%0 Conference Proceedings
%T Research on a Smart Input Device Using Multimodal Bio-signal Interface
%+ Sejong University
%+ Samsung Electronics [Korea]
%A Lee, Hyunju
%A Kim, Taehyun
%A Shin, Dongkyoo
%A Park, Heewon
%A Kim, Soohan
%A Shin, Dongil
%Z Part 13: UMAS
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 9th International Conference on Network and Parallel Computing (NPC)
%C Gwangju, South Korea
%Y James J. Park
%Y Albert Zomaya
%Y Sang-Soo Yeo
%Y Sartaj Sahni
%I Springer
%3 Network and Parallel Computing
%V LNCS-7513
%P 565-574
%8 2012-09-06
%D 2012
%R 10.1007/978-3-642-35606-3_67
%Z Computer Science [cs]Conference papers
%X This paper presents a smart input device based on multimodal analysis methods using human bio-signals. The core of the smart input device is software modules, which consist of an intelligent driving function and an analysis processing function for bio-signals. The smart input device utilizes a multimodal interface that analyzes and recognizes human bio-signal patterns. The multimodal analysis system can recognize a user’s emotional and stress status by analyzing an electrocardiogram (ECG), and it can determine the user’s level of concentration from electroencephalogram (EEG) patterns. To analyze the concentration, stress, and emotional status of the user, the EEG rendering system and ECG analysis system use five signal values, i.e., MID_BETA, THETA, ALPHA, DELTA, GAMMA, and the P, Q, R, S and T waves. A reformation of SVM and a clustering algorithm were applied to the user’s EEG and ECG signal patterns for body context recognition. In our experiment, the on/ off status of the user’s stress status controls the difficulties of the game, such as the selecting the type of race course or the number of obstacles. In addition, the speed of the car can be controlled depending on the concentration or non-concentration status of the user. The stress status of the user was predicted with an accuracy of 83.2% by the K-means algorithm, and the concentration status was predicted with an accuracy of 71.85% by the SVM algorithm. We showed that a bio-signal interface is quite useful and feasible for new games.
%G English
%Z TC 10
%Z WG 10.3
%2 https://inria.hal.science/hal-01551346/document
%2 https://inria.hal.science/hal-01551346/file/978-3-642-35606-3_67_Chapter.pdf
%L hal-01551346
%U https://inria.hal.science/hal-01551346
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-TC10
%~ IFIP-NPC
%~ IFIP-WG10-3
%~ IFIP-LNCS-7513