Arm gesture recognition on microsoft KinectUsinga Hidden Markov Model-based representations of poses

Gesture recognition has recently generated significant research interest. It is primarily apprehensive on exploring the performance of human acumen. Visual understanding of hand gestures can help in attaining the simplicity and characteristic craved for Human Computer Interaction (HCI). Gesture recognition is effortless for human beings but a very challenging task when it comes to computers. To aid this problem, we have proposed a Kinect based state-of-the-art solution. We introduce three gestures i.e. acceleration, turn right and turn left and yield their skeletal tracks through Kinect. Collected dataset is then normalized and trained to accumulate library of poses using an HMM-based algorithm. We evaluate our approach on a dataset of 228 videos. After cross-validation, experimental results show that the accuracy of 81.13% is achieved for discretized poses.