ABSTRACT
Adverse mental states of the user such as inattention, fatigue, or high workload can considerably impact the effectiveness and safety of human-machine systems. Approaches of adaptive system design aiming at mitigating those critical states through the application of adaptation strategies have been researched for several decades. More recently, this research area has been able to benefit from technical advances, especially in the field of sensor technology for user state assessment. Thus, new possibilities now arise to detect mental states and to address them by adaptive technology. However, there are still challenges of transferring these concepts from the laboratory to practical applications. This chapter provides an overview of current research and theoretical concepts related to user states and their assessment. It proposes a holistic view on user state and highlights its implications for the design and implementation of adaptive intelligent systems. Subsequently, this chapter introduces RASMUS (Real-time Assessment of Multidimensional User State) as an example of a diagnostic component enabling dynamic system adaptation.
