Gesture is one of the most vivid and dramatic way of communications between human and computer/edge device. Hence, there has been a growing interest to create easy-to-use interfaces by directly utilizing the natural communication and management skills of humans. Adopting hand gesture as an interface in HCI will allow the deployment of a wide range of applications without any physical contact with the computing environments. The main purpose of gesture recognition is to identify a particular human gesture and convey information to the computer/edge device. Overall aim is to make the computer understand human gestures, to control remotely through hand postures a wide variety of devices.
This Application presents a hand gesture interface for controlling media player using QueSSence.
QueSSence recognizes a set of four specific hand gestures, namely: Play, Stop, Volume Increase, and Volume Decrease by using K-Nearest Neighbor algorithm.
Developed algorithm is based on four phases, Data acquisition, Data segmentation, Features extraction, and Classification.
We design an affective technique for hand gesture recognition to control media player using the properties of accelerometer signals generated by human hand movements.
We tested and evaluate the effectiveness of algorithm in real time to four specific hand gestures, namely: Play, Stop, Volume Increasing, and Volume Decreasing.