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

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  • Chao Zhang

    (Changsha University of Science & Technology)

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.

Suggested Citation

  • Chao Zhang, 2022. "Exploring user cognition difference and pleasure balance guidance method for product perceptible features in vehicle-mounted system," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(3), pages 1019-1030, December.
    • Handle: RePEc:spr:ijsaem:v:13:y:2022:i:3:d:10.1007_s13198-021-01205-9
    DOI: 10.1007/s13198-021-01205-9
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    References listed on IDEAS

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    1. Die Yin & Taifu Li & Zhiqiang Liao, 2020. "Research on the Evaluation Modeling Method of User Experience Quality under Uncertain Noise Environment," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-13, December.
    2. Ahmad Khazaee Poul & Mojtaba Shourian & Hadi Ebrahimi, 2019. "A Comparative Study of MLR, KNN, ANN and ANFIS Models with Wavelet Transform in Monthly Stream Flow Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(8), pages 2907-2923, June.
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