Physiological computing is a paradigm of computing that treats users’ physiological data as input during computing tasks in an Ambient Assisted Living (AAL) environment. By monitoring, analyzing and responding to such inputs, Physiological Computing Systems (PCS) are able to respond to the users’ cognitive, emotional and physical states. A specific case of PCS is Neural Computer Interface (NCI), which uses electrical signals governing users’ muscular activity (EMG data) to establish a direct communication pathway between the user and a computer. We present taxonomy of speller application parameters, propose a model of PCS, and describe the development of the EMG-based speller as a benchmark application. We analyze and develop an EMG-based speller application with a traditional letter-based as well as visual concept-based interface. Finally, we evaluate the performance and usability of the developed speller using empirical (accuracy, information transfer speed, input speed) metrics.
Towards textually describing complex video contents with audio-visual concept classifiers
