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Research and Design of Smart Care-Giving Clothing for Disabled Elderly

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In current society, smart clothing technology has become a critical way to improve the life quality of the elderly. This work conducted a category product evaluation and user demand system through the bibliometrics method, product evaluation analysis, focus group interviews and questionnaires. Based on the system, we designed smart clothing from the aspects of the clothing structure, hardware, software program and intelligent terminal platform to meet the needs of disabled elderly people and their caregivers in multiple scenarios. According to the test results of the smart clothing, the average error of temperature and humidity monitoring is 0.20℃ and 2.88%RH. The time of putting on-taking off clothing in 6 representative daily scenarios was reduced by 51.67%. The daily body checking times, uncomfortable behaviours, and the anxiety of caregivers was decreased by 42.31%, 28.52% and 74.37%. Compared with ordinary clothing, six of the eight comfort performances are basically the same, and two are slightly worse.
Rocznik
Strony
21--31
Opis fizyczny
Bibliogr. 22 poz., rys., tab.
Twórcy
autor
  • School of Design, Jiangnan University, 214122 Wuxi, P.R. China
autor
  • School of Design, Jiangnan University, 214122 Wuxi, P.R. China
autor
  • School of Design, Jiangnan University, 214122 Wuxi, P.R. China
autor
  • School of Design, Jiangnan University, 214122 Wuxi, P.R. China
autor
  • School of Design, Jiangnan University, 214122 Wuxi, P.R. China
Bibliografia
  • 1. Wang, J.; Wang, Y.; Cai, H.; Zhang, J.; Pan, B.; Bao, G.; Guo, T. Analysis of the status quo of the Elderly’s demands of medical and elderly care combination in the underdeveloped regions of Western China and its influencing factors: a case study of Lanzhou. BMC geriatrics 2020, 20, 1–17.
  • 2. Tamura, T. Home geriatric physiological measurements. Physiological Measurement 2012, 33, R47.
  • 3. Korzeniewska, E.; Krawczyk, A.; Mróz, J.; Wyszyńska, E.; Zawiślak, R. Applications of smart textiles in post-stroke rehabilitation. Sensors 2020, 20, 2370.
  • 4. Bariya, M.; Li, L.; Ghattamaneni, R.; Ahn, C.H.; Nyein, H.Y.Y.; Tai, L.C.; Javey, A. Glove-based sensors for multimodal monitoring of natural sweat. Science advances 2020, 6, eabb8308.
  • 5. Wang, Q.; Timmermans, A.; Chen, W.; Jia, J.; Ding, L.; Xiong, L.; Rong, J.; Markopoulos, P. Stroke patients’ acceptance of a smart garment for supporting upper extremity rehabilitation. IEEE journal of translational engineering in health and medicine 2018, 6, 1–9.
  • 6. Luo, J.; Mao, A.; Zeng, Z. Sensor-based smart clothing for women’s menopause transition monitoring. Sensors 2020, 20, 1093.
  • 7. Geng, T.; Jia, X.; Guo, Y. Lower Limb Joint Nursing and Rehabilitation System Based on Intelligent Medical Treatment. Journal of Healthcare Engineering 2021, 2021.
  • 8. Yapici, M.K.; Alkhidir, T.E. Intelligent medical garments with graphenefunctionalized smart-cloth ECG sensors. Sensors 2017,17, 875.
  • 9. Cao H, Ji X. Prediction of Garment Production Cycle Time Based on a Neural Network. FIBRES & TEXTILES in Eastern Europe 2021; 29, 1(145): 8-12. DOI: 10.5604/01.3001.0014.5036
  • 10. Xiangfang, R.; Lei, S.; Miaomiao, L.; Xiying, Z.; Han, C. Research and sustainable design of wearable sensor for clothing based on body area network. Cognitive Computation and Systems 2021.
  • 11. Norman, D.A. Emotional design: Why we love (or hate) everyday things; Basic Civitas Books, 2004.
  • 12. Fernández-Caramés, T.M.; Fraga-Lamas, P. Towards the Internet of smart clothing: A review on IoT wearables and garments for creating intelligent connected e-textiles. Electronics 2018, 7, 405.
  • 13. An, B.W.; Shin, J.H.; Kim, S.Y.; Kim, J.; Ji, S.; Park, J.; Lee, Y.; Jang, J.; Park, Y.G.; Cho, E.; others. Smart sensor systems for wearable electronic devices. Polymers 2017, 9, 303.
  • 14. Han Ch, Lei S, Shaogeng Z, Mingming W, Ying T. Man-algorithm Cooperation Intelligent Design of Clothing Products in Multi Links. FIBRES & TEXTILES in Eastern Europe 2022; 30, 1(151): 59-66. DOI: 10.5604/01.3001.0015.6462
  • 15. Lu, Z., & Chen, Y. (2022). Pyramid frequency network with spatial attention residual refinement module for monocular depth estimation. Journal of Electronic Imaging, 31(2), 023005.
  • 16. Dong, K.; Peng, X.; Wang, Z.L. Fiber/fabric-based piezoelectric and triboelectric nanogenerators for flexible/stretchable and wearable electronics and artificial intelligence. Advanced Materials 2020, 32, 1902549.
  • 17. Roberts, S.C.; Owen, R.C.; Havlicek, J. Distinguishing between perceiver and wearer effects in clothing color-associated attributions. Evolutionary Psychology 2010, 8, 147470491000800304.
  • 18. Fang, L.; Clausen, G.; Fanger, P.O. Impact of temperature and humidity on perception of indoor air quality during immediate and longer whole-body exposures. Indoor Air 1998, 8, 276–284.
  • 19. Polomano, R.C.; Dunwoody, C.J.; Krenzischek, D.A.; Rathmell, J.P. Perspective on pain management in the 21st century. Pain management nursing 2008, 9, 3–10.
  • 20. Qi, W., & Su, H. (2022). A Cybertwin based Multimodal Network for ECG Patterns Monitoring using Deep Learning. IEEE Transactions on Industrial Informatics.
  • 21. Qi, W., & Aliverti, A. (2019). A multimodal wearable system for continuous and realtime breathing pattern monitoring during daily activity. IEEE journal of biomedical and health informatics, 24(8), 2199-2207.
  • 22. Zhang, X.; Lei, S.; Ying, T. Research on the design of smart mountaineering gear based on solar power technology. Textile and Apparel 2020, 30, 231–238.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-49012025-661f-4671-8e66-66847bacb16a
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