Machine learning in 3D space gesture recognition

The rapid increase in the development of robotic systems in a controlled and uncontrolled environment leads to the development of a more natural interaction system. One such interaction is gesture recognition. The proposed paper is a simple approach towards gesture recognition technology where the hand movement in a 3-dimensional space is utilized to write the English alphabets and get the corresponding output in the screen or a display device. In order to perform the experiment, an MPU-6050 accelerometer, a microcontroller and a Bluetooth for wireless connection are used as the hardware components of the system. For each of the letters of the alphabets, the data instances are recorded in its raw form. 20 instances for each letter are recorded and it is then standardized using interpolation. The standardized data is fed as inputs to an SVM (Support Vector Machine) classifier to create a model. The created model is used for classification of future data instances at real time. Our method achieves a correct classification accuracy of 98.94% for the English alphabets’ hand gesture recognition. The primary objective of our approach is the development of a low-cost, low power and easily trained supervised gesture recognition system which identifies hand gesture movement efficiently and accurately. The experimental result obtained is based on use of a single subject