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Artificial intelligence enhances a new generation of intelligent flexible wearable systems

Danyang Qin1, Rongrong Bao2, and Caofeng Pan2

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 Corresponding author: Rongrong Bao, baorongrong@buaa.edu.cn

DOI: 10.1088/1674-4926/26070010CSTR: 32376.14.1674-4926.26070010

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Abstract: AI-integrated flexible wearable sensors combine smart materials, conformal device design, and data-driven algorithms to continuously capture and interpret physiological, motion, and environmental signals. Here, we summarize how representative machine learning and deep learning models are used for signal processing, feature extraction, multimodal fusion, and system optimization in next-generation wearable sensing platforms. We further discuss their applications in personalized healthcare, human–machine interaction, and sensory augmentation. Finally, we identify key challenges, including data quality, model generalization, interpretability, power-efficient deployment, and privacy and security, together with perspectives on the development of more reliable, scalable, and trustworthy intelligent sensing systems.

Keywords: artificial intelligence, flexible wearable sensors, smart materials, machine learning, personalized healthcare



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Fig. 1.  (Color online) Overview of the three pillars of wearable intelligent systems.

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Fig. 2.  (Color online) ML models in flexible wearable sensors. (a) Schematic diagram of the SVM model for binary classification. (b) Schematic diagram of the RF model. (c) Schematic diagram of the KNN model. (d) Architecture of the CNN model. (e) Architecture of the LSTM model. (f) Architecture of the Transformer model.

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Fig. 3.  (Color online) AI-empowered data processing and perception in flexible sensing. (a) Attention-based force decoupling neural network. Reproduced with permission from Ref. [49]. Copyright 2025, Wiley-VCH GmbH. (b) Flexible tactile sensor with slip and shear deformation perception capabilities. Reproduced with permission from Ref. [50]. Copyright 2024, Wiley-VCH GmbH. (c) High-accuracy decoupling and classification of slip and pressure using ML models. Reproduced with permission from Ref. [50]. Copyright 2024, Wiley-VCH GmbH. (d) Neuromorphic artificial tactile perception system based on SNN. Reproduced with permission from Ref. [51]. Copyright 2024, AAAS. (e) Dataset-efficient visuotactile sensing system empowered by the integration of an FEM model and neural networks. Reproduced with permission from Ref. [52]. Copyright 2025, AAAS.

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Fig. 4.  (Color online) AI-driven multimodal data fusion and decoupling. (a) Schematic of the flexible multiplex biochemical sensor patch. Reproduced with permission from Ref. [55]. Copyright 2023, AAAS. (b) CNN architecture for feature extraction and classification of sensor images. Reproduced with permission from Ref. [55]. Copyright 2023, AAAS. (c) Deep neural network-based physiological-molecular data fusion for stress state assessment. Reproduced with permission from Ref. [57]. Copyright 2026, Springer Nature. (d) Decoupling of flexible sensors based on PEDOT:PSS composites under pressure and thermal stimuli. Reproduced with permission from Ref. [59]. Copyright 2023, Wiley-VCH GmbH. (e) DNN hierarchical architecture for accurate pressure prediction. Reproduced with permission from Ref. [59]. Copyright 2023, Wiley-VCH GmbH.

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Fig. 5.  (Color online) AI-enabled performance augmentation and super-resolution for sparse sensor arrays. (a) AI-assisted computational super-resolution for tactile arrays. Reproduced with permission from Ref. [62]. Copyright 2025, AAAs. (b) Architecture of the spatiotemporal feature aggregation network. Reproduced with permission from Ref. [62]. Copyright 2025, AAAs. (c) Precise localization and recognition of large-area sensor arrays based on Kriging interpolation and KNN classification. Reproduced with permission from Ref. [63]. Copyright 2024, Springer Nature.

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Fig. 6.  (Color online) AI-empowered advanced physiological monitoring and smart healthcare systems. (a) Deep learning-enabled automated segmentation and cardiac function assessment from echocardiography. Reproduced with permission from Ref. [66]. Copyright 2023, Springer Nature. (b) Intelligent simulation of TCM pulse diagnosis and cardiovascular parameter extraction. Reproduced with permission from Ref. [68]. Copyright 2024, Wiley-VCH GmbH. (c) Flexible chronoepifluidic SERS patch for continuous sweat biomarker monitoring. Reproduced with permission from Ref. [69]. Copyright 2025, Springer Nature. (d) Autoencoder-based representation learning and signal reconstruction for complex physiological data. Reproduced with permission from Ref. [69]. Copyright 2025, Springer Nature. (e) ResCNN architecture. Reproduced with permission from Ref. [71]. Copyright 2025,AAAS.

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Fig. 7.  (Color online) AI-empowered wearable human motion monitoring and sports protection systems. (a) AI-integrated wearable "Eyelectronics" for ocular motion classification and strabismus diagnosis. Reproduced with permission from Ref. [74]. Copyright 2026, AAAS. (b) Wearable interactive network system for full-body motion tracking and real-time bidirectional haptic feedback. Reproduced with permission from Ref. [75]. Copyright 2025, Springer Nature. (c) ML-based intention detection and hysteresis control strategy for upper limb exoskeletons. Reproduced with permission from Ref. [76]. Copyright 2025, Springer Nature. (d) CNN-based lower limb motion posture recognition using plantar pressure data from smart insoles. Reproduced with permission from Ref. [77]. Copyright 2025, American Chemical Society. (e) Training loss and accuracy curves demonstrating rapid model convergence. Reproduced with permission from Ref. [77]. Copyright 2025, American Chemical Society. (f) Confusion matrix validating the high accuracy of 16-class human motion posture recognition. Reproduced with permission from Ref. [77]. Copyright 2025, American Chemical Society.

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Fig. 8.  (Color online) AI empowered wearable HMI systems. (a) Schematic illustration of a bidirectional soft HMI system integrating a skin-attachable sEMG sensor and a multimodal robotic hand. Reproduced with permission from Ref. [81]. Copyright 2025, AAAS. (b) Real-time demonstration of gesture mimicry and robotic arm control via the sEMG-based wearable interface. Reproduced with permission from Ref. [81]. Copyright 2025, AAAS. (c) CNN-based signal processing workflow of the SAAS for mapping voice commands to virtual and physical controls. Reproduced with permission from Ref. [82]. Copyright 2025, Springer Nature. (d) DL-based hand motion capture and multimodal HMI control system. Reproduced with permission from Ref. [85]. Copyright 2026, AAAS. (e) Optical photographs of the flexible, wearable triboelectric AI acoustic textile. Reproduced with permission from Ref. [87]. Copyright 2025, AAAS. (f) An AI-empowered acoustic textile for IoT control and cloud interaction. Reproduced with permission from Ref. [87]. Copyright 2025, AAAS.

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Fig. 9.  (Color online) AI-empowered smart wearables for sensory augmentation and environmental comprehension. (a) AI-driven wearable olfactory interface for latency-free odor enhancement in mixed reality environments. Reproduced with permission from Ref. [88]. Copyright 2024, Springer Nature. (b) A wearable speech reconstruction system framework with dual-channel decoding and LLM-enhanced generation. Reproduced with permission from Ref. [89]. Copyright 2026, Springer Nature. (c) Adaptive forward model-based architecture for haptic dynamics modeling and inverse optimization. Reproduced with permission from Ref. [93]. Copyright 2024, Springer Nature. (d) Digitally embroidered smart glove enabling cross-spatiotemporal tactile sharing and skill transfer. Reproduced with permission from Ref. [93]. Copyright 2024, Springer Nature. (e) Wearable ring for humidity detection. Reproduced with permission from Ref. [94]. Copyright 2025, Wiley-VCH GmbH. (f) Architecture of feature extraction and classification based on XGBoost. Reproduced with permission from Ref. [94]. Copyright 2025, Wiley-VCH GmbH.

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Table 1.   Summary and comparison of ML in wearable devices.

CategoryAlgorithmsAdvantagesDisadvantagesApplication
Traditional machine learningKNN
Simple to implement, effective for small datasetsSensitive to noise, feature scaling, sensor drift, and large datasetsPlantar-pressure activity recognition[41]
Traditional machine learningRandom
forest		Robust to nonlinear features, suitable for multichannel sensing,
interpretable feature importance		Dependence on handcrafted features, increased memory and latency with size growthBreathing-pattern classification[42]
Traditional machine learningSVMEffective for small samples, suitable for high-dimensional features, strong classification marginLimited scalability, sensitivity to distribution shiftsWrist gesture recognition[43]
Deep learningCNN
Automatic spatial feature extraction, suitable for sensor arrays and tactile maps, local-noise robustnessLarge labeled-data demand, sensitivity to sensor layout and re-wearing shiftsHand-gesture recognition[34]
Deep learningLSTMStrong temporal-dependency modeling, suitable for dynamic wearable signals, sequential-pattern learningHigh sequential-computation cost, sensitivity to window length and sampling rateGesture recognition[44]
Deep learningTransformerLong-range dependency modeling, cross-channel correlation learning, high training parallelismHigh data, memory, and computation requirements, overfitting risk on small datasetsHuman-activity recognition[45]
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    Received: 03 July 2026 Revised: 31 July 2026 Online: Accepted Manuscript: 14 October 2026Uncorrected proof: 16 October 2026

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      Danyang Qin, Rongrong Bao, Caofeng Pan. Artificial intelligence enhances a new generation of intelligent flexible wearable systems[J]. Journal of Semiconductors, 2026, In Press. doi: 10.1088/1674-4926/26070010 ****D Y Qin, R R Bao, and C F Pan, Artificial intelligence enhances a new generation of intelligent flexible wearable systems[J]. J. Semicond., 2026, 47(11): 111601 doi: 10.1088/1674-4926/26070010
      Citation:
      Danyang Qin, Rongrong Bao, Caofeng Pan. Artificial intelligence enhances a new generation of intelligent flexible wearable systems[J]. Journal of Semiconductors, 2026, In Press. doi: 10.1088/1674-4926/26070010 ****
      D Y Qin, R R Bao, and C F Pan, Artificial intelligence enhances a new generation of intelligent flexible wearable systems[J]. J. Semicond., 2026, 47(11): 111601 doi: 10.1088/1674-4926/26070010
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      Artificial intelligence enhances a new generation of intelligent flexible wearable systems

      DOI: 10.1088/1674-4926/26070010
      CSTR: 32376.14.1674-4926.26070010
      • 1.

        School of Physics, Beihang University, Beijing 100191, China

      • 2.

        Institute of Atomic Manufacturing, Beihang University, Beijing 100191, China

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      • Danyang Qin received her B.E. degree from Beijing University of Posts and Telecommunications in 2025. She is currently pursuing her M.S. degree at Beihang University. Her research focuses on flexible tactile sensors, machine learning and intelligent sensing systems
      • Rongrong Bao received her B.S. degree from Tianjin University in 2007 and Ph.D. degree from Technical Institute of Physics and Chemistry, Chinese Academy of Sciences (CAS) in 2012. She was a postdoc fellow in the same institute. She has been a Professor in the group of Prof. Caofeng Pan at Beihang University. Her main research interests focus on the fields of the production and characterization of organic−inorganic composite nanodevices and flexible pressure sensors
      • Corresponding author: baorongrong@buaa.edu.cn
      • Received Date: 2026-07-03
      • Revised Date: 2026-07-31
        • Available Online: 2026-10-14

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