The field of research of this paper combines Human Computer Interface, gesture recognition and fingertips tracking. Most gesture recognition algorithms processing color images are unable to locate folded fingers hidden inside hand contour. With use of hand landmarks detection and localization algorithm, processing directional images, the fingertips are tracked whether they are risen or folded inside the hand contour. The capabilities of the method, repeatibility and accuracy, are tested with use of 3 gestures that are recorded on the USB camera. Fingertips are tracked in gestures presenting a linear movement of an open hand, finger folding into fist and clenched fist movement. In conclusion, a discussion of accuracy in application to HCI is presented.

Monitoring head movements is important in many aspects of life from medicine and rehabilitation to sports, and VR entertainment. In this study, we used recordings from two sensors, i.e. an accelerometer and a gyroscope, to calculate the angles of movement of the gesturing person’s head. For the yaw motion, we proposed an original algorithm using only these two inertial sensors and the detected motion type obtained from a pre-trained SVM classifier. The combination of the gyroscope data and the detected motion type allowed us to calculate the yaw angle without the need for other sensors, such as a magnetometer or a video camera. To verify the accuracy of our algorithm, we used a robotic arm that simulated head gestures where the angle values were read out from the robot kinematics. The calculated yaw angles differed from the robot’s readings with a mean absolute error of approx. 1 degree and the rate of differences between these values exceeding 5 degrees was significantly below 1 percent except for one outlier at 1.12%. This level of accuracy is sufficient for many applications, such as VR systems, human-system interfaces, or rehabilitation.