Abstract: A method for training a neural network for facial expression recognition includes recognizing a plurality of digital human face models. For each of the plurality of digital human face models, a plurality of simulated facial expressions are simulated. Simulated capacitance measurements for an array of simulated radio frequency (RF) antennas are found for each of the plurality of simulated facial expressions. The simulated capacitance measurements for each simulated facial expression are provided as input training data to a neural network configured to output facial expression parameters based on input capacitance measurements.

Abstract: A system for assigning a social attribute class to a human subject in a predefined closed environment includes an image capturing component, a pose detection component configured to perform pose detection and tracking of a human subject in real-time, an action detection component configured to detect an action of the human subject, an activity detection component configured to relate a sequence of actions to detect an activity of the human subject, and a social attribute classification component. The social attribute classification component is configured to determine an average speed (s) of the human subject as a first social attribute, an interaction time of the human subject (Tint) as a second social attribute, an analysis time (Tanal) as a third social attribute, and automatically assigns a social attribute class to the human subject based on the values of the first, second and third social attributes.

Abstract: A system for anomaly monitoring in a pre-defined environment includes one or more image capturing devices configured to capture one or more images of the pre-defined environment in real-time, a process detecting component configured to detect one or more target processes based on captured image data, each target process representing a sequence of user activities in the pre-defined environment, a blockchain unit configured to store the one or more target processes and a reference process in form of a private distributed logbook, wherein each of the target processes and the reference process include a series of process transactions, wherein each process transaction is stored in an encrypted form in the blockchain unit, and an analysis unit communicatively coupled to the blockchain unit, and configured to analyse the private distributed logbook by comparing each target process with the reference process to determine one or more anomalies therein.

Abstract: A system for assigning a social attribute class to a human subject in a predefined closed environment includes an image capturing component, a pose detection component configured to perform pose detection and tracking of a human subject in real-time, an action detection component configured to detect an action of the human subject, an activity detection component configured to relate a sequence of actions to detect an activity of the human subject, and a social attribute classification component. The social attribute classification component is configured to determine an average speed (s) of the human subject as a first social attribute, an interaction time of the human subject (Tint) as a second social attribute, an analysis time (Tanal) as a third social attribute, and automatically assigns a social attribute class to the human subject based on the values of the first, second and third social attributes.

Abstract: A system for anomaly monitoring in a pre-defined environment includes one or more image capturing devices configured to capture one or more images of the pre-defined environment in real-time, a process detecting component configured to detect one or more target processes based on captured image data, each target process representing a sequence of user activities in the pre-defined environment, a blockchain unit configured to store the one or more target processes and a reference process in form of a private distributed logbook, wherein each of the target processes and the reference process include a series of process transactions, wherein each process transaction is stored in an encrypted form in the blockchain unit, and an analysis unit communicatively coupled to the blockchain unit, and configured to analyse the private distributed logbook by comparing each target process with the reference process to determine one or more anomalies therein.

Abstract: A system for operating a plurality of self-checkout (SCO) terminals of an SCO surface area of a retail store includes a central camera to capture an overview image of the SCO surface area, and a central control unit. The central control unit includes a memory, and a central processing unit configured to identify a non-scan event at an SCO terminal, check if the number of other already locked SCO terminals is less than a first threshold, lock the SCO terminal if the number of other locked SCO terminals is less than the first threshold, check if a number of consecutive non-scan events at the SCO terminal has reached a second threshold, if the number of other locked SCO terminals has reached the first threshold, and lock the SCO terminal, if the number of consecutive non-scan events detected at the SCO terminal has reached the second threshold.