Design Of A Self-Learning Prosthetic Hand Using Wireless Data Transmission and Flex Sensor

The project aims developing a prosthetic hand that can transfer data received from the flex sensor glove wirelessly and memorizing the desired movement. Prosthesis hand designs were examined and two of them were printed by 3D printer. In order to detect the desired movement of the hand, flex sensor glove was made. Flex sensor glove uses XBee for wireless data transmission.Movement of the prosthetic’s fingers is provided by servos. When the motors stretch the line the fingers close and when they release they open. A keypad is added for reducing the use of gloves. Once the desired movement has been registered, a key can be easily switched.As a result; a learning, RC prosthesis design which is open to development has been developed.

Artificial Intelligence System Object and Place Recognizer for Blind People and Development of Data Set

In many areas, such as finance, health and safety, and in the process of facilitating the lives of persons with disabilities, artificial intelligence systems operate. Applications developed using artificial intelligence technologies are able to identify visually impaired objects, while location recognition practices are inadequate. With a mobile application that works on deep learning as we develop for our project and does not require an Internet connection, visually impaired individuals can identify places near the external environment. Individuals with no visual impairment can also be part of the solution by taking pictures of the surrounding sites, thereby contributing to the growth of the data set.

The Design of Computer Controlled Refractometer

In this project, we aimed to design a computer-controlled refractometer with the basic tools we had. For this purpose we used a hollow glass prism which is filled with a liquid whose refractive index to be measured. We placed this prism on a stepper motor in front of a laser module. So the laser beam refracts as it passes through this liquid and falls to the screen on the other side of the prism with a deviation. To determine angle of this deviation, we used a webcam. We adjusted the angle of incidence of the beam by rotating the stepper motor. A Python code is used to determine the amount of deviation and to rotate the stepper motor via Arduino. In order to test the refractometer we designed, refractive indices of different liquids were measured and presented with error calculations.

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