Exploring user cognition difference and pleasure balance guidance method for product perceptible features in vehicle-mounted system

Chao Zhang ()
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Chao Zhang: Changsha University of Science & Technology

International Journal of System Assurance Engineering and Management, 2022, vol. 13, issue 3, No 6, 1019-1030

Abstract: Abstract The cognition difference and pleasure balance guidance method are studied for the vehicle-mounted human–computer interaction system to reduce the user and designers’ cognition differences. First, the perceivable innovation design is introduced, and the user-designers cognition correlation model is established based on perceivable product innovation. Then, the physiological signals of users are collected through related equipment, and the recognition model for users’ positive and negative emotions and physiological signals is constructed through the support vector machine (SVM) algorithm and the K nearest neighbor (KNN) algorithm. Finally, the pleasure balance guidance method is proposed based on the user and designers’ cognition differences. The experimental results show that the recognition rate of emotion extracted from wavelet packet energy using the KNN and the SVM algorithm is 80.66% and 76.23%, respectively. The recognition rate of physical load extracted from the time-domain features and wavelet packet energy features through the SVM algorithm is 79.51% and 57.23%, respectively. The results are of great engineering significance for the design of the automobile interaction system.

Keywords: Machine learning; User experience; Human–computer interaction; Emotional calculation; K-nearest neighbor algorithm (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s13198-021-01205-9

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