The paper describes a prototype operator panel, which was designed to operate with the S7-200 family of Programmable Logic Controllers (PLC-s) from Siemens. Most of the functionality of the operator panel was implemented in a computer program, which runs on a PC-class computer. The program communicates with a PLC through its communication port configured in the Freeport mode. Two kinds of interface between the PC, and the PLC are supported: wired, and wireless. For wired connection a standard PC/PPI cable supplied by Siemens is used. For wireless connection two communication modules were designed, which operate in the free 433 MHz band. The operator panel program is intuitive, and easy to use. States of PLC inputs and outputs are presented using graphical objects. It is possible to modify states of the outputs, and monitor and edit any variable in the M and V memory in the PLC. The application supports also alarming. The program can be run on any computer with the MS Windows operating system installed. This makes the solution very cost-effective. Providing both wired and wireless communication radically increases flexibility of the proposed solution. The panel can be quickly mounted in areas, where pulling new cables is inconvenient, difficult or expensive.

Based on recent advances in non-linear analysis, the surface electromyography (sEMG) signal has been studied from the viewpoints of self-affinity and complexity. In this study, we examine usage of critical exponent analysis (CE) method, a fractal dimension (FD) estimator, to study properties of the sEMG signal and to deploy these properties to characterize different movements for gesture recognition. SEMG signals were recorded from thirty subjects with seven hand movements and eight muscle channels. Mean values and coefficient of variations of the CE from all experiments show that there are larger variations between hand movement types but there is small variation within the same type. It also shows that the CE feature related to the self-affine property for the sEMG signal extracted from different activities is in the range of 1.855~2.754. These results have also been evaluated by analysis-of-variance (p-value). Results show that the CE feature is more suitable to use as a learning parameter for a classifier compared with other representative features including root mean square, median frequency and Higuchi's method. Most p-values of the CE feature were less than 0.0001. Thus the FD that is computed by the CE method can be applied to be used as a feature for a wide variety of sEMG applications.

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.