Journal of Textile Research ›› 2025, Vol. 46 ›› Issue (11): 255-263.doi: 10.13475/j.fzxb.20250504102

• Comprehensive Review • Previous Articles     Next Articles

Research status and development of intelligent fall injury protection clothing for the elderly

FAN Shuyue1,2,3, WANG Zhaohui1,2,3(), LIU Huanhuan1,2,3, YE Qinwen1,2,3   

  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:2025-05-21 Revised:2025-07-22 Online:2025-11-15 Published:2025-11-15
  • Contact: WANG Zhaohui E-mail:wzh_sh2007@dhu.edu.cn

Abstract:

Significance With the acceleration of population aging, falls among the elderly have become a major public health concern, often resulting in disability or death. Epidemiological studies show that 30%-50% of older adults fall each year, with 5%-10% suffering serious injuries such as fractures or head trauma. Over 95% of hip fractures are caused by falls, posing serious threats to elderly health and independence. Conventional fall prevention approaches, like environmental modifications or exercise, are pre-emptive and provide limited protection during actual falls. Thus, developing smart wearable products with real-time sensing and active protection capabilities is vital to reduce fall-related injuries. +++Progress Early fall protection products, such as hip pads, offer limited protection due to their restricted coverage, discomfort, and poor appearance, resulting in low user compliance. In recent years, the rapid development of smart wearable technology has greatly promoted the research and development and application of smart fall protection clothing. Intelligent fall protection systems usually consist of two core parts, namely, intelligent monitoring module and active protection module. The system collects the user's motion status data in real time via sensors, and uses the threshold method or machine learning algorithms to identify falls quickly and accurately. When the system detects a fall, the control module immediately commands the airbag inflator to rapidly deploy the protective structure to protect high-risk areas such as the head, hip, and spine before the human body touches the ground, effectively reducing the risk of injury. In addition, some studies have integrated wireless communication modules into the system, which automatically sends alarm information to caregivers or medical terminals when the fall is triggered, thus realising timely positioning and rescue interventions after the fall. Currently, intelligent fall protection products based on airbags have been widely used in high-risk sports, but wearable protection products for the daily life of the elderly are still at an early stage of research and development, and need to be further optimised and improved in terms of comfort, maturity and stability. +++Conclusion and Prospect Intelligent fall injury protection clothing has shown significant advantages in real-time monitoring, efficient protection and good wearing experience in dealing with the risk of falling in the elderly, which has broad research value and application potential. Based on the current research status and future trends, the further development of intelligent fall injury protection clothing should focus on the following aspects. For intelligent recognition and algorithm optimization, multimodal sensor data and deep learning are incorporated to enhance fall detection accuracy and adaptability to complex real-world conditions. In system integration and module miniaturization, flexible electronics and textile circuits are promoted to achieve embedded and invisible designs that enhance wearability, reduce weight, and improve device reliability. In achieving lightweight materials and flexible structure, high-strength, lightweight airbag materials are developed and fabric structures and coatings are optimized to enhance pressure resistance and comfort. Structural innovations, such as foldable or hollow support designs, can improve conformity to the body while reducing overall weight. In the case of age-friendly design and wearing experience, tailored designs are carried out for elderly users by focusing on loose fits, ease of wearing, intuitive interfaces, and soft, skin-friendly materials. These adaptations aim to increase acceptance, safety, and everyday usability. In terms of multifunctional expansion and intelligent linkage, features are extended beyond fall protection by integrating heart rate, blood oxygen, and respiration monitoring. Combining positioning and communication modules enables automatic alerts and remote response. Linkage with intelligent household systems and elderly care platforms will help build multi-level safety networks and improve home care services. With continued advancements in cross-disciplinary innovation, intelligent airbag-based protection clothing is expected to become a practical and efficient solution to the increasing fall risk faced by aging populations.

Key words: fall in the elderly, intelligent clothing, protection clothing, airbag protection system, wearable sensor, fall detection algorithm

CLC Number: 

  • TS941

Fig.1

Types of falls and vulnerable areas for falls"

Fig.2

Key modules of intelligent fall injury prevention clothing system"

Fig.3

Classification of automatic fall monitoring technologies"

Fig.4

Overall integrated design schematic diagram of intelligent fall protection suits. (a) Vest; (b) Belt"

[1] 中国疾病预防控制中心慢性非传染性疾病预防控制中心介绍[J/OL]. 伤害医学(电子版), 2018, 7(2): 2, 65.
Introduction of China center for disease control and prevention center for chronic non-communicable diseases[J/OL]. Injury Medicine (Electronic Edition), 2018, 7(2): 2, 65.
[2] HADDAD Y K, BERGEN G, FLORENCE C S. Estimating the economic burden related to older adult falls by state[J]. Journal of Public Health Management and Practice, 2019, 25(2): E17-E24.
[3] 刘洋. 我国社区老年人跌倒状况及居家环境影响因素研究[D]. 北京: 北京协和医学院, 2025:1-10.
LIU Yang. Fall prevalence and the home environmental factors among community-dwelling older adults in China[D]. Beijing: Peking Union Medical College, 2025:1-10.
[4] 于晓坤, 关娟娟, 罗洋. 女性老年人髋关节抗冲击保护裤装的设计开发[J]. 东华大学学报(自然科学版), 2020, 46(5): 733-739.
YU Xiaokun, GUAN Juanjuan, LUO Yang. Design and development of anti-impact hip protection pants for elderly female[J]. Journal of Donghua University (Natural Science), 2020, 46(5): 733-739.
[5] QUIGLEY P A, SINGHATAT W, TARBERT R J. Technology innovation to protect hips from fall-related fracture[J]. Physical Medicine and Rehabilitation Research, 2019, 4: 1-4.
[6] XIMENES M A M, BRANDÃO M G S A, MACÊDO T S, et al. Efetividade de tecnologia educacional Para prevenção de Quedas em ambiente hospitalar[J]. Acta Paulista de Enfermagem, 2022, 35: eAPE01372.
[7] PIERLEONI P, BELLI A, PALMA L, et al. A high reliability wearable device for elderly fall detection[J]. IEEE Sensors Journal, 2015, 15(8): 4544-4553.
doi: 10.1109/JSEN.2015.2423562
[8] AMBROSE A F, PAUL G, HAUSDORFF J M. Risk factors for falls among older adults: a review of the literature[J]. Maturitas, 2013, 75(1): 51-61.
doi: 10.1016/j.maturitas.2013.02.009 pmid: 23523272
[9] MAO L Y, LIANG D, NING Y K, et al. Pre-impact and impact detection of falls using built-In tri-accelerometer of smartphone[C]// Health Information Science. Cham: Springer International Publishing, 2014: 167-174.
[10] MORELAND B, KAKARA R, HENRY A. Trends in nonfatal falls and fall-related injuries among adults aged ≥65 years-United States, 2012-2018[J]. MMWR Morbidity and Mortality Weekly Report, 2020, 69(27): 875-881.
doi: 10.15585/mmwr.mm6927a5
[11] JAMES S L, LUCCHESI L R, BISIGNANO C, et al. The global burden of falls: global, regional and national estimates of morbidity and mortality from the global burden of disease study 2017[J]. Injury Prevention, 2020, 26(Supp 1): i3-i11.
[12] GANZ D A, LATHAM N K. Prevention of falls in community-dwelling older adults[J]. New England Journal of Medicine, 2020, 382(8): 734-743.
doi: 10.1056/NEJMcp1903252
[13] VAISHYA R, VAISH A. Falls in older adults are serious[J]. Indian Journal of Orthopaedics, 2020, 54(1): 69-74.
doi: 10.1007/s43465-019-00037-x pmid: 32257019
[14] CIANFEROTTI L, FOSSI C, BRANDI M L. Hip protectors: are they worth it?[J]. Calcified Tissue International, 2015, 97(1): 1-11.
doi: 10.1007/s00223-015-0002-9 pmid: 25926045
[15] 陈凌娴, 李俊, 王敏. 护膝防护性能及其功能设计研究进展[J]. 毛纺科技, 2022, 50(3): 117-123.
CHEN Lingxian, LI Jun, WANG Min. Research progress of the protective performance and functional design of kneepad[J]. Wool Textile Journal, 2022, 50(3): 117-123.
[16] AHN S, CHOI D, KIM J, et al. Optimization of a pre-impact fall detection algorithm and development of hip protection airbag system[J]. Sensors and Materials, 2018, 30(8): 1743.
doi: 10.18494/SAM.2018.1876
[17] JEONG Y, AHN S, KIM J, et al. Impact attenuation of the soft pads and the wearable airbag for the hip protection in the elderly[J]. International Journal of Precision Engineering and Manufacturing, 2019, 20(2): 273-283.
doi: 10.1007/s12541-019-00053-9
[18] ZHONG Z C, CHEN F Y, ZHAI Q, et al. A real-time pre-impact fall detection and protection system[C]// 2018 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM). New York: IEEE, 2018: 1039-1044.
[19] BIAN Z P, HOU J H, CHAU L P, et al. Fall detection based on body part tracking using a depth camera[J]. IEEE Journal of Biomedical and Health Informatics, 2015, 19(2): 430-439.
doi: 10.1109/JBHI.2014.2319372
[20] ZIGEL Y, LITVAK D, GANNOT I. A method for automatic fall detection of elderly people using floor vibrations and sound: proof of concept on human mimicking doll falls[J]. IEEE Transactions on Bio-Medical Engineering, 2009, 56(12): 2858-2867.
doi: 10.1109/TBME.10
[21] BOUTELLAA E, KERDJIDJ O, GHANEM K. Covariance matrix based fall detection from multiple wearable sensors[J]. Journal of Biomedical Informatics, 2019, 94: 103189.
doi: 10.1016/j.jbi.2019.103189
[22] ROUGIER C, MEUNIER J, ST-ARNAUD A, et al. 3D head tracking for fall detection using a single calibrated camera[J]. Image and Vision Computing, 2013, 31(3): 246-254.
doi: 10.1016/j.imavis.2012.11.003
[23] WANG S K, CHEN L, ZHOU Z X, et al. Human fall detection in surveillance video based on PCANet[J]. Multimedia Tools and Applications, 2016, 75(19): 11603-11613.
doi: 10.1007/s11042-015-2698-y
[24] LI M, XU G H, HE B, et al. Pre-impact fall detection based on a modified zero moment point criterion using data from kinect sensors[J]. IEEE Sensors Journal, 2018, 18(13): 5522-5531.
doi: 10.1109/JSEN.2018.2833451
[25] DAHER M, EL BADAOUI EL NAJJAR M, et al. Automatic fall detection system using sensing floors[J]. International Journal of Computing and Information Sciences, 2016, 12(1): 75-82.
doi: 10.21700/ijcis
[26] SHALINI V B, BEENAPATI C, A J, et al. Intelligent fall protection device for geriatric people[C]// 2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI). New York: IEEE, 2024: 831-835.
[27] VAN THANH P, TRAN D T, NGUYEN D C, et al. Development of a real-time, simple and high-accuracy fall detection system for elderly using 3-DOF accelerometers[J]. Arabian Journal for Science and Engineering, 2019, 44(4): 3329-3342.
doi: 10.1007/s13369-018-3496-4
[28] WU F L, ZHAO H Y, ZHAO Y, et al. Development of a wearable-sensor-based fall detection system[J]. International Journal of Telemedicine and Applications, 2015, 2015: 576364.
[29] DE QUADROS T, LAZZARETTI A E, SCHNEIDER F K. A movement decomposition and machine learning-based fall detection system using wrist wearable device[J]. IEEE Sensors Journal, 2018, 18(12): 5082-5089.
doi: 10.1109/JSEN.2018.2829815
[30] SHI G Y, ZHANG J Y, DONG C, et al. Fall detection system based on inertial mems sensors: analysis design and realization[C]// 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER). New York: IEEE, 2015: 1834-1839.
[31] MAO A H, MA X D, HE Y N, et al. Highly portable, sensor-based system for human fall monitoring[J]. Sensors, 2017, 17(9): 2096.
doi: 10.3390/s17092096
[32] SUCERQUIA A, LÓPEZ J D, VARGAS-BONILLA J F. Real-life/real-time elderly fall detection with a triaxial accelerometer[J]. Sensors, 2018, 18(4): 1101.
doi: 10.3390/s18041101
[33] 涂亚庆, 陈鹏, 陈宝欣, 等. 基于离散特征的跌倒检测智能方法及应用[J]. 仪器仪表学报, 2017, 38(3):629-634.
TU Yaqing, CHEN Peng, CHEN Baoxin, et al. Intelligent fall detection method based on discrete feature and its application[J]. Journal of Instrumentation, 2017, 38(3): 629-634.
[34] HUANG C N, CHAN C T. A ZigBee-based location-aware fall detection system for improving elderly tele-care[J]. International Journal of Environmental Research and Public Health, 2014, 11(4): 4233-4248.
doi: 10.3390/ijerph110404233
[35] PHU P T, HAI N T, TAM N T. A threshold algorithm in a fall alert system for elderly people[M]// 5th International Conference on Biomedical Engineering in Vietnam. Cham: Springer International Publishing, 2015: 347-350.
[36] 彭亚平, 贺乾格, 柯希垚, 等. 一种基于加速度传感器的摔倒检测腰带[J]. 电子测量技术, 2018, 41(11): 117-120.
PENG Yaping, HE Qiange, KE Xiyao, et al. An anti-fall detection belt based on accelerometer[J]. Electronic Measurement Technology, 2018, 41(11): 117-120.
[37] HSIEH C Y, LIU K C, HUANG C N, et al. Novel hierarchical fall detection algorithm using a multiphase fall model[J]. Sensors, 2017, 17(2): 307.
doi: 10.3390/s17020307
[38] PUTRA I P E S, BRUSEY J, GAURA E, et al. An event-triggered machine learning approach for accelerometer-based fall detection[J]. Sensors, 2017, 18(1): 20.
doi: 10.3390/s18010020
[39] 何坚, 周明我, 王晓懿. 基于卡尔曼滤波与k-NN算法的可穿戴跌倒检测技术研究[J]. 电子与信息学报, 2017, 39(11): 2627-2634.
HE Jian, ZHOU Mingwo, WANG Xiaoyi. Wearable method for fall detection technology based on Kalman filter and k-NN algorithm[J]. Journal of Electronics and Information Technology, 2017, 39(11): 2627-2634.
[40] HNOOHOM N, JITPATTANAKUL A, INLUERGSRI P, et al. Multi-sensor-based fall detection and activity daily living classification by using ensemble learning[C]// 2018 International ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI-NCON). New York: IEEE, 2018: 111-115.
[41] AL-OKBY M F R, AL-BARRAK S S. New approach for fall detection system using embedded technology[C]// 2020 IEEE 24th International Conference on Intelligent Engineering Systems (INES). New York: IEEE, 2020: 209-214.
[42] LUO Y L, SHI G Y, LAM J, et al. Towards a human airbag system using/SPL MU/IMU with SVM training for falling-motion recognition[C]// 2005 IEEE International Conference on Robotics and Biomimetics-ROBIO. New York: IEEE, 2006: 634-639.
[43] TAMURA T, YOSHIMURA T, SEKINE M, et al. A wearable airbag to prevent fall injuries[J]. IEEE Transactions on Information Technology in Biomedicine, 2009, 13(6): 910-914.
doi: 10.1109/TITB.2009.2033673 pmid: 19846379
[44] Hip'Air可穿戴式安全气囊,给臀部多一重保护[EB/OL].(2018-01-09)[2025-05-08]. https://k.sina.cn/article_1646897097_6229a7c900100227p.html.
Hip'Air wearable airbag provides an extra layer of protection for your hips[EB/OL]. (2018-01-09) [2025-05-08]. https://k.sina.cn/article_1646897097_6229a7c900100227p.html.
[45] 苏州衣带保智能技术有限公司[EB/OL]. [2025-05-08]. http://wx.zgznhh.com/.
Suzhou Yidai Bao Intelligent Technology Co., Ltd.[EB/OL]. [2025-05-08]. http://wx.zgznhh.com/.
[46] 信安智囊-智能气囊防护大师[EB/OL]. [2025-05-08]. https://s-airbag.com/.
Security Expert-Intelligent Airbag protection master[EB/OL]. [2025-05-08]. https://s-airbag.com/.
[47] 诸文旎, 祝国成. 安全气囊织物发展现状[J]. 现代纺织技术, 2021, 29(3): 40-44.
ZHU Wenni, ZHU Guocheng. Development status of airbag fabric[J]. Advanced Textile Technology, 2021, 29(3): 40-44.
[48] 日本发明穿戴式安全气囊[EB/OL]. [2025-05-08]. https://news.sohu.com/20080926/n259757656.shtml.
Japanese invention of wearable safety airbags[EB/OL]. [2025-05-08]. https://news.sohu.com/20080926/n259757656.shtml.
[49] AHN S, KIM J, KOO B, et al. Evaluation of inertial sensor-based pre-impact fall detection algorithms using public dataset[J]. Sensors, 2019, 19(4): 774.
doi: 10.3390/s19040774
[50] WANG S B, SUN J X, LIU S W. Fall prevention system based on airbag protection and mechanical exoskeleton support[J]. MATEC Web of Conferences, 2021, 336: 02015.
doi: 10.1051/matecconf/202133602015
[51] Compare D-air ® Models[EB/OL]. [2025-05-08]. https://www.dainese.com/im/en/compared-air-models.html.
[52] SHI G Y, CHAN C S, LI W J, et al. Mobile human airbag system for fall protection using MEMS sensors and embedded SVM classifier[J]. IEEE Sensors Journal, 2009, 9(5): 495-503.
doi: 10.1109/JSEN.2008.2012212
[53] 刘欢欢, 孟虎, 王朝晖. 适老化智能可穿戴设计研究进展及发展趋势[J]. 纺织学报, 2024, 45(3): 236-243.
LIU Huanhuan, MENG Hu, WANG Zhaohui. Progress and trends in application of wearable technology for elderly population[J]. Journal of Textile Research, 2024, 45(3): 236-243.
[54] MUHAMMAD K, FARGHALY S, ALASWAD M, et al. Functional design methods for elderly clothes[J]. Journal of Textiles, Coloration and Polymer Science, 2024, 21(2):285-291.
[55] LU H C, WU F G, YANG W Y, et al. The clothing design for the elderly care[M]// Human-computer interaction. design practice in contemporary societies. Cham: Springer International Publishing, 2019: 33-46.
[56] 杨璨. 基于户外运动监测功能的老年服装设计[J]. 上海纺织科技, 2020, 48(5): 42-45.
YANG Can. Research on the design of aged clothing based on outdoor sports monitoring function[J]. Shanghai Textile Science & Technology, 2020, 48(5): 42-45.
[1] WU Xueyang, XU Qicheng, SHAN Yinghao, LIN Xiaowu, LIU Chenming. System design for human wearable nanogrid integrating solar energy and electromagnetic energy collection [J]. Journal of Textile Research, 2025, 46(07): 202-208.
[2] ZHANG Jiacheng, YU Ying, ZUO Yuxin, GU Zhiqing, TANG Tengfei, CHEN Hongli, LÜ Yong. Torsional sensing characteristics of polyacrylonitrile/MoS2 fiber membranes based on flexoelectric effect [J]. Journal of Textile Research, 2025, 46(06): 80-87.
[3] WANG Zhongyu, SU Yun, WANG Yunyi. Development of personal comfort models based on machine learning and their application prospect in clothing engineering [J]. Journal of Textile Research, 2023, 44(05): 228-236.
[4] WEI Yuhui, ZHENG Chen, CHENG Erxiao, ZHAO Shuhan, SU Zhaowei. Preparation and properties of photocatalytic self-cleaning aramid fabrics [J]. Journal of Textile Research, 2023, 44(05): 171-176.
[5] HUANG Rui, XIAO Aimin. Research and development of special-care incontinence underwear based on temperature and humidity sensor [J]. Journal of Textile Research, 2022, 43(07): 141-148.
[6] JIN Peng, XUE Zhebin, GE Yao. New intelligent mining clothing design with real-time gas monitoring function [J]. Journal of Textile Research, 2020, 41(11): 143-149.
[7] LIU Airong, CHEN Yanmin, GE Fengyan, CAI Zaisheng, WANG Juan. Progress on fiber-based surface-enhanced Raman scattering substrates [J]. Journal of Textile Research, 2020, 41(05): 176-183.
[8] . Research progress and development trend of wearable medical monitoring clothing [J]. Journal of Textile Research, 2015, 36(06): 162-168.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!