A survey of human-computer interaction (HCI) & natural habits-based behavioural biometric modalities for user recognition schemes

•The article presents a survey of the human-computer interaction and natural habits-based biometrics, namely, touchstroke, swipe, touch-signature, hand-movements, voice, gait, and single footstep that can be acquired from smart devices equipped with motion sensors, touch screens, and microphones or by external IoT sensors or nodes in an unobtrusive manner.•The article elicits attributes and features of the aforementioned behavioral biometrics that can be exploited for designing reliable user recognition schemes. We discuss the methodologies, classifiers, datasets, and performance results of recent user recognition schemes that employ these behavioral biometrics modalities.•The article presents security, privacy, and usability attributes with regard to the (CIA) properties in human-to-things recognition schemes.•The article discusses challenges, limitations, prospects, and opportunities associated with behavioral biometric-based user recognition schemes. The prospects and market trends indicate that behavioral biometrics can instigate innovative ways to implement implicit (frictionless), continuous (active), or risk-based (non-static) recognition schemes for IoT applications.•Ultimately, with the availability of smart sensors, advanced machine learning algorithms, and powerful IoT platforms, behavioral biometrics can substitute conventional recognition schemes, thus, reshaping the existing user recognition landscape.