The interaction of humans with computers and machines may take place in different possible ways, by exploiting a rich spectrum of cues. It typically involves the design and the usage of an interface, capable of capturing signals from people and their surrounding environment, and also to return stimuli or take actions depending on the interested application.
As such, human–computer communication and interaction is by nature a multidisciplinary topic, situated at the intersection of computer science, behavioral sciences, design, media, psychology, and several other fields of study. Within this framework, signal processing techniques are increasingly used to analyze the signals which can be gathered through the diverse types of sensors deployed when designing an interface, and extract from the collected data relevant information describing the users’ needs, demands, intentions, as well as physical and emotional status.
Actually, signal processing techniques can be employed to derive useful user’s characteristics about neural and muscular activities from the electrical signals the human body generates. In addition to medical purposes, such data can be exploited to monitor the subjects’ interest and involvement in the performed tasks, besides being used to communicate requests to a computer. Multimedia data can be analyzed by computer-vision techniques to understand and keep track of human’s posture, movements, and actions. Also interactions within social networks or virtual environments can be investigated to catch users’ habits and evaluate potential risks or opportunities. Furthermore, advanced signal processing techniques based deep neural networks could be also employed to process large amounts of heterogeneous data collected about the subjects involved in an application.