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1. All-printed chip-less wearable neuromorphic system for multimodal physicochemical health monitoring

   https://doi.org/10.1038/s41467-025-60854-7  

   Choi, Yongsuk; Jin, Peng; Lee, Sanghyun; Song, Yu; Tay, Roland Yingjie; Kim, Gwangmook; Yoo, Jounghyun; Han, Hong; Yeom, Jeonghee; Cho, Jeong Ho; et al (December 2025, Nature Communications)

   Abstract Recent advancements in wearable sensor technologies have enabled real-time monitoring of physiological and biochemical signals, opening new opportunities for personalized healthcare applications. However, conventional wearable devices often depend on rigid electronics components for signal transduction, processing, and wireless communications, leading to compromised signal quality due to the mechanical mismatches with the soft, flexible nature of human skin. Additionally, current computing technologies face substantial challenges in efficiently processing these vast datasets, with limitations in scalability, high power consumption, and a heavy reliance on external internet resources, which also poses security risks. To address these challenges, we have developed a miniaturized, standalone, chip-less wearable neuromorphic system capable of simultaneously monitoring, processing, and analyzing multimodal physicochemical biomarker data (i.e., metabolites, cardiac activities, and core body temperature). By leveraging scalable printing technology, we fabricated artificial synapses that function as both sensors and analog processing units, integrating them alongside printed synaptic nodes into a compact wearable system embedded with a medical diagnostic algorithm for multimodal data processing and decision making. The feasibility of this flexible wearable neuromorphic system was demonstrated in sepsis diagnosis and patient data classification, highlighting the potential of this wearable technology for real-time medical diagnostics. 

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2. A Distributed Wearable Computing Framework for Human Activity Classification

   https://doi.org/10.3390/electronics14163203  

   Rivas-Caicedo, Jhonathan L; Niño-Tejada, Kevin; Saldaña-Aristizabal, Laura; Patarroyo-Montenegro, Juan F (August 2025, Electronics)

   Human Activity Recognition (HAR) using wearable sensors plays a critical role in applications such as healthcare, sports monitoring, and rehabilitation. Traditional approaches typically rely on centralized models that aggregate and process data from multiple sensors simultaneously. However, such architecture often suffers from high latency, increased communication overhead, limited scalability, and reduced robustness, particularly in dynamic environments where wearable systems operate under resource constraints. This paper proposes a distributed neural network framework for HAR, where each wearable sensor independently processes its data using a lightweight neural model and transmits high-level features or predictions to a central neural network for final classification. This strategy alleviates the computational load on the central node, reduces data transmission across the network, and enhances user privacy. We evaluated the proposed distributed framework using our publicly available multi-sensor HAR dataset and compared its performance against a centralized neural network trained on the same data. The results demonstrate that the distributed approach achieves comparable or superior classification accuracy while significantly lowering inference latency and energy consumption. These findings underscore the promise of distributed intelligence in wearable systems for real-time and energy-efficient human activity monitoring. 

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3. Next-Generation Potentiometric Sensors: A Review of Flexible and Wearable Technologies

   https://doi.org/10.3390/bios15010051  

   Fathy, Mahmoud Abdelwahab; Bühlmann, Philippe (January 2025, Biosensors)

   In recent years, the field of wearable sensors has undergone significant evolution, emerging as a pivotal topic of research due to the capacity of such sensors to gather physiological data during various human activities. Transitioning from basic fitness trackers, these sensors are continuously being improved, with the ultimate objective to make compact, sophisticated, highly integrated, and adaptable multi-functional devices that seamlessly connect to clothing or the body, and continuously monitor bodily signals without impeding the wearer’s comfort or well-being. Potentiometric sensors, leveraging a range of different solid contact materials, have emerged as a preferred choice for wearable chemical or biological sensors. Nanomaterials play a pivotal role, offering unique properties, such as high conductivity and surface-to-volume ratios. This article provides a review of recent advancements in wearable potentiometric sensors utilizing various solid contacts, with a particular emphasis on nanomaterials. These sensors are employed for precise ion concentration determinations, notably sodium, potassium, calcium, magnesium, ammonium, and chloride, in human biological fluids. This review highlights two primary applications, that is, (1) the enhancement of athletic performance by continuous monitoring of ion levels in sweat to gauge the athlete’s health status, and (2) the facilitation of clinical diagnosis and preventive healthcare by monitoring the health status of patients, in particular to detect early signs of dehydration, fatigue, and muscle spasms. 

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4. mRI: Multi-modal 3D Human Pose Estimation Dataset using mmWave, RGB-D, and Inertial Sensors

   An, Sizhe An; Li, Yin; Ogras, Umit (November 2022, Advances in Neural Information Processing Systems 35 (NeurIPS 2022))

   The ability to estimate 3D human body pose and movement, also known as human pose estimation (HPE), enables many applications for home-based health monitoring, such as remote rehabilitation training. Several possible solutions have emerged using sensors ranging from RGB cameras, depth sensors, millimeter-Wave (mmWave) radars, and wearable inertial sensors. Despite previous efforts on datasets and benchmarks for HPE, few dataset exploits multiple modalities and focuses on home-based health monitoring. To bridge this gap, we present mRI1, a multi-modal 3D human pose estimation dataset with mmWave, RGB-D, and Inertial Sensors. Our dataset consists of over 160k synchronized frames from 20 subjects performing rehabilitation exercises and supports the benchmarks of HPE and action detection. We perform extensive experiments using our dataset and delineate the strength of each modality. We hope that the release of mRI can catalyze the research in pose estimation, multi-modal learning, and action understanding, and more importantly facilitate the applications of home-based health monitoring. 

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5. The 3D Printing of Nanocomposites for Wearable Biosensors: Recent Advances, Challenges, and Prospects

   https://doi.org/10.3390/bioengineering11010032  

   Parupelli, Santosh Kumar; Desai, Salil (January 2024, Bioengineering)

   Notably, 3D-printed flexible and wearable biosensors have immense potential to interact with the human body noninvasively for the real-time and continuous health monitoring of physiological parameters. This paper comprehensively reviews the progress in 3D-printed wearable biosensors. The review also explores the incorporation of nanocomposites in 3D printing for biosensors. A detailed analysis of various 3D printing processes for fabricating wearable biosensors is reported. Besides this, recent advances in various 3D-printed wearable biosensors platforms such as sweat sensors, glucose sensors, electrocardiography sensors, electroencephalography sensors, tactile sensors, wearable oximeters, tattoo sensors, and respiratory sensors are discussed. Furthermore, the challenges and prospects associated with 3D-printed wearable biosensors are presented. This review is an invaluable resource for engineers, researchers, and healthcare clinicians, providing insights into the advancements and capabilities of 3D printing in the wearable biosensor domain. 

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