IoT devices, which have become an indispensable part of our lives, attract more attention day by day, especially in smart cities. Use of IoT devices on textiles reveals smart textile equipment. In this work, the elbow joint angle of the person can be measured with 2 accelerometer and gyro sensor integrated on the textile. It is one of the first studies to measure the elbow joint angle by placing a sensor on textile and wirelessly transfer it to the computer in real-time in the literature. In our present work, angle measurement process has been carried out on a platform where calculations are made on the Arduino microcontroller using IoT devices. By performing experiments with the smart textile prototype created, its performance has been evaluated. The high performance values show that the elbow joint angles of the individuals can be measured reliably with the designed prototype.

The use of smart textiles is expanding. The wearer’s data are transferred to the Cloud by a mobile device, and shared with authorized parties. The study aims to monitor continuously and share our wearable smart textile’s heartbeat, body temperature, and the surrounding magnetic field data, providing early intervention before negative health events occur, or a high magnetic field is of concern to its wearer. A heartbeat sensor, a temperature sensor, and an ESP32 module with a built-in Hall effect sensor were integrated with a special conductive wire woven fabric. The data measured by the sensors were sent to the cloud server wirelessly by the ESP32. Our custom-made software analyzes the collected data with statistical methods, enabling the generation of predictions and early warnings. The generated reports can be sent to the smart textile user, doctors, and authorized third-party health institutions, and relevant magnetic field authorities. Our study shows that the body temperature reported by the designed smart textile has less than a 2.0% error compared with the actual value. On the other hand, the reported heartbeat has a 11.0% error, as it largely depends on sensor quality and placement location. In addition to these, continuous monitoring of the ambient magnetic field has been achieved with smart textiles. Our smart textile design sends the wearer’s body temperature, heartbeat, and surrounding magnetic field information to a cloud server automatically and wirelessly. Our custom-made software and mobile application use the data to provide early warnings and live reports on users’ mobile devices.

Günümüzde tarımsal faaliyetler, teknolojik yenilikler ile gelişimlerini sürdürmektedir. Bilgi ve iletişim teknolojilerinin tarımda gelişmiş sistemler oluşturarak kullanılmasıyla Hassas Tarım kavramı ortaya çıkmaktadır. Nesnelerin ve sensörlerin internet aracılığıyla birbirleriyle iletişim kurduğu Nesnelerin İnterneti teknolojileri Hassas Tarım kullanımını desteklemektedir. Tarım alanında yapılan çalışmaların etkinliğinin artırılarak daha verimli sonuçlar alınması, aynı zamanda bilgi alışverişinin hızı ve sürekliliği ile birlikte daha kaliteli ürünler alınmasını sağlamak mümkün hale gelmektedir. Tarlalarda yapılan ilaçlama ve gübreleme işlemlerinin otomasyon sistemi kullanılarak daha hızlı ve etkin bir şekilde yapılabilmesi Nesnelerin İnterneti cihazları ile mümkün olmaktadır. Böylelikle ilaçlama ve gübreleme zamanının, lokasyonunun ve miktarının elektronik donanımlar ile kontrol altında olması sağlanabilmektedir. Yapılan çalışma ile, Nesnelerin İnterneti cihazlarının kullanıldığı ve üzerinde tablet olan akıllı traktör prototipi oluşturulmuştur. Prototip üzerindeki tablet aracılığıyla küresel konumlama sistemi (GPS) üzerinden bilgilerin alınması ve traktörün belirlenen alan üzerinde gezinmesini sağlamak üzere mobil uygulama geliştirilmiştir. Oluşturulan akıllı traktör, topladığı GPS bilgileri doğrultusunda harita üzerinde belirtilen alanda kendisi gezinerek veya mobil uygulama üzerinden yönlendirilerek ilaçlama ve gübreleme yapılmasını sağlamaktadır.

In recent years, the use of solar energy from renewable energy sources has been increasing. Solar energy can be used directly for electricity and heat generation, as well as for small-scale use in smart cities such as traffic lights, city lighting, vehicle charging stations. The Internet of Things enables remote monitoring and control of solar energy systems, diagnosis of faults that may occur in the system and increasing system efficiency. The energy production of the system can be monitored depending on the time of day, and if solar tracking systems are applied, tracking angles can be monitored too. In this study, the electrical parameters of a solar panel such as voltage, current values, orientation of the solar panel and the light intensity in the outdoor environment were recorded in the cloud storage area by sensors. The data obtained from all sensors are evaluated and preprocessed by the microcontroller. These collected data are transferred to comparable graphs under two different light intensities. The proposed work is an example of an application that, if scaled up, can be used in monitoring larger-scale systems where solar panels can be grouped in series and parallel. The novel contribution of the study is to evaluate the basic electrical parameters and orientation of solar panels together and to monitor the faults (voltage, current, solar tracking system induced faults) caused by these characteristics.

Parkinson's disease is one of the neurodegenerative diseases that affects neurons in the brain and causes motor functions to deteriorate. The most common symptom of this disease is involuntary tremor, especially in the hands and fingers, when the patient is in a resting position. In this study, a machine learning-based embedded system is proposed that can detect tremor and determine its level according to sensor data obtained from fingers. Subsequently, tremor data was obtained using Arduino UNO and MPU-6050 sensor, machine learning models were trained, and autonomous decision making have been performed. The study aims to evaluate tremor autonomously in real time, report it to the specialist, and assist in diagnosis and treatment. Unlike the studies in the literature, in this study, tremor signals were processed in real time with machine learning techniques instead of rule-based decision making. Tremor signals are digitally generated using sensors via the Internet of Things. Since mobility is crucial in the healthcare industry, the data was transferred wirelessly to the local server and evaluated for ease of use. As a result of this study, 96% accuracy was achieved using artificial neural networks in tremor level detection. By increasing the amount of data and the number of participants, the potential for the system to be developed and used in clinics is quite high.