| 引用本文: | 谢黎,史丰硕,王雨晴,康晓管,罗光荣,尤思雨,于龙飞,郭建文.手表情感化设计的滤波和特征提取技术研究[J].包装工程,2025,46(8):218-237. |
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| 摘要: |
| 目的 探讨特定人群手表产品的适宜滤波方法与生理特征指标。方法 在梳理产品情感化设计流程中生理信号滤波和特征提取研究现状的基础上，搭建手表情感化意象实验平台，利用皮电信号、心电信号和脑电信号，基于Lasso回归探究人群特性下(大学生、高校教师和职场白领)不同滤波和特征提取方法与产品情感意象的差异关联。结果 皮电信号指标在各种滤波方法下呈现出较低的波动性，心电信号指标受“高通+低通”滤波方法的影响较大，脑电信号指标受“IMF+小波变换”滤波方法的影响较大;高校教师相较于大学生和职场白领在滤波方式上呈现出较高的稳定性;不同人群在效价、唤醒的Lasso回归图中保留的生理情感特征维度和数量不同。结论 在产品情感化设计中应根据不同人群的生理特性，优选相应的滤波方法和特征提取指标，以准确建立“生理-产品情感意象”映射模型，实现产品形态与情感状态的有效融合。 |
| 关键词: 生理信号 信号处理技术 滤波 特征提取 产品情感化设计 |
| DOI：10.19554/j.cnki.1001-3563.2025.08.021 |
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| 基金项目:教育部人文社会科学研究项目(21YJCZH184) |
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| Application of Filtering and Feature Extraction Technology in the Emotional Design of Watches |
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XIE Li, SHI Fengshuo, WANG Yuqing, KANG Xiaoguan,LUO Guangrong, YOU Siyu, YU Longfei, GUO Jianwen
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(Dongguan University of Technology, Guangdong Dongguan 523000, China)
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| Abstract: |
| The work aims to explore the appropriate filtering methods and physiological characteristic indicators of watch products for specific groups of people. Based on the current status of research on physiological signal filtering and feature extraction in the emotional product design process, an experimental platform for emotional imagery of watches was built. Skin electrodermal signals, electrocardiogram signals and electroencephalogram signals were used to explore the differential associations between different filtering and feature extraction methods and emotional imagery under population characteristics (college students, university teachers and white-collar workers) based on Lasso regression. The skin electrodermal signal indicators showed low volatility under various filtering methods, the electrocardiogram signal indicators were greatly affected by the "high-pass + low-pass" filtering method, and the electroencephalogram signal indicators were greatly affected by the "IMF + wavelet transform" filtering method. University teachers showed higher stability in filtering methods compared with college students and white-collar workers. The dimensions and number of physiological emotional features retained in the Lasso regression diagrams of valence and arousal were different for different groups of people. In the emotional product design, the corresponding filtering methods and feature extraction indicators should be optimized according to the physiological characteristics of different groups of people, so as to accurately establish the "physiological-product emotional imagery" mapping model and realize the effective integration of product form and emotional state. |
| Key words: physiological signals signal processing technology filtering feature extraction emotional product design |
