New technology and algorithms empower computers with ways to analyze human behavior. Human behavior understanding not only improves the existing applications with more ways of interaction and smarter decision and response logic, it also opens up new venues and application areas. Hence, in many research fields, such as ubiquitous computing, multimodal interaction, ambient assisted living and assisted cognition, as well as computer supportive collaborative work, the awareness is emerging that endowing the computer with a capacity to attribute meaning to users’ attitudes, preferences, personality, social relationships, etc., as well as to understand what people are doing, the activities they have been engaged, their routines and lifestyles, has the potential to re-define the relationship between the computer and the interacting human, moving the computer from a passive observer role to a socially active participating role and enabling it to drive some kinds of interaction.
Human behavior is cultural, contextual, and idiosyncratic. Nonetheless, it is adaptive in the short term. The challenges of automatically interpreting complex behavioral patterns generated when humans interact with machines or with others are still open, including the joint modeling of behavioral cues taking place at different time scales, the inherent uncertainty of machine detectable evidences of human behavior, the mutual influence of people involved in interactions, the presence of long term dependencies in observations extracted from human behavior, and the important role of dynamics in human behavior understanding.
Dr. Albert Ali Salah