纺织学报 ›› 2022, Vol. 43 ›› Issue (08): 197-205.doi: 10.13475/j.fzxb.20210107509

• 综合述评 • 上一篇    下一篇

可穿戴技术在情绪识别中的应用进展及发展趋势

刘欢欢1,2,3, 王朝晖1,2,3(), 叶勤文1,2, 陈子唯1,2, 郑婧瑾1,2   

  1. 1.东华大学 服装与艺术设计学院, 上海 200051
    2.东华大学 现代服装设计与技术教育部重点实验室, 上海 200051
    3.上海市智能制造与工程一带一路国际联合实验室, 上海 200051
  • 收稿日期:2021-01-29 修回日期:2021-09-07 出版日期:2022-08-15 发布日期:2022-08-24
  • 通讯作者: 王朝晖
  • 作者简介:刘欢欢(1997—),女,硕士生。主要研究方向为服装先进制造与人体科学研究。
  • 基金资助:
    上海市科学技术委员会“科技创新行动计划”“一带一路”国际合作项目(21130750100)

Progress and trends in application of wearable technology for emotion recognition

LIU Huanhuan1,2,3, WANG Zhaohui1,2,3(), YE Qinwen1,2, CHEN Ziwei1,2, ZHENG Jingjin1,2   

  1. 1. College of Fashion and Design, Donghua University, Shanghai 200051, China
    2. Key Laboratory of Clothing Design and Technology, Ministry of Education, Donghua University, Shanghai 200051, China
    3. Shanghai Belt and Road Joint Laboratory of Textile Intelligent Manufacturing, Shanghai 200051, China
  • Received:2021-01-29 Revised:2021-09-07 Published:2022-08-15 Online:2022-08-24
  • Contact: WANG Zhaohui

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摘要：

为促进情绪识别智能可穿戴产品技术在纺织服装领域的创新发展,系统介绍了近些年国内外情绪识别监测内容方法、分类算法以及情绪识别可穿戴设备的研究现状。首先概述了情绪分类模型并总结情绪产生时出现的生理反应;然后针对目前情绪识别监测内容方法的研究现状,阐述了生理信号和行为表现二大类情绪识别监测内容方法,进一步总结常用情绪识别分类算法,并依据可穿戴设备部位总结现有情绪识别可穿戴产品;最后讨论了情绪识别智能可穿戴设备在未来发展中需要解决的问题,并从柔性舒适采集设备、识别结果准确度以及识别结果交互方式3个方面展望其未来发展趋势和应用前景。

关键词: 情绪识别, 机器学习, 情绪模型, 智能可穿戴, 生理信号, 行为表现

Abstract:

In order to promote the innovative development of smart wearable product technology for emotion recognition in the textile and apparel field, this paper systematically introduced the current research status of emotion recognition monitoring, classification algorithms and emotion recognition wearable devices. The emotion classification model was outlined and the physiological reactions summarized that occur when emotions were generated. In view of the current research status of emotion recognition monitoring methods, two categories of emotion recognition monitoring methods, namely physiological signals and behavioural manifestations, were elaborated, and the common emotion recognition classification algorithms and the existing emotion recognition wearable products based on the wearable device parts were summarized. The review also discussed the challenges and problems that need to be addressed in future development of emotion recognition smart wearable. The review identified future development trend and application prospects from three aspects: flexible and comfortable collection devices, accuracy of recognition results, and the way to interact with recognition results.

Key words: emotion recognition, machine learning, emotion modeling, smart wearable, physiological signal, behavioral expression

中图分类号: 

  • TS941

图1

效价–唤醒度情绪模型"

表1

4种典型情绪状态及其生理反应"

项目 愤怒 悲伤 愉悦 快乐
心率 ↑ ↓ ↑↓ ↑
心率变异性 ↓ ↓ ↑ ↓
皮肤电导率水平 ↑ ↓ ↑ ↑
呼吸频率 ↑ ↑ ↑ ↑
体温 ↑ ↓

图2

不同情绪下嘴部肌肉差异"

图3

眼部区域面容改变的7个AU单元"

图4

购物中心情绪监测系统示意图"

表2

情绪识别可穿戴研究中情绪类型和识别信号及结果总结"

情感状态 信号类型 识别算法 识别准确度/％ 参考文献
愤怒、厌恶、中性、愉悦等8种 EMG、PPG、RESP、EDA K近邻算法 81 [12]
快乐、愤怒、愉悦、悲伤 ECG、EDA、EMG、RESP K近邻算法;线性判别函数;神
经网络		 81、80、81 [40]
高压、低压 PPG 逻辑回归 63左右 [41]
效价–唤醒度模型下的积极兴奋、消极兴奋和冷静 BP、EEG、EDA、PPG、RESP 线性判别分析;二次判别分析;
支持向量机		 <50、<47、<50 [42]
效价–唤醒度二维模型(5等级) ECG、EDA、RESP 二次判别分析 >90 [43]
放松、焦虑、激动、快乐 EDA、PPG 卷积神经网络 <75 [44]
高/低效价,高/低唤醒度 ECG、EMG、EOG 支持向量机 5060 [45]
平静、快乐、悲伤、恐惧、愤怒 EDA、PPG 规则范式分类器 <87 [46]
放松,3种不同类型压力状态(生理、情感、认知) EDA、TEMP、HR 高斯混合模型 <85 [47]
高/低效价,高/低唤醒度 EEG、EDA、EMG 决策树; 朴素贝叶斯算法; 支持
向量机		 63.8、58.5 [48]
高/低效价,高/低唤醒度 EDA、PPG、TEMP 朴素贝叶斯算法; K近邻算法;
随机森林;支持向量机		 76 [49]
高/低效价,高/低唤醒度 ECG、EDA 卷积神经网络 效价维度75;
唤醒度维度71		 [50]
快乐、放松、厌恶、悲伤和中性 EDA、PPG、EMG 深度信念网络+支持向量机 <67 [51]
笑声监测 EDA、PPG 逻辑回归;随机森林;支持向量机 87 [52]
高/低效价,高/低唤醒度 EMG、PPG、TEMP 决策树;K近邻算法;随机森林 <67 [53]

表3

不同情绪识别方法与可穿戴技术结合总结"

识别内容 监测部位 可应用产品形态
生理信号 头部、脸部、胸部 头盔、头箍、眼镜、口罩、项链、
内衣、背心、衬衫、胸带等
肢体动作 手腕、脚腕 智能手环、智能手表、
裤子、鞋类等
面部表情 脸部 眼镜、口罩等
语音语调 可采集到音频的
位置都可		 佩戴型产品
行为线索 可采集到行为信息的
位置都可		 佩戴型产品、智能家居等

图5

Amoeba智能眼镜"

图6

眼镜式可穿戴系统"

图7

PSYCHE可穿戴监控系统模型"

图8

飞利浦“布贝尔服”"

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