Journal of Textile Research ›› 2024, Vol. 45 ›› Issue (02): 214-220.doi: 10.13475/j.fzxb.20231005401

• Apparel Engineering • Previous Articles     Next Articles

Personalized customization of curved surface pillows based on machine vision

GE Sumin1, LIN Ruibing1, XU Pinghua1,2,3(), WU Siyi1, LUO Qianqian1   

  1. 1. School of Fashion Design & Engineering, Zhejiang Sci-Tech University, Hangzhou, Zhejiang 310018, China
    2. Digital Intelligence Style and Creative Design Research Center, Key Research Center of Philosophy and Social Sciences, Zhejiang Sci-Tech University, Hangzhou, Zhejiang 310018, China
    3. Key Laboratory of Silk Culture Inheriting and Products Design Digital Technology, Ministry of Culture and Tourism, Hangzhou, Zhejiang 310018, China
  • Received:2023-10-16 Revised:2023-11-24 Online:2024-02-15 Published:2024-03-29

RichHTML

8

PDF (PC)

37

Abstract:

Objective Curved surface pillows, as innovative bedding products designed to provide comfortable support and promote quality sleep, are characterized by their customization of pillow curvature based on individual features and needs. This customization aims to accommodate the shape of the head and neck, improve sleeping postures, and alleviate pressure from the neck and shoulders. With growing consumer concerns on health, the demands towards personalized customization of curved surface pillows continue to rise. To enhance the adaptability of curved surface pillows and meet the demands for large-scale personalized customization, a method for personalized customization of curved surface pillows based on machine vision is proposed.

Method In the experiment, photographs of head, neck, and shoulder areas of participants were captured using a mobile phone. MINet saliency object detection was employed to segment human subjects and extract their contours. 4 key reference points and 32 feature points within the human subjects were extracted. Based on this information, contour curves were fitted and dimensions were measured, resulting in the construction of a sample dataset. These data were then imported into the SPSS system for data analysis. The classification and summary function to select variables were used to calculate the mean and variance and to find the relationship between variables for classification.

Results In the experiment, 65 valid sample datasets were collected to verify the method's discriminative capability. These datasets included measurements of participants' head and neck areas and sleeping posture information. Measurements revealed irregular curves and unevenness in the back of the neck for each individual. The degree of curvature and unevenness varied significantly among participants. However, if a one-person-one-version customization method was to be adopted, there would be problems such as long cycle time and high cost. Therefore, the collected head and neck samples were classified into 12 categories based on the different degrees of curvature of the posterior neck, which was primarily a classification and summary of the measured sample's (shoulder width - width between ears)/2 data and peak and valley values. Through calculation and data association, it was found that the lateral lying height of the pillow was divided into 12 categories with an interval length of 0.5 cm, the peak curve was divided into an interval length of 0.35 cm, and the valley curve was divided into an interval length of 0.3 cm. Such classifications allowed for the selection of the appropriate category based on each participant's head shape and size, enabling the customization of a personalized curved surface pillow tailored to their individual needs. Consequently, each individual would have a pillow customized to their unique head features, ultimately enhancing sleep comfort and quality. By extracting and classifying sample data, models of curved surface pillows that fit individual data were created. Pillows customized based on individual measurements were found to better accommodate the shape of the individual's head. On the one hand, further expanding the shape adaptability of curved pillows would effectively reduce the occurrence of neck pain and discomfort, and improve sleep quality, and on the other hand, it would also provide a classification method reference for the customized market of curved pillows.

Conclusion Associating shape data of personalized pillows with user information, the complete data for personalized pillows is formed, and the system uses three-dimensional modeling software to create models of curved surface pillows based on individual head shapes and sleeping postures. This approach takes into account the neck morphology and curves, providing better neck support, making individuals more comfortable during sleep, and enhancing sleep quality. At the same time, it also provides a new design method for pillow customization, improving customization efficiency, and saving production costs. However, this experiment lacks analysis of actual neck pressure. Due to long-term use and compression, the height and shape of the pillow may change. In future research, the introduction of pressure sensing technology to monitor actual neck pressure in real time could optimize pillow design parameters.

Key words: machine vision, curved surface pillow, personalized customization, neural network, contour extraction

CLC Number: 

  • TS107.7

Fig. 1

Pipeline of online personalized customization of curved surface pillows"

Fig. 2

Effectiveness of human subject segmentation. (a) Frontal view; (b) Frontal view segmentation image; (c) Side view; (d) Side view segmentation image"

Fig. 3

Contour extraction results. (a) Frontal view contour; (b) Side view contour"

Fig. 4

Human body key point diagram"

Fig. 5

Locatization diagram of two ear points and shoulder points"

Tab. 1

Range of key parameters for different categoriescm"

类型 侧卧区域高度 曲线峰值 曲线谷值
1 <5 <2 <1
2 5~5.5 2~2.35 1~1.3
3 5.5~6 2.35~2.7 1.3~1.6
4 6~6.5 2.7~3.05 1.6~1.9
5 6.5~7 3.05~3.4 1.9~2.2
6 7~7.5 3.4~3.75 2.2~2.5
7 7.5~8 3.75~4.1 2.5~2.8
8 8~8.5 4.1~4.45 2.8~3.1
9 8.5~9 4.45~4.8 3.1~3.4
10 9~9.5 4.8~5.15 3.4~3.7
11 9.5~10 5.15~5.5 3.7~4
12 >10 >5.5 >4

Fig. 6

Schematic representation of curve fitting. (a) Curve fitting; (b) Dimension measurement"

Fig. 7

3-D visual representation of personalized customized pillow"

Fig. 8

Illustration of sectioned curved surface pillow. (a) Supine sleeping area; (b) Lateral sleeping area"

Fig. 9

Sample data for side-sleeping area"

Fig. 10

Sample data for supine-sleeping area"

Fig. 11

Corresponding coordinate diagram for dimension data. (a) X-Y coordinate plane; (b)3-D coordinate map"

Fig. 12

Measurement of curved surface pillow height. (a) Supine diagram; (b) Size corresponding diagram; (c) Dimension measurement"

[1] DEYONKER O, ZEE P, REID K, et al. Association with sleep duration and quality among healthy adults[J]. Sleep, 2017, 40: A299.
[2] ADAMS R J, APPLETON S L, TAYLOR A W, et al. Sleep health of Australian adults in 2016: results of the 2016 Sleep Health Foundation national survey[J]. Sleep Health, 2017, 3(1): 35-42.
doi: S2352-7218(16)30129-2 pmid: 28346149
[3] MITH A, JONES B, BROWN C, et al. The impact of personalized pillow design on sleep quality in individuals with chronic neck pain[J]. Journal of Pain Management, 2016, 9(4): 389-396.
[4] PARK S, LEE H, KIM M. Effect of personalized pillow design on sleep quality and neck pain[J]. Applied Ergonomics, 2017, 58: 222-229.
[5] SON J, JUNG S, SONG H, et al. A survey of Koreans on sleep habits and sleeping symptoms relating to pillow comfort and support[J]. International Journal of Environmental Research and Public Health, 2020, 17(1): 302.
doi: 10.3390/ijerph17010302
[6] TOWNSI N, LAING I A, HALL G L, et al. The impact of respiratory viruses on lung health after preterm birth[J]. European Clinical Respiratory Journal, 2018, 5(1): 1487214.
doi: 10.1080/20018525.2018.1487214
[7] 王中昱, 苏云, 王云仪. 机器学习建立的个体热舒适模型及其在服装领域的应用展望[J]. 纺织学报, 2023, 44(5): 228-236.
WANG Zhongyu, SU Yun, WANG Yunyi. Individual thermal comfort model established by machine learning and its application prospects in the field of clothing[J]. Journal of Textile Research, 2023, 44(5): 228-236.
[8] SON J, JUNG S, SONG H, et al. A survey of Koreans on sleep habits and sleeping symptoms relating to pillow comfort and support[J]. International Journal of Environmental Research and Public Health, 2020, 17(1): 302.
doi: 10.3390/ijerph17010302
[9] COTTENGIM C, PARKS S E, ERCK LAMBERT A B, et al. U-shaped pillows and sleep-related infant deaths, United States, 2004-2015[J]. Maternal and Child Health Journal, 2020, 24: 222-228.
doi: 10.1007/s10995-019-02847-9 pmid: 31828577
[10] RADWAN A, FESS P, JAMES D, et al. Effect of different mattress designs on promoting sleep quality, pain reduction, and spinal alignment in adults with or without back pain; systematic review of controlled trials[J]. Sleep Health, 2015, 1(4): 257-267.
doi: S2352-7218(15)00140-0 pmid: 29073401
[11] LEE J, PARK H, KIM S. A study on personalized pillow design using anthropometric data and pressure distribution analysis[J]. Journal of the Ergonomics Society of Korea, 2013, 32(2): 151-158.
[12] JEON M Y, JEONG H C, LEE S W, et al. Improving the quality of sleep with an optimal pillow: a randomized, comparative study[J]. The Tohoku journal of Experimental Medicine, 2014, 233(3): 183-188.
doi: 10.1620/tjem.233.183
[13] SORKINE-HORNUNG O, RABINOVICH M. Least-squares rigid motion using svd[J]. Computing, 2017, 1(1): 1-5.
[14] BEAUMONT C A, KNOOPS P G, BORGHI A, et al. Three-dimensional surface scanners compared with standard anthropometric measurements for head shape[J]. Journal of Cranio-Maxillofacial Surgery, 2017, 45(6): 921-927.
doi: S1010-5182(17)30098-7 pmid: 28454666
[15] REN S, WONG D W C, YANG H, et al. Effect of pillow height on the biomechanics of the head-neck complex: investigation of the cranio-cervical pressure and cervical spine alignment[J]. Peer J, 2016, 4: e2397.
doi: 10.7717/peerj.2397
[16] CAI D, CHEN H L. Ergonomic approach for pillow concept design[J]. Applied Ergonomics, 2016, 52: 142-150.
doi: 10.1016/j.apergo.2015.07.004 pmid: 26360205
[17] LEE H, KIM Y, KIM J. A study on personalized pillow design using anthropometric data and pressure mapping analysis[J]. Journal of the Korean Society for Precision Engineering, 2007, 24(2): 175-182.
[18] 王曙东, 马倩, 王可, 等. 3D生物打印制备组织工程支架的研究进展[J]. 纺织学报, 2023, 44(3): 210-220.
WANG Shudong, MA Qian, WANG Ke, et al. Research progress in 3D bioprinting for tissue engineering sca-ffolds[J]. Journal of Textile Research, 2023, 44(3): 210-220.
[19] KURAMOTO A, INUI Y, ICHIKAWA T, et al. Comfortable pillow design based on individual basis[J]. International Information Institute (Tokyo). Information, 2017, 20(9A): 6627-6644.
[1] SHI Weimin, HAN Sijie, TU Jiajia, LU Weijian, DUAN Yutang. Empty yarn bobbin positioning method based on machine vision [J]. Journal of Textile Research, 2023, 44(11): 105-112.
[2] LEI Junjie, SHEN Chunya, HU Xudong, RU Xin, PENG Laihu. Large-scale scheduling of weaving workshop based on NSGAII and neural network [J]. Journal of Textile Research, 2023, 44(11): 208-215.
[3] LIU Rong, XIE Hong. Similarity pattern matching technology based on garment structural feature recognition [J]. Journal of Textile Research, 2023, 44(10): 134-142.
[4] CHEN Jinwen, WANG Xin, LUO Weihao, MEI Chennan, WEI Jingyan, ZHONG Yueqi. VR-oriented personalized head and face texture generation technology of dressed human body [J]. Journal of Textile Research, 2023, 44(09): 188-196.
[5] REN Guodong, TU Jiajia, LI Yang, QIU Zian, SHI Weiming. Yarn state detection based on lightweight network and knowledge distillation [J]. Journal of Textile Research, 2023, 44(09): 205-212.
[6] YAO Linhan, ZHANG Ying, YAO Lan, ZHENG Xiaoping, WEI Wenda, LIU Chengxia. Embroidery style transfer modeling based on multi-scale texture synthesis [J]. Journal of Textile Research, 2023, 44(09): 84-90.
[7] CHEN Gang, JIN Guiyang, WU Jing, LUO Qian. Research and development of key technologies and whole-set equipment for intelligent sewing [J]. Journal of Textile Research, 2023, 44(08): 197-204.
[8] CHEN Taifang, ZHOU Yaqin, WANG Junliang, XU Chuqiao, LI Dongwu. Online detection of yarn breakage based on visual feature enhancement and extraction [J]. Journal of Textile Research, 2023, 44(08): 63-72.
[9] FU Han, HU Feng, GONG Jie, YU Lianqing. Defect reconstruction algorithm for fabric defect detection [J]. Journal of Textile Research, 2023, 44(07): 103-109.
[10] JI Yue, PAN Dong, MA Jiedong, SONG Limei, DONG Jiuzhi. Yarn tension non-contacts detection system on string vibration based on machine vision [J]. Journal of Textile Research, 2023, 44(05): 198-204.
[11] LI Xueliang, DU Yuhong, REN Weijia, ZUO Hengli. Classification and identification of foreign fibers based on near-infrared spectroscopy and ResNet [J]. Journal of Textile Research, 2023, 44(05): 84-92.
[12] CAI Liling, REN Qianbin, JI Xiaofen, XIAO Zengrui, ZHANG Yiling. Leg style perception evaluation and personalized customization of women's sports trousers [J]. Journal of Textile Research, 2023, 44(04): 165-171.
[13] TAO Jing, WANG Junliang, XU Chuqiao, ZHANG Jie. Feature extraction method for ring-spun-yarn evenness online detection based on visual calibration [J]. Journal of Textile Research, 2023, 44(04): 70-77.
[14] GU Bingfei, ZHANG Jian, XU Kaiyi, ZHAO Songling, YE Fan, HOU Jue. Human contour and parameter extraction from complex background [J]. Journal of Textile Research, 2023, 44(03): 168-175.
[15] WANG Bin, LI Min, LEI Chenglin, HE Ruhan. Research progress in fabric defect detection based on deep learning [J]. Journal of Textile Research, 2023, 44(01): 219-227.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
京ICP备14006648号
Copyright 2021 © Editorial office of Journal of Textile Research
Supported by Beijing Magtech Co.Ltd