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In gesture and motion detection using RF signals, the work takes advantage of the effects of motion on changing the multipath propagation of signals and the resulting change in statistical behavior of the features of RF cloud to classify human activity. Similarly, it is possible to use the uniqueness of these variations for individual human motions to identify a person. For example, when we train a computer to detect the keystroke of a person using the CSI from Wi-Fi signals, the same system can identify that person as the keystrokes of one individual vary from that of another. This way, using the CSI for keystroke detection can also be used for human authentication for security purposes [83]. In recent years, a body of literature has also evolved for applications of the Wi-Fi RF cloud in authentication and security. Again, the simplest feature that is used is the RSS and it is possible to use the RSS behavior of body mounted sensors to identify a person [85]. Fundamentally, authentication is a binary decision-making process (is it Alice or not) and activity classification is a multiple classification problem (is Alice jogging, walking, standing, or sitting). In authentication we compare RF feature characteristics of one person to others, while in activity classification we usually compare different activities of a single person. Such similarities have led to the emergence of literature in using more complex CSI from Wi-Fi signals for device-free authentication such as those in [86]. Another survey of these categories of applications is available in [87].
ETG members offer a variety of options to support the implementation of an EtherCAT Master, ranging from free downloads of EtherCAT Master Libraries, sample Master code, all the way to complete packages (including services) for various real-time operating systems and CPUs. 59ce067264