In human computer interaction, some of the user activities are intentional, and other unintentional, but user interfaces are usually designed to react only to intentional commands. However, user's unintentional activity contains many clues about a user, that can be beneficial to take into account in designing appropriate response. Current study focuses on these unintentional traces, that left behind by use of standard input devices, keyboard and mouse, and specifically, we try to predict users age and gender. Mouse and keyboard data used in this study, are collected in six different systems between 2011 and 2017 in total from 1519 subjects. Some supervised machine learning models yield to f-scores over 0.9 when predicted both user age or gender.

The transition from Industry 4.0 to Industry 5.0 highlights recent European efforts to design intelligent devices, systems, and automation that can work alongside human intelligence and enhance human capabilities. In this vision, human–machine interaction (HMI) goes beyond simply deploying machines, such as autonomous robots, for economic advantage. It requires societal and educational shifts toward a human-centric research vision, revising how we perceive technological advancements to improve the benefits and convenience for individuals. Furthermore, it also requires determining which priority is given to user preferences and needs to feel safe while collaborating with autonomous intelligent systems. This proposed human-centric vision aims to enhance human creativity and problem-solving abilities by leveraging machine precision and data processing, all while protecting human agency. Aligned with this perspective, we conducted a systematic literature review focusing on trust and trustworthiness in relation to characteristics of humans and systems in human–robot interaction (HRI). Our research explores the aspects that impact the potential for designing and fostering machine trustworthiness from a human-centered standpoint. A systematic analysis was conducted to review 34 articles in recent HRI-related studies. Then, through a standardized screening, we identified and categorized factors influencing trust in automation that can act as trust barriers and facilitators when implementing autonomous intelligent systems. Our study comments on the application areas in which trust is considered, how it is conceptualized, and how it is evaluated within the field. Our analysis underscores the significance of examining users’ trust and the related factors impacting it as foundational elements for promoting secure and trustworthy HRI.

Knowing user emotional state is crucial for building more adaptive and responsible systems. This study combines data collected in two different experiments. Emotions were inducted by situations or by movie clips, and data about user emotions were collected using Think-Aloud protocol, and two-dimensional feedback, and. In the first experiment, only mouse data were collected, at last study also keyboard data were collected. Supervised machine learning models were generated using features extracted from keyboard and mouse data. Best models predicted user emotion dimensions over 90% of accuracy. Data from the first experiment was tested on second experiment models.

Handedness is one of the soft biometrie attributes that can be employed for profiling anonymous authors. In this study, we present handedness detection by typing differences. We collected free form keystroke data from 504 people, 101 reported left hand as their writing hand and 403 were right handed. Using machine learning we discriminated left and right handed people over 99.5% accuracy with balanced dataset. Finally, we reduced feature set to a single feature which still yield to 98% classification accuracy.