Journal of Textile Research ›› 2026, Vol. 47 ›› Issue (05): 190-200.doi: 10.13475/j.fzxb.20251006501

• Apparel Engineering • Previous Articles     Next Articles

Design of pressure adaptive garment for simulated hugs based on OpenCV image processing

WEN Yihan1, LU Hong1,2()   

  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
  • Received:2025-10-28 Revised:2026-03-06 Online:2026-05-15 Published:2026-07-10
  • Contact: LU Hong E-mail:luhong@dhu.edu.cn

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Abstract:

Objective Hugging, with its broad emotional expression and strong psychological identification, is a common posture in haptic interaction. Various feedback methods, different interaction scenarios, and diverse devices have been used for wearable devices for simulating hugs. However, current devices, especially garment-type ones, have limitations. Feedback module placement lacks clear visual reference, pressure output struggles to adapt to different partners, and functional barriers exist between devices designed for collaborative use and those for individual use. These issues hinder the devices' universality and social interaction capabilities. Exploring hugging postures is important for optimizing feedback module placement and for improving device interaction methods.

Method This study focuses on images of hugging postures among individuals in intimate relationships. By integrating subjective evaluations with objective image capture, average images were analyzed to propose segmentation and localization of comprehensive hugging zones and develop pressure-adaptive garment for simulated hugs. Based on hand placement and body position, three common and comfortable combinations were selected as variables, which are the right lateralization criss-cross style, the neck-waist style with the neck surrounded, and neck-waist style with the waist surrounded. Objective image capture was facilitated utilizing experimental garments coated with reversible thermochromic paint, capturing front, back, sides, and shoulder region images by photographic documentation. Subjective evaluations were adopted to collect participants' pressure perceived zones for ranking the body parts during hugging through online questionnaires. The functional garment employed an ESP32 microcontroller as the main control unit, flexible resistive pressure sensors as input units, and flexible airbag as feedback actuators.

Results For the 360 images collected from objective experiments, multi-image averaging was performed using OpenCV, yielding a total of 18 average images, corresponding to the spatial distribution characteristics of the front, back, sides, and shoulder parts under three hugging postures. Based on the 18 average images, two comprehensive average images were further synthesized, presenting the overall average distribution of the front and back views for the three postures. Analysis of subjective data generated 18 pressure perception heatmaps, reflecting the distribution of pressure perception intensity on different body parts during the hugging process. SPSS was adopted to analyze the ranking data of body parts, and the results revealed a high degree of consistency in the perception intensity priorities between the front and back parts under different hugging postures. In contrast, the perception intensity of the shoulder region showed lower consistency. By comparing the average images with the pressure perception heatmaps, comprehensive hugging zones were manually delineated. Based on this, the spatial positioning and overall layout of the pressure feedback modules for functional garments were determined. Functional testing revealed that the pressure-adaptive recognition of this garment achieved a 60% consistency rate on three metrics, i.e., actual preset values, system recognition, and personal perception.

Conclusion This study focuses on hugging postures among individuals in intimate relationships, employing OpenCV average image processing and subjective pressure perception heatmaps to explore the spatial distribution characteristics of three hugging postures. Manual segmentation is adopted to delineate comprehensive hugging zones. Based on the extracted zones, a functional hugging simulation garment was designed and developed, which was capable of adaptive adjustment according to varying hugging pressure. The findings provide methodological and visual foundations for designing feedback modules in simulated hugging garments, establishing a reusable methodological framework for future development and optimization of functional hugging devices. Furthermore, adhering to the law of large numbers where increasing sample size drives sample means toward theoretical expectations, subsequent research can enhance the representativeness of average images and the universality of region localization by expanding image acquisition scale and establishing broader standardized image databases.

Key words: affective interaction, simulated hug, haptic feedback, OpenCV, flexible airbag, pressure sensor, functional garment, embedded system

CLC Number: 

  • TS941.731

Fig.1

Three bug postures. (a) Right lateralization criss-cross style hug; (b) Neck is surrounded; (c) Waist is surrounded"

Fig.2

Experimental procedures"

Fig.3

Average discoloration images of thermochromic garment at different hug postures. (a) Right lateralization criss-cross style hug; (b) Neck-waist style hug with neck surrounded; (c) Neck-waist style hug with waist surrounded"

Fig.4

Pressure perception heatmaps of human body parts at different hug postures. (a) Right lateralization criss-cross style; (b) Neck-waist style with neck surrounded; (c) Neck-waist style with waist surrounded"

Tab.1

Consistency analysis of perceptual pressure intensity of different body parts"

躯干
部位		 排序赋值
(标准化)		 N(x) 卡方
检验
右偏向
十字交叉		 颈部
被环绕		 腰部
被环绕
正面 1 24(60.0) 23(57.5) 20(50.0) ns
2 7(17.5) 2(5.0) 4(10.0)
3 5(12.5) 13(32.5) 12(30.0)
4 4(10.0) 2(5.0) 4(10.0)
背面 1 11(27.5) 4(10.0) 13(32.5) ns
2 17(42.5) 18(45.0) 20(50.0)
3 9(22.5) 15(37.5) 5(12.5)
4 3(7.5) 3(7.5) 2(5.0)
肩部 1 4(10.0) 12(30.0) 1(2.5) ***
2 10(25.0) 16(40.0) 8(20.0)
3 17(42.5) 8(20.0) 11(27.5)
4 9(22.5) 4(10.0) 20(50.0)

Fig.5

Comprehensive discoloration regions of thermochromic garment on different body parts. (a) Front; (b) Back; (c) Two sides; (d) Shoulder"

Fig.6

Framework of program function logic"

Tab.2

Names and functions of electronic components"

电子元件名称 主要功能
ESP32-WROOM-32芯片 系统编译运算核心
Micro SD卡转接模块 网页代码读取通道
PCA9685舵机驱动板 I2C通信协议控制气泵模块
380微型气泵 实现充气与放气
DS3231时钟模块 获取精确时间
电阻式薄膜压力传感器 获取拥抱压力数据
多通道采集板 压力传感器集成通道

Fig.7

Physical image of circuit connections"

Fig.8

Garment style and module layout. (a) Front; (b) Back"

Fig.9

Pressure adaptive garment for simulated hugs"

Tab.3

Functional test results statistics"

评估
对象		 评级
实际预设 系统识别 个人感知
1# 3 3 2
2# 3 3 1
3# 3 3 2
4# 3 3 3
5# 3 3 2
6# 2 3 3
7# 2 2 3
8# 2 2 3
9# 2 2 2
10# 2 2 1
11# 1 2 1
12# 1 1 1
13# 1 1 2
14# 1 1 1
15# 1 1 3

Tab.4

Analysis results of bivariate consistency"

对象组合 Cohen's Kappa值 线性加权Kappa系数
数值 解释 数值 解释
实际预设&
系统识别		 0.800 高度一致 0.850 高度一致
系统识别&
个人感知		 0.000 随机一致 0.100 轻微一致
实际预设&
个人感知		 0.000 随机一致 0.100 轻微一致

Tab.5

Comprehensive scoring table"

对象
序号		 轻压力拥抱 中压力拥抱 强压力拥抱
A B C D A B C D A B C D
1 3 3 3 2 7 7 7 6 6 6 6 5
2 6 7 6 6 6 6 7 5 6 5 6 4
3 6 6 6 5 6 6 6 4 6 6 6 4
4 7 4 6 4 6 6 5 6 6 3 3 3
5 7 6 5 6 5 6 7 7 6 7 6 6
平均分 5.8 5.2 5.2 4.6 6.0 6.2 6.4 5.6 6.0 5.4 5.4 4.4
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