The research offers a performance assessment and structural issues affecting reliability, including sensor diversity, dominance, noise, and intermittent data loss due to motion artifacts and dropouts, and explores multimodal fusion failure modes, demonstrating how asynchronous failure and partial observability can cause instability in multimodal representations.
Abstract
Multimodal sensing and data fusion using artificial intelligence have transformed wearable health technologies by integrating various physiological signals for continuous health monitoring. This study provides a comprehensive perspective on wearable technologies, such as fusion hierarchies, machine learning interpretation, and deployment in wearable body area networks. The research offers a performance assessment and structural issues affecting reliability, including sensor diversity, dominance, noise, and intermittent data loss due to motion artifacts and dropouts. As a result, it explores multimodal fusion failure modes, demonstrating how asynchronous failure and partial observability can cause instability in multimodal representations. The study also highlights the transition from continuous to event- and window-based processing to improve energy, computational, and clinical efficiency in edge computing. The study explores emerging approaches like self-supervised learning, multimodal foundation models, and digital twin-based personalized health models for enhancing robustness and generalization. The research also considers advances in functional materials, such as mechanochromic materials and biointegrated sensing platforms, to enable intuitive and seamless physiological monitoring. Finally, the work considers the regulatory, energy, and system constraints on these innovations and notes that future wearable systems must combine computational intelligence with physical form factors, interpretability, and safety, with robustness and adaptability being the guiding design principles.
Received: 20 March 2026 | Revised: 22 May 2026 | Accepted: 1 July 2026
Conflicts of Interest
The authors declare that they have no conflicts of interest to this work.
Data Availability Statement
Data sharing is not applicable to this article as no new data were created or analyzed in this study.
Author Contribution Statement
Najeem Olawale Adelakun: Conceptualization, Methodology, Resources, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Matthew Babatunde Olajide: Methodology, Validation, Formal analysis, Data curation, Writing – review & editing, Supervision, Project administration. Samuel Adeniyi Omolola: Formal analysis, Investigation, Resources, Data curation, Project administration.
Textile-based sensors have emerged as key components for wearable healthcare, intelligent sports analytics, and industrial monitoring, yet their practical deployment remains constrained by electromagnetic interference, motion artifacts, nonlinear multimodal coupling, and limited computational efficiency in dynamic environments. This study proposes a hybrid AI framework integrating CNN– Transformer architectures to address these challenges through adaptive wavelet denoising, GAN-based data augmentation, and attention-guided multimodal feature fusion for heterogeneous sensing signals. A lightweight deployment strategy combining model pruning and quantization reduces computational overhead by 70%, enabling real-time inference with latency below 50 ms on edge devices. Experimental validation demonstrates that the proposed framework achieves 95.3% motion recognition accuracy using an 8 × 8 pressure sensor array, attains an AUC of 0.95 for respiratory anomaly detection under low signal-to-noise conditions, and predicts fabric remaining useful life with an RMSE of 6.2 hours through physics-informed learning. By improving robustness against environmental disturbances and enhancing multimodal information extraction, the proposed method provides an effective computational solution for intelligent textile sensing systems and offers methodological support for electromagnetic interference mitigation, wearable sensing networks, and advanced smart sensing platforms involving wireless signal acquisition and integrated electromagnetic environments.
The development of digital healthcare has raised demands for proactive, long‐term, and high‐precision health monitoring, which greatly promotes the innovation of intelligent wearable medical devices. Wearable sensor patches feature superior flexibility, lightweight structure, and skin conformability, enabling continuous in vivo physiological signal acquisition and showing enormous potential in personalized health monitoring and precise disease intervention. However, several critical technical bottlenecks severely restrict their clinical translation, including unsatisfactory biocompatibility, inefficient multimodal signal fusion, unstable sustainable energy supply, and vulnerable data security and privacy protection. Focusing on the latest advances in wearable sensor patches oriented to health monitoring and disease diagnosis and treatment, this review systematically elaborates the key enabling technologies of wearable patches, covering functional materials, miniaturization and fabrication, self‐powered systems, and multimodal sensing architectures. Furthermore, we summarize the practical applications of wearable patches in multimodal physiological signal monitoring, chronic disease management, neuropsychiatric disorder evaluation, and rehabilitation medicine. This review also discusses the role of artificial intelligence and digital integration technology in intelligent data analysis and personalized health modeling. Importantly, we highlight current challenges and future research directions, aiming to provide insights and reliable references for the further optimization and clinical implementation of wearable sensor patches in next‐generation intelligent healthcare systems.
- Human Activity Recognition (HAR) is a rapidly advancing research domain with transformative applications across healthcare, smart homes, rehabilitation, elderly monitoring, sports analytics, and context-aware computing. The performance and deployability of HAR systems are fundamentally determined by the sensor modalities through which human motion and behavioral data are captured. This systematic review provides a comprehensive examination of the full spectrum of sensor modalities employed in HAR research spanning wearable inertial sensors, physiological sensors, vision-based sensors, depth cameras, ambient sensing systems, and emerging multimodal fusion frameworks tracing, technical characteristics, application domains, and comparative strengths and limitations. The review further examines data acquisition protocols, benchmark datasets, and the persistent challenges of inter-subject variability, sensor placement sensitivity, privacy constraints, and computational efficiency that continue to shape the field. Emerging directions including federated sensing, edge-optimized data acquisition, self-supervised learning from unlabeled sensor streams, and privacy-preserving modalities are critically evaluated as promising pathways toward robust, scalable, and ethically responsible HAR deployment. By synthesizing evidence across more than a decade of HAR sensor research, this review provides a structured reference for researchers and practitioners designing next-generation activity recognition systems, and identifies the most consequential open challenges and future directions for the field.
O. A. Ayegbusi, M. Onyesolu· Iconic research and engineer...· 0 citations
Population aging has increased the need for continuous, non-invasive, and context-aware monitoring systems capable of supporting autonomy, safety, and early intervention in daily living environments. Multimodal biometric sensing offers an important technical basis for this purpose, as it combines physiological, motion-related, and environmental signals to provide a more complete view of older adults’ functional and health-related conditions. However, many existing solutions remain fragmented, device-dependent, and insufficiently connected to core instrumentation requirements, including signal quality, sensor calibration, temporal synchronization, latency, energy consumption, interoperability, reliability, and data privacy. This article proposes an Edge-AI instrumentation framework for multimodal biometric sensing in active aging environments, supported by a structured analysis of recent literature on wearable, ambient, and context-aware sensing systems. The framework integrates wearable, ambient, and context-aware sensors with local processing capabilities to support signal acquisition, preprocessing, quality control, feature extraction, anomaly detection, and decision support close to the data source. By placing Edge AI within the instrumentation pipeline, the proposed framework identifies design requirements that may help reduce response time, limit unnecessary transmission of sensitive biometric data, and improve feasibility in home-based and assisted-living contexts. These expected benefits, however, require empirical testing through future prototype implementation and real-world evaluation. The article also defines a validation-oriented perspective for sensor-based active aging systems, covering technical, operational, and human-centered dimensions such as measurement accuracy, signal robustness, usability, privacy preservation, interoperability, reproducibility, energy efficiency, and system scalability. The proposed framework is intended to support the design, comparison, and validation of more reliable, interpretable, and reproducible sensor-based monitoring systems, while offering a structured basis for prototype development and future real-world evaluation in active aging environments.
Teresa Guarda, Washington Torres-Guin, J. Coronado-Hernández et al.· Italian National Conference...· 0 citations
This study reviews pre-2018 developments and proposes an improved HAR framework incorporating advanced feature engineering, sensor fusion, and ensemble learning techniques, which shows strong potential in healthcare, fitness, and smart environments.
Silvia Diallo, F. Z. Idrissi· International Journal of Mod...· 0 citations
Clinical translation remains limited by data security and privacy risks, insufficient standardization and regulatory alignment, long-term stability and biocompatibility concerns, and uneven validation maturity across technologies.
Jirui Wen, Jiang Wu, Yi Yang et al.· Chinese Medical Journal· 0 citations
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