Hand State Combination as Gesture Recognition using Kinect v2 Sensor for Smart Home Control Systems Smart home technology is evolving along with advanced technology evolvement. One of the technology which is constantly evolving is a humanmachine interface. Within this group, a gesturebased interface enables to change this movement becomes a command for computer systems without contacting any surfaces. A gesture is one of the most convenient and natural ways to control a smart home. This research discusses the development of hand gesture recognition using hand state combinations to control various electronic devices for a smart home to facilitate human activities in a home. The system is built by performing hand gesture recognition from a command received by Kinect v2 sensor with hand tracking process to get the desired request based on existing hand state combination. There are three hand states with nine hand state combinations. Each combination means different commands for different electronic devices. This study uses the hand state from Kinect v2 dataset, which are open, close, lasso, untracked, and unknown. Based on the experimental results, the system accuracy reached 87% with an optimal distance of three meters.

The Manipulation of RealTime KinectBased Robotic Arm Using DoubleHand Gestures This study focused on utilizing the Kinect depth sensor to track doublehand gestures and control a realtime robotic arm. The control system is mainly composed of the microprocessor, a color camera, the depth sensor, and the robotic arm. The Kinect depth sensor was used to take photos of the human body to analyze the skeleton of a human body and obtain the relevant information. Such information was used to identify the gestures of the left hand and the left palm of the user. The gesture of left hand was used as an input command device. The gesture of the right hand was used for imitation movement teaching of robotic arm. From the depth sensor, the realtime images of the human body and the deep information of each joint were collected and converted to the relative positions of the robotic arm. Combining forward kinematics and inverse kinematics and DH link, the gesture information of the right hand was calculated, which was converted via coordinates into each angle of the motor of the robotic arm. From the color camera, when the left palm was not detected, the user could simply use the right hand to control the action and movement of the realtime robotic arm. When the left palm was detected and 5 fingertips were identified, it meant the start of recording the realtime imitation movement of the robotic arm by the right hand. When 0 fingertip was identified, it meant the stoppage of the above recording. When 2 fingertips were identified, the user could not only control the realtime robotic arm but also repeat the recorded actions.