Neural Network(NN) has been applied to the human cognitive state estimation based on the set of physiological measures. Heart Rate, Blood Pressure, Respiration Rate, Skin Potential Response (SPR), Blink Rate and Saccadic Eye Motion Rate have been chosen as the representative metrical indices reflecting human mental state. The qualitative tendencies of these measures have been taken as the inputs of the NN. The human cognitive states are categorized into six pre-specified states; (1) Information Acquisition(IA), (2) Memory Related(MR), (3) Thought(TH), (4) Motor Action(MA), (5) Emotion(EM) and (6) Others(OT). The adopted network is three layers feed-forward network trained with backpropagation algorithm with forgetting. Sets of training data for learning have been collected through the laboratory experiments, in which the subjects were induced to undergo the specific sequence of the cognitive state. The resultant NN showed the superior capability of discriminating the human cognitive states based on the pattern of the physiological measures.
|Number of pages||8|
|Publication status||Published - 1994 Dec 1|
|Event||Proceedings of the IEEE/RSJ/GI International Conference on Intelligent Robots and Systems. Part 3 (of 3) - Munich, Ger|
Duration: 1994 Sep 12 → 1994 Sep 16
|Other||Proceedings of the IEEE/RSJ/GI International Conference on Intelligent Robots and Systems. Part 3 (of 3)|
|Period||94/9/12 → 94/9/16|
ASJC Scopus subject areas