ region is constructed accounting the variation of relative permittivity in the metamaterial. By the phase compensation property imparted by the metamaterial lens, more focused beams are obtained. The simulated and measured results clearly demonstrate that the metamaterial lens enhances the gain over an ultra-wide frequency band (10-18 GHz) compared to the conventional horn with the same physical size. A simple fabrication process using a 3D printer is introduced, and has been successfully applied. This result represents a remarkable achievement in this field, and may enable a comprehensive solution for satellite and radar systems as a high gain, compact, light-weighted, broadband radiator.Publication Open Access Determination of Respiratory Parameters by Means of Hurst Exponents of the Respiratory Sounds and Stochastic Processing Methods(IEEE-Institute of Electrical and Electronics Engineers Inc., 2021) SAATÇI, ESRA; SAATÇI, ERTUĞRULObjectives: System approach to the human respiratory system and input/output signals which characterize the system properties were not explored in detail in the literature. The aim of this study is to propose a combination of methods to investigate the indirect relationship between the fractal properties of Respiratory Signals (RS) and Respiratory Sound Signals (RSS) and the clinically measured respiratory parameters. Methods: We used Hurst exponent to reveal the fractal properties of RS and RSS and to estimate the pressures in the respiratory system. The combination of well-known statistical signal processing methods and optimization were applied to the experimentally acquired 23 records. Pearson correlation coefficient and Bland-Altman analysis were the chosen validation methods. Results: Considerable amounts of Hurst exponent values of RSS were found to be between 0.5 and 1, which means increasing trend or decreasing trend can be seen in RSS with fractional Gaussian process properties. Results of the pressure estimator revealed that internal pressure due to tissue viscoelasticity is higher than the pressure due to static elasticity. Feature power and skewness also provided distinctive results for all recordings. Conclusion: Hurst exponent values of the RSS are fruitful representation of the signals which bring the underlaying system characteristics into the surface. We illustrated that required number of sensors can be reduced in the feature calculation to ease implementation effort on the hardware of the handheld devices. Significance: Bland-Altman plots were very successful to demonstrate the connection between the sets of measured respiratory parameters and calculated features.Publication Open Access Speaker Accent Recognition Using MFCC Feature Extraction and Machine Learning Algorithms(Marmara Üniversitesi, Fen Bilimleri Enstitüsü, 2021) AYRANCI, AHMET AYTUĞ; Atay, Sergen; Yıldırım, TülaySpeech and speaker recognition systems aim to analyze parametric information contained in the human voice and recognize it at the highest possible rate. One of the most important features in the audio signal for the speaker to be recognized successfully by the system is the speaker's accent. Speaker accent recognition systems are based on the analysis of patterns such as the way the speaker speaks and the word choice he uses while speaking. In this study, the data obtained by the MFCC feature extraction technique from voice signals of 367 speakers with 7 different accents were used. The data of 330 speakers in the data set were taken from the "Speaker Accent Recognition" data set in the UC Irvine Machine Learning (ML) open data source. The data of the other 37 speakers were obtained by converting the voice recordings in the "Speaker Accent Archive" data set created by George Mason University into data using the MFCC feature extraction technique. 9 ML classification algorithms were used for the designed speaker accent recognition system. Also, the k-fold cross-validation technique was used to test the data set independently. In this way, the performance of ML algorithms is shown when the data set is divided into a k number of parts. Information about the classification algorithms used in the designed system and the hyperparameter optimizations made in these algorithms are also given. The success performances of the classification algorithms are shown with performance metrics.Publication Open Access Sub-Block Aided OFDM with Index Modulation(Bajece (İstanbul Teknik Üniversitesi), 2019) ACAR, YUSUFRecently, orthogonal frequency division multiplexing (OFDM) with index modulation (IM) has been appeared as a novel method for future wireless communication systems. However, such a mechanism has low spectral efficiency since some sub-carriers are not activated in order to implicitly convey information. In this paper, a subblock dependent approach, called sub-block aided OFDMIM (SA-OFDM-IM) technique, is proposed for spectral efficiency enhancement of the OFDM-IM method with lower complexity. The simulation results illustrate that the proposed SA-OFDM-IM and well known OFDM-IM have same bit error rate (BER) performance while SA-OFDMIM has 40% more spectral efficiency with low complexity.Publication Open Access Multifractal Behaviour of Respiratory Signals(AVES Yayıncılık, İstanbul Üniversitesi-Cerrahpaşa, 2020) SAATÇI, ERTUĞRUL; SAATÇI, ESRAIn this study, to analyze the biomedical signals emerging from fractal structures in the human body, fractal analysis was used. Respiratory signals, such as airflow, mouth pressure, and lung volume, comprise a complex relationship that has not been inspected to date. Furthermore, the mechanism for which it is linked to the lung’s fractal structure has not been scrutinized to date. Thus, using a well-known method, known as multifractal detrended fluctuation analysis (MF-DFA), this study aims to determine both mono- and multi-fractal property of respiratory signals ,. The real signals were analyzed using the MF-DFA algorithm. Moreover, for different scales, generalized Hurst exponent values were calculated. The results demonstrated that respiratory signals are fractional Brown motion-type signals, whereas fractal properties demonstrate less intersubject change. Moreover, in addition to both airflow and lung volume, respiratory signals and sounds are multifractal signals. In conclusion, the presence of the lung’s long-memory property is the primary reason of multifractality.Publication Open Access Design of 24-28 GHz band 5G Antenna Based on Symmetrically Located Circular Gaps(Osman Sağdıç, 2020) ÖZPINAR, HÜRREM; AKŞİMŞEK, HÜSEYİN SİNAN5G (fifth generation) cellular system is expected to work in a wide frequency range to meet the demand for mobile services and applications. Antennas will be addressed to the future 5G applications should pose superior characteristics, such as high gain and ultra-large bandwidth response by considering atmospheric absorption/free-space path loss on planned millimeter-wave frequency range of 5G communications. Therefore, antenna design for the future 5G applications is a challenging process. In this article we present a high-gain, broadband mm-Wave antenna based on a circular patch structure with a ground plane and resonator gaps. The designed antenna is analyzed using a widely used full-wave electromagnetic solver. The major antenna figure-of-merits including reflection coefficient, VSWR (voltage-standing wave ratio), antenna patterns in E- and H-planes, surface current distribution, antenna directivity and maximum gain, are obtained. The simulation results show that the gapped circular patch based design has the S11 response less than −10 dB in the frequency range of 21.6-28.8 GHz, which includes 24-28 GHz band of 5G cellular systems. Moreover, it is observed that the symmetrically located circular gaps on both top and bottom layers decrease the side lobe level under −10 dB value, and enhance the gain. We attribute the improvement in the antenna performance to the created current regions due to gaps hosting large vortex current distributions. With 10 mm × 13mm surface area, the proposed antenna demonstrates the peak gain of 9.44 dBi and the radiation efficiency of over 85%. High gain and compact size make this antenna suitable for coming 5G devices.Publication Open Access Akciğer Basınçlarının İnvasiv Olmayan Yöntemler ile Kestirilmesi Amacıyla Akciğer Basınçları Ve Akciğer Sesleri Arasındaki İlişkinin Modellenmesi(TÜBİTAK EEEAG Proje, 2020) SAATÇI, ESRA; Öztürk, Ayşe Bilge; SAATÇI, ERTUĞRUL; Akan, aydınSolunum fonksiyon testleri solunum hastalıklarının teshis ve tedavisinin izlenmesinde kullanılırlar. Hastane ortamında yapılan bu testler pahalı cihazlara ve hastalar tarafından yapılan çesitli solunum manevralarına ihtiyaç duyarlar. Bu projenin amacı klinikte kullanılan solunum fonksiyon testlerinin yerine basit yöntemler ile solunum parametrelerinin bulunmasıdır. Bu amacı gerçeklestirmek için akciger basınçlarının girisimsel olmayan yöntemler ile kestirilmesi gerekmektedir. Basit mikrofonlar ile ölçülen akciger seslerinin ve havayolu gaz akıs hızı, sıcaklıgı ve nemi gibi çesitli solunum sinyallerinin istatistiksel ve fraktal sinyal isleme yöntemleri ile islenmesi bu projede önerilen temel yöntemdir. Solunum parametrelerinin kestiriminde bazı sinyal isleme yaklasımları önerilmis olsa bile solunum sesleriyle beraber istatistiksel ve fraktal sinyal isleme yöntemleri kombinasyonunun kullanılması bu projenin yenilikçi kısmıdır. Yapılan analizler sonucunda derin ve normal solunumların birlikte kullanıldıgı bronsial solunum sesinden elde edilen Hurst üstelinin agız içi basıncının kestiriminde en basarılı sonuçları verdigi görülmüstür. Ayrıca viskoelastik modelin yardımıyla kestirilen akciger basınçlarının gücü en iyi spirometrik testlerde FEV1 ve FVC parametreleriyle IOS testinde R5 parametresi ile ilişkilidir.Publication Metadata only A Novel Compact, Broadband, High Gain Millimeter-Wave Antenna for 5G Beam Steering Applications(IEEE Institute of Electrical and Electronics Engineers, Inc., 2020) ÖZPINAR, HÜRREM; AKŞİMŞEK, HÜSEYİN SİNAN; Tokan, Nurhan TürkerThe millimeter-wave (mmWave) antennas for smartphones are one of the key components to complete the transition to 5G mobile networks. Although research and development of mmWave 5G antennas for cellular handsets are currently at the center of a significant research effort in both academia and telecommunication industry, a systematic antenna design approved by wireless community has not been proposed yet. With this communication, we propose a novel, high gain, wide band and compact mmWave 5G antenna, namely clover antenna for cellular handsets. The presented antenna has clover like conductor profile whose parameters can be adjusted to obtain high gain or wide band. The designed antennas are simulated with a widely used full-wave analysis tool. Numerical results of the mmWave antenna are confirmed successfully by the experimental results in ${{\text{24}}}$-${\text{28}}$ GHz band. The antenna achieves measured peak gain of ${\text{ 7.8}}$-${\text{9}}$ dBi in the band. Besides, with a ${\text{16}}$-element clover antenna array, the beam steering capability of the antenna is demonstrated. Beam steering between ${{ \pm \text{45}<^>\circ }}$ is achieved with low side lobe levels. Practical design considerations for the integration of the arrays in handset to obtain full-coverage in horizontal plane are investigated. The calculated spatial peak power density values of the proposed array on the outer surface of a head phantom are demonstrated for different scan angles.Publication Restricted PyTHang: an Open-s-Source Wearable Sensor System for Real-Time Monitoring of Head-Torso Angle for Ambulatory Application(Taylor & Francis Ltd., 2021) GÜRKAN, GÜRAYThis article presents the realization of a low-cost wearable sensor system and its Python-based software that can measure and record relative head-torso angle, especially in sagittal plane. The system is mainly developed to track head-torso angle during walk in a clinical study. The open-hardware part of the system is composed of a pair of triaxial digital accelerometers, a microprocessor, a Bluetooth module and a rechargeable battery unit. The reception of the transmitted acceleration data, visualization, interactive sensor alignment, angle estimation and data-logging are realized by the developed open-source graphical user interface. The system is tested on a tripod for verification and on a subject for practical demonstration. Developed system can be constructed and used for ambulatory monitoring and analysis of relative head-torso angle. Open-source user interface can be downloaded and developed for further (different) algorithms and device hardware.