Affective computing--a rationale for measuring mood with mouse and keyboard.

Area of Science:

• Human-Computer Interaction (HCI)
• Affective Computing
• Behavioral Psychology

Background:

• Emotions are crucial in Human-Computer Interaction (HCI).
• Current emotion recognition methods in HCI often rely on intrusive techniques like video cameras or physiological sensors.
• There is a need for non-invasive methods to assess affective states.

Purpose of the Study:

• To develop and evaluate a novel method for measuring affective states.
• To utilize motor-behavioral parameters from standard input devices for emotion recognition.
• To offer a less intrusive alternative to existing HCI emotion detection techniques.

Main Methods:

• Collecting motor-behavioral data from standard input devices (mouse and keyboard).
• Analyzing parameters such as movement patterns, typing speed, and interaction dynamics.
• Developing algorithms to infer affective states from collected behavioral data.

Main Results:

• The proposed method successfully measures affective states through motor-behavioral analysis.
• Results indicate a correlation between specific motor-behavioral patterns and emotional conditions.
• The approach demonstrates feasibility as a non-intrusive emotion recognition technique.

Conclusions:

• Motor-behavioral parameters from standard input devices offer a viable, non-intrusive approach to emotion recognition in HCI.
• This method has the potential to enhance user experience and adaptive system design.
• Further research can refine the accuracy and scope of this affective computing technique.