Bent fingers’ angle calculation using supervised ANN to control electro-mechanical robotic hand

The shape of human hand is such that it can perform many tedious tasks easily. It can reach narrow places and can perform difficult operations. It can bend its fingers at different angles to pick or hold different objects and apply force via fingers or palm area. It is very helpful in many difficult applications. However, there is risk of injury to the human hand, or even life in dangerous operations. It is not advisable to gamble of human body parts for applications like land mine removal. Hence, there is a need of a robotic hand which can perform the same operation as a human hand does in real time. This paper discusses a vision-based technique of controlling a robotic hand which has human hand like joints in fingers. The user has to show a gesture to the system with bare hand without any limitation on hand direction and the robotic hand would mimic that gesture. The positions of human hand fingers were calculated using supervised Artificial Neural Network. The preprocessing made the whole algorithm faster by cropping the region of interest from input image frame. The gesture was extracted from the input image and fingertips, centre of palm were detected. The animated simulation of robotic hand is done in Blender® software.

Figure optionsDownload as PowerPoint slideHighlights
► Controlling of robotic hand to replace human hand in dangerous applications.
► Bent fingers’ angle calculation to find their current position.
► Supervised ANN implementation to find finger positions.
► A novel real time algorithm to reduce computational time by cropping image.