Journal of Textile Research ›› 2026, Vol. 47 ›› Issue (1): 268-276.doi: 10.13475/j.fzxb.20250605302

• Comprehensive Review • Previous Articles     Next Articles

Research progress in e-textiles based on machine learning model

HU Weilin1, BAI Jie2, LIU Dan2, BAI Meng2, LI Juan2, LI Qizheng3()   

  1. 1. College of Textile Science and Engineering ( International Institute of Silk ), Zhejiang Sci-Tech University, Hangzhou, Zhejiang 310018, China
    2. China Textile Engineering Society, Beijing 100025, China
    3. Institute of Digital and Intelligent Publishing, Zhejiang Sci-Tech University, Hangzhou, Zhejiang 310018, China
  • Received:2025-06-25 Revised:2025-11-10 Online:2026-01-15 Published:2026-01-15
  • Contact: LI Qizheng E-mail:liqizheng@zstu.edu.cn

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Abstract:

Significance E-textiles, which integrate sensing, communication, and interactive capabilities into fabrics through embedded flexible electronics, are heralding a transformative shift in the textile industry. These smart textiles have the potential to revolutionize various domains, including human-computer interaction, motion analysis, and health monitoring, by offering versatile applications that go beyond conventional garment functionality. However, the widespread adoption of e-textiles is currently hindered by several challenges. Key barriers include the complexity of adapting machine learning (ML) models to meet the demanding computational constraints of embedded devices, the difficulty of generalizing models across a diverse range of users, and the high costs associated with optimizing both materials and production processes. Despite the significant potential of e-textiles, existing research has yet to systematically explore the inherent relationships between ML models and the various e-textile structures and applications. The matching of specific ML models to the unique characteristics of e-textile structures and their intended use cases remains an unresolved challenge. By addressing this gap, the review seeks to offer valuable insights that will inform the development of intelligent, efficient, and deployable textile systems, facilitating their transition from lab prototypes to practical, real-world applications.

Progress The review examines the evolving role of machine learning in e-textiles along three key dimensions: past developments, current trends, and future directions. Historically, the application of ML models in e-textiles has been shaped by the interplay between model characteristics, the structural properties of e-textiles, and the requirements of different application scenarios. The review discusses how specific ML models have been adapted to address various needs, including human-computer interaction, motion analysis, and health monitoring. For each of these applications, the strengths and weaknesses of the algorithms are critically assessed, providing a comprehensive understanding of their effectiveness in real-world contexts. Additionally, the review highlights a series of documented cases and statistically analyzes the correlations between e-textile structures, the best-performing ML models, and application-specific requirements. These relationships are visually represented through Sankey diagrams, illustrating how the structural characteristics of e-textiles influence the selection of appropriate ML models for different application contexts. The review also identifies key trends currently shaping the field, including the integration of ML in signal processing, process optimization, and scenario-based implementations. By analyzing the temporal evolution of relevant literature, the review offers a snapshot of ongoing advancements and the trajectory of these developments.

Conclusion and Prospect The review concludes that the successful integration of ML models in e-textiles is largely dependent on the specific use case, data characteristics, and the inherent structure of the textile system. ML models like CNNs, LSTMs, and SVMs have demonstrated significant progress in enhancing performance across key application areas, including human-computer interaction, motion analysis, and health monitoring. CNNs, for example, excel in handling spatially rich data, such as pressure distribution images, making them ideal for applications like gesture recognition and handwriting analysis. LSTMs are particularly effective at modeling temporal dependencies, which is crucial for applications involving continuous motion signals. SVMs, on the other hand, offer efficiency and robustness, particularly in scenarios with limited data and well-defined features, making them a popular choice for health monitoring applications. The field is experiencing three major shifts: the movement from simple classification tasks to the decoupling of multi-modal signals in signal processing; the transition from experience-based trial-and-error approaches to model-driven optimization in manufacturing; and the evolution from generalized monitoring to personalized, precision services in application contexts. Despite these advancements, challenges remain, such as the need for lightweight models that can operate within the computational constraints of embedded devices, the difficulty of ensuring model generalization across diverse user populations, and the high cost of optimizing performance. Future research should focus on developing more efficient, lightweight models, enhancing model generalization with diverse and large datasets, and incorporating cost considerations into the design and optimization of ML-driven e-textiles. By addressing these challenges, the review lays a foundational framework for matching ML models with e-textile applications, which will be crucial in driving the intelligent development and industrial scalability of this emerging technology.

Key words: electronic textiles, machine learning, human-computer interaction, motion analysis, health monitoring, flexible sensor, smart textiles

CLC Number: 

  • TP212

Fig.1

Main product forms of e-textiles based on machine learning models"

Tab.1

Analysis of machine learning model types used in human computer interaction e-textiles"

应用
场景		 机器学习
模型		 优点 不足
手势
识别		 LSTM 擅长处理时间序列数据[17-18];避免梯度消失问题[17] 捕捉空间特征方面存在局限[17];对复杂动态信号的特征提取能力有限[19]
CNN 自动提取特征,与LSTM结合可较好处理时空任务[17] 处理时间序列数据表现较差[17,20]
SVM 小样本高准确率;参数少易调参;泛化能力强、稳健性好[21];资源占用低[22];算法简洁、运行速度快[23] 难以处理高维时空特征[17],在复杂或多类手势识别性能较差[22]
手写
识别		 CNN 提取局部空间特征[24] 处理纯时间序列任
务时性能欠佳[24];
模型复杂度
较高[25]
SVM 小数据集上高精度;稳健性强;计算效率高[26] 依赖手动特征
工程[26]
情感
识别		 CNN 自动从图像数据中提取空间特征[27] 存在过拟合倾向;
对数据质量
敏感[27]
SVM 小样本场景下表现稳健;泛化性好[28] 依赖数据质量[28]

Tab.2

Analysis of machine learning model types used in motion analysis e-textiles"

应用
场景		 机器学习
模型		 优点 不足
步态
分析		 LSTM 擅长处理时间序列数据[34] 存在过拟合的风险[35]
CNN 自动提取特征[36-37];有效提取压力分布空间特征[34] 计算成本高[36];面临梯度消失或爆炸[38]
活动
识别		 LSTM 擅长捕捉数据的时间
特征[39-40]		 高计算资源
需求[41-42]
CNN 自动提取特征[39] 缺乏时间感知
能力[34]
KNN 区分基本活动性能
较好[43]		 复杂动作分类
较弱[43]

Tab.3

Analysis of machine learning model types used in health monitoring e-textiles"

应用
场景		 机器学习
模型		 优点 不足
呼吸
监测		 SVM 稳健性好、易训练[49];多参数处理优异[50];适合小样本机器学习,避开高维空间的复杂性[51] 依赖人工特征工程,对图像或原始波形数据的处理能力有限[49]
CNN 慢速呼吸率下性能稳定[52] 受咳嗽、运动等行为影响[52]
心电
监测		 SVM 稳健性强;易训练[49];多生理参数处理优异[53];适合小样本学习,避开了高维空间的复杂性[51] 需手动提取
特征[54]
LSTM 自动识别相关特征[54] 模型复杂度
较高[54]
汗液
监测		 SVM 在预测pH值时表现出高效和非线性处理能力[55] 图像特征提取能力较弱[55]
肌电
监测		 SVM 高效识别生物信号[54] 需手动提取特征,复杂信号处理效果受限[54]

Fig.2

Scenario application and model adaptation of e-textiles with different structures"

Fig.3

Comparison of statistical learning and deep learning techniques in sleep monitoring"

Fig.4

Optimization method of process plan based on machine learning model"

[1] BERZOWSKA J. Electronic textiles: wearable computers, reactive fashion, and soft computation[J]. TEXTILE, 2005, 3(1): 58-75.
doi: 10.2752/147597505778052639
[2] AHMAD M M, AHUJA K. E-textiles: a revolutionary technology[J]. International Journal of System Assurance Engineering and Management, 2023, 14(6): 2031-2047.
[3] TIAN Y C, DING R D, YOON S S, et al. Recent advances in next-generation textiles[J]. Advanced Materials, 2025, 37(8): 2417022.
doi: 10.1002/adma.v37.8
[4] MU G R, ZHANG Y, YAN Z H, et al. Recent advancements in wearable sensors: integration with machine learning for human-machine interaction[J]. RSC Advances, 2025, 15(10): 7844-7854.
doi: 10.1039/D5RA00167F
[5] SHEN T C, DI GIULIO I, HOWARD M. Human movement prediction with wearable sensors on loose clothing[C]// 2024 IEEE-RAS 23rd International Conference on Humanoid Robots (Humanoids). New York: IEEE, 2024: 483-490.
[6] CESARELLI G, DONISI L, COCCIA A, et al. The e-textile for biomedical applications: a systematic review of literature[J]. Diagnostics, 2021, 11(12): 1-28.
doi: 10.3390/diagnostics11010001
[7] LUO Q S. Implementation method of intelligent emotion-aware clothing systembased on nanofibre technology[J]. Industria Textila, 2024, 75(1): 3-14.
doi: 10.35530/IT
[8] BATY F, CVETKOVIC D, BOESCH M, et al. Validation of a textile-based wearable measuring electrocardiogram and breathing frequency for sleep apnea monitoring[J]. Sensors, 2024, 24(19): 1-10.
doi: 10.3390/s24010001
[9] KWON K, LEE Y J, CHUNG S, et al. Full body-worn textile-integrated nanomaterials and soft electronics for real-time continuous motion recognition using cloud computing[J]. ACS Applied Materials & Interfaces, 2025, 17(5): 7977-7988.
[10] LI J Q, HE X J, KE L, et al. Hierarchically nano-decorated poly(lactic acid) nanofibers for humidity-resistant respiratory healthcare and high-accuracy disease diagnosis[J]. ACS Applied Materials & Interfaces, 2024, 16(39): 52476-52486.
[11] WEN F, SUN Z D, HE T, et al. Machine learning glove using self-powered conductive superhydrophobic triboelectric textile for gesture recognition in VR/AR applications[J]. Advanced Science, 2020, 7(14): 2000261.
doi: 10.1002/advs.v7.14
[12] GAO Y Y, ZONG R S, FENG J Y, et al. Flexible self-powered and self-sensing shoes based on aeroelastic structure for application in human motion moni-toring[J]. Journal of Power Sources, 2025, 638: 236626.
doi: 10.1016/j.jpowsour.2025.236626
[13] WANG J, ZHANG R, WANG Y F, et al. Highly sensitive, breathable, and superhydrophobic dome structure nonwoven-based flexible pressure sensor utilizing machine learning for handwriting recog-nition[J]. International Journal of Biological Macromolecules, 2025, 300: 139838.
doi: 10.1016/j.ijbiomac.2025.139838
[14] GUO L, LU Z X, YAO L G. Human-machine interaction sensing technology based on hand gesture recognition: a review[J]. IEEE Transactions on Human-Machine Systems, 2021, 51(4): 300-309.
doi: 10.1109/THMS.2021.3086003
[15] CUI L L, HU C Y, WANG W, et al. An adhesive, stretchable, and freeze-resistant conductive hydrogel strain sensor for handwriting recognition and depth motion monitoring[J]. Journal of Colloid and Interface Science, 2025, 677: 273-281.
doi: 10.1016/j.jcis.2024.07.214 pmid: 39094488
[16] KAUR S, KULKARNI N. Recent trends and challenges in human computer interaction using automatic emotion recognition: a review[J]. International Journal of Biometrics, 2024, 16(1): 16-43.
doi: 10.1504/IJBM.2024.135160
[17] WANG D P, WANG M Y, ZHANG Z Q, et al. Wearable electronic glove and multilayer para-LSTM-CNN-based method for sign language recognition[J]. IEEE Internet of Things Journal, 2024, 11(24): 40787-40799.
doi: 10.1109/JIOT.2024.3454215
[18] JIN W Y, PEI J Y, CAO Y T, et al. Intelligent flexible pressure sensors with improved sensing range and sensitivity based on 3D-graphene patterning induced by UV laser[J]. ACS Applied Materials & Interfaces, 2024, 16(48): 66763-66772.
[19] SHAO Q Y, ZHANG Y L, LIU J, et al. Investigation of flexible graphene hybrid knitted sensor for joint motion recognition based on convolutional neural network fusion long short-term memory network[J]. Journal of Industrial Textiles, 2024, 54: 15280837231225827.
doi: 10.1177/15280837231225827
[20] LIU L, HU T, ZHAO X M, et al. Innovative smart gloves with Phalanges-based triboelectric sensors as a dexterous teaching interface for Embodied Artificial Intelligence[J]. Nano Energy, 2025, 133: 110491.
doi: 10.1016/j.nanoen.2024.110491
[21] KIM D, MIN J, KO S H. Recent developments and future directions of wearable skin biosignal sensors[J]. Advanced Sensor Research, 2024, 3(2): 2300118.
doi: 10.1002/adsr.v3.2
[22] ZENG X H, HU M L, HE P, et al. Highly conductive carbon-based e-textile for gesture recognition[J]. IEEE Electron Device Letters, 2023, 44(5): 825-828.
doi: 10.1109/LED.2023.3263170
[23] WANG N C, YAO Y, WU P G, et al. Soft polymer optical fiber sensors for intelligent recognition of elastomer deformations and wearable applications[J]. Sensors, 2024, 24(7): 1-11.
doi: 10.3390/s24010001
[24] WANG F L, WANG H, WU L, et al. Improved morse code recognition and real-time translation system based on a low-cost, tailorable flexible capacitive sensor[J]. ACS Applied Electronic Materials, 2025, 7(1): 388-399.
doi: 10.1021/acsaelm.4c01850
[25] NA W D, XU C, AN L, et al. Alkali ion-accelerated gelation of MXene-based conductive hydrogel for flexible sensing and machine learning-assisted recog-nition[J]. Gels, 2024, 10(11): 1-15.
doi: 10.3390/gels10010001
[26] WANG J, ZHANG R, WANG Y F, et al. Highly sensitive, breathable, and superhydrophobic dome structure nonwoven-based flexible pressure sensor utilizing machine learning for handwriting recogni-tion[J]. International Journal of Biological Macromolecules, 2025, 300: 139838.
doi: 10.1016/j.ijbiomac.2025.139838
[27] YANG J, WANG R, GUAN X, et al. AI-enabled emotion-aware robot: the fusion of smart clothing, edge clouds and robotics[J]. Future Generation Computer Systems, 2020, 102: 701-709.
doi: 10.1016/j.future.2019.09.029
[28] 吴学奎, 任立红, 丁永生, 等. 面向智能服装的多生理信息融合的情绪判别[J]. 计算机工程与应用, 2009, 45(33): 218-221, 235.
WU Xuekui, REN Lihong, DING Yongsheng, et al. Multi-physiology information fusion for emotion distinction in smart clothing[J]. Computer Engineering and Applications, 2009, 45(33): 218-221, 235.
doi: 10.3778/j.issn.1002-8331.2009.33.069
[29] LONG K X, SU C C, HU C X, et al. Stretchable, conductive Hydrogel-Based triboelectric nanogenerator integrated with deep learning algorithm for character recognition[J]. Chemical Engineering Journal, 2024, 499: 155919.
doi: 10.1016/j.cej.2024.155919
[30] ZHENG Z T, HUANG Z B, ZHANG N, et al. Stretch-tolerant interconnects derived from silanization-assisted capping layer lamination for smart skin-attachable electronics[J]. Materials Today Physics, 2024, 46: 101494.
doi: 10.1016/j.mtphys.2024.101494
[31] MA S Q, WANG X Y, LI P, et al. Optical micro/nano fibers enabled smart textiles for human-machine inter-face[J]. Advanced Fiber Materials, 2022, 4(5): 1108-1117.
doi: 10.1007/s42765-022-00163-6
[32] CHEN K X, ZHANG D L, YAO L N, et al. Deep learning for sensor-based human activity recognition: overview, challenges, and opportunities[J]. ACM Computing Surveys, 2021, 54(4): 1-40.
[34] LIU J Y, TAN X, JIA X H, et al. A gait phase recognition method for obstacle crossing based on multi-sensor fusion[J]. Sensors and Actuators A: Physical, 2024, 376: 115645.
doi: 10.1016/j.sna.2024.115645
[35] YANG F, ZHENG Q Q, CHEN L Q, et al. Research on human behavior intention perception method based on wearable sensors[J]. IEEE Access, 2024, 12: 70278-70288.
doi: 10.1109/ACCESS.2024.3401685
[36] YI S J, MEI Z Y, IVANOV K, et al. Gait-based identification using wearable multimodal sensing and attention neural networks[J]. Sensors and Actuators A: Physical, 2024, 374: 115478.
doi: 10.1016/j.sna.2024.115478
[37] HASAN S M, D'AURIA B G, PARVEZ MAHMUD M A, et al. AI-aided gait analysis with a wearable device featuring a hydrogel sensor[J]. Sensors, 2024, 24(22): 1-14.
doi: 10.3390/s24010001
[38] XIANG K Y, LIU M J, CHEN J, et al. AI-assisted insole sensing system for multifunctional plantar-healthcare applications[J]. ACS Applied Materials & Interfaces, 2024, 16(25): 32662-32678.
[39] ZOU H L, CHEN Z J, ZHANG C Y, et al. STFNet: enhanced and lightweight spatiotemporal fusion network for wearable human activity recognition[J]. IEEE Sensors Journal, 2024, 24(8): 13686-13698.
doi: 10.1109/JSEN.2024.3373444
[40] QIU J G, LI Y, LI H, et al. Wearable Sensor-based physical activity intensity recognition using deep learning feature engineering fusion[J]. Measurement, 2025, 241: 115663.
doi: 10.1016/j.measurement.2024.115663
[41] NUNES W A, REUSCH R S, LUZA L, et al. Deploying human activity recognition in embedded RISC-V processors: deploying human activity recognition in embedded RISC-V processors[J]. Design Automation for Embedded Systems, 2024, 28(3): 187-217.
doi: 10.1007/s10617-024-09288-w
[42] MEKRUKSAVANICH S, JITPATTANAKUL A, MEKRUKSAVANICH S, et al. Deep residual network with a CBAM mechanism for the recognition of symmetric and asymmetric human activity using wearable sensors[J]. Symmetry, 2024, 16(5): 1-26.
doi: 10.3390/sym16010001
[43] AISYAH NURMAULIA ENTIFAR S, AQILLA ELLENAHAYA ENTIFAR N, FITRIAN WIBOWO A, et al. Extremely-low electrical-hysteresis hydrogels for multifunctional wearable sensors and osmotic power generators[J]. Chemical Engineering Journal, 2025, 509: 160971.
doi: 10.1016/j.cej.2025.160971
[44] LIN Y K, SUN M Z, LI F M, et al. A triboelectric smart carpet with an optimized braided structure for cruise ship monitoring enabled by deep learning[J]. Materials Today Communications, 2024, 38: 108184.
doi: 10.1016/j.mtcomm.2024.108184
[45] COSTANZO I, SEN D, RHEIN L, et al. Respiratory monitoring: current state of the art and future roads[J]. IEEE Reviews in Biomedical Engineering, 2022, 15: 103-121.
doi: 10.1109/RBME.2020.3036330
[46] Dash P K. Electrocardiogram monitoring[J]. Indian Journal of Anaesthesia, 2002, 46(4): 251-260.
[47] NIU J Q, LIN S J, CHEN D, et al. A fully elastic wearable electrochemical sweat detection system of tree-bionic microfluidic structure for real-time moni-toring[J]. Small, 2024, 20(11): 2306769.
doi: 10.1002/smll.v20.11
[48] PALUMBO A, VIZZA P, CALABRESE B, et al. Biopotential signal monitoring systems in rehabilitation: a review[J]. Sensors, 2021, 21(21): 1-22.
doi: 10.3390/s21010001
[49] DING T Y, GAGLIANO L, JAHANI A, et al. Epileptic seizure forecasting with wearable-based nocturnal sleep features[J]. Epilepsia Open, 2024, 9(5): 1793-1805.
doi: 10.1002/epi4.v9.5
[50] CARBONARO N, LAURINO M, ARCARISI L, et al. Textile-based pressure sensing matrix for in-bed monitoring of subject sleeping posture and breathing activity[J]. Applied Sciences, 2021, 11(6): 1-14.
doi: 10.3390/app11010001
[51] 卢妍, 洪岩, 方剑. 智能背景下机器学习在柔性应变传感器中的应用研究进展[J]. 纺织学报, 2024, 45(5): 228-238.
LU Yan, HONG Yan, FANG Jian. Research progress on applications of machine learning in flexible strain sensors in context of material intelligence[J]. Journal of Textile Research, 2024, 45(5): 228-238.
[52] BHONGADE A, GUPTA R, PRATHOSH A P, et al. ResPara-net: respiration parameter estimation using wearable single inertial measurement unit sensor and deep learning[J]. IEEE Sensors Journal, 2024, 24(15): 24931-24944.
doi: 10.1109/JSEN.2024.3408464
[53] BATY F, CVETKOVIC D, BOESCH M, et al. Validation of a textile-based wearable measuring electrocardiogram and breathing frequency for sleep apnea monitoring[J]. Sensors, 2024, 24(19): 1-10.
doi: 10.3390/s24010001
[54] ZHAO Z L, YANG J X, LIU J H, et al. Application of additive manufacturing and deep learning in exercise state discrimination[J]. Sensors, 2025, 25(2): 1-20.
doi: 10.3390/s25010001
[55] CHENANI H, RAZAGHI Z, SAEIDI M, et al. A stretchable, adhesive, and wearable hydrogel-based patches based on a bilayer PVA composite for online monitoring of sweat by artificial intelligence-assisted smartphones[J]. Talanta, 2025, 287: 127640.
doi: 10.1016/j.talanta.2025.127640
[56] EJUPI A, MENON C, EJUPI A, et al. Detection of talking in respiratory signals: a feasibility study using machine learning and wearable textile-based sensors[J]. Sensors, 2018, 18(8): 1-12.
doi: 10.3390/s18010001
[57] CHEN X W, JIANG X, FANG J W, et al. DisPad: flexible on-body displacement of fabric sensors for robust joint-motion tracking[J]. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2023, 7(1): 1-27.
[58] 谢浩月, 唐虹, 顾琳燕, 等. 基于温湿度监测功能的智能消防内衣研究[J]. 针织工业, 2019(5): 58-62.
XIE Haoyue, TANG Hong, GU Linyan, et al. Study on smart fire-fighting underwear based on temperature and humidity monitoring function[J]. Knitting Industries, 2019(5): 58-62.
[59] KOUCHEHBAGHI N H, YOUSEFZADEH M, GHAREHAGHAJI A, et al. A machine learning-guided design and manufacturing of wearable nanofibrous acoustic energy harvesters[J]. Nano Research, 2024, 17(10): 9181-9192.
doi: 10.1007/s12274-024-6613-6
[60] KE J M, LIU F R, XU G F, et al. Data-driven strain sensor design based on a knowledge graph frame-work[J]. Sensors, 2024, 24(17): 1-16.
doi: 10.3390/s24010001
[61] LIU T, ZHANG M Y, LI Z H, et al. Machine learning-assisted wearable sensing systems for speech recognition and interaction[J]. Nature Communications, 2025, 16: 2363.
doi: 10.1038/s41467-025-57629-5
[62] ZHOU Y, GUO S, ZHOU Y, et al. Ionic composite nanofiber membrane-based ultra-sensitive and anti-interference flexible pressure sensors for intelligent sign language recognition[J]. Advanced Functional Materials, 2025, 35(29): 2425586.
doi: 10.1002/adfm.v35.29
[63] ABU JARAD N, PRASAD A, RAHMANI S, et al. Smart fabrics with integrated pathogen detection, repellency, and antimicrobial properties for healthcare applications[J]. Advanced Functional Materials, 2024, 34(41): 2403157.
doi: 10.1002/adfm.v34.41
[64] ELSHEAKH D N, FAHMY O M, FAROUK M, et al. An early breast cancer detection by using wearable flexible sensors and artificial intelligent[J]. IEEE Access, 2024, 12: 48511-48529.
doi: 10.1109/ACCESS.2024.3380453
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