Journal of Textile Research ›› 2022, Vol. 43 ›› Issue (08): 197-205.doi: 10.13475/j.fzxb.20210107509

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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 Online:2022-08-15 Published:2022-08-24
  • Contact: WANG Zhaohui E-mail:wzh_sh2007@dhu.edu.cn

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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

CLC Number: 

  • TS941

Fig.1

Valence-arousal dimension mood model"

Tab.1

Four typical emotional states and their physiological responses"

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 ↑ ↓

Fig.2

Differences in mouth muscles by mood"

Fig.3

AU units for facial changes in eye area"

Fig.4

Schematic diagram of mood monitoring system for shopping centers"

Tab.2

Summary of emotion types and recognition signals and results in emotion recognition wearable research"

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]

Tab.3

Summary comparison of advantages and disadvantages of different methods of emotion recognition content"

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

Fig.5

Amoeba smart glasses"

Fig.6

Eyewear wearable system"

Fig.7

PSYCHE prototype wearable monitoring system"

Fig.8

Philips "Buell suit""

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