Wireless sensor systems face key challenges, including reliance on batteries, complex circuitry, high fabrication costs, and limited durability in harsh or remote environments. These limitations hinder long-term, autonomous sensing, particularly for wearable, military, and environmental applications. To overcome these issues, we present a fully selfpowered, battery-free, and flexible wireless force sensing platform based on a 3D multi-nanomaterials printed triboelectric nanogenerator (TENG). Our system uses low-cost, multi-nanomaterials 3D printing to fabricate a mechanically robust TENG integrated with an antenna to form a passive RLC circuit for wireless signal transmission. By incorporating a diode-switch configuration, the high output impedance of the TENG is reduced, enabling direct, efficient resonance-based wireless transmission in the megahertz range. The system transmits force information through shifts in resonant frequency, offering stability against environmental variations. Additionally, we demonstrate wireless power transfer by rectifying the received signal to power an LED remotely. This compact, battery-free, active electronics-free platform provides a promising solution for sustainable, self-powered autonomous wireless sensing in inaccessible or resource-limited environments.

The profound effects of stress on health demand precise and objective measurement techniques, as traditional self-report questionnaires often fall short. Molecular biosensors, particularly those detecting cortisol-a key stress hormone-offer a compelling alternative. Yet, traditional cortisol assays, which rely on saliva, urine, or blood samples, are cumbersome, costly, and unsuitable for continuous monitoring. Our research introduces an innovative wearable sensing system that employs a molecularly imprinted polymer-radiofrequency (MIP-RF) mechanism for non-invasive, real-time cortisol detection in sweat. This advanced system is wireless, flexible, battery-free, reusable, and environmentally stable, optimized for long-term use with an inductance-capacitance transducer. This transducer accurately translates cortisol levels into resonant frequency shifts, demonstrating high sensitivity$(\sim 160\text{kHz}/\log[\mu \mathrm{M}])$within a physiological range of$0-1_{1}\mathrm{M}$. Enhanced with Near-Field Communication (NFC) for seamless, battery-free operation and a 3D-printed microfluidic channel for direct sweat collection, the device allows continuous cortisol monitoring throughout daily activities. Validation through circadian rhythm tracking-comparing morning and evening cortisol levels-confirms its efficacy. This breakthrough signifies a transformative advancement in on-demand, non-laboratory health monitoring, harnessing wearable technology for precise molecular stress biomarker detection.

In this study, we present the development and characterization of a self-powered, flexible, and multi-material 3D-printed Triboelectric Nanogenerator-Unit Cell Force (TENG-UCF) sensor designed for multimodal force sensing and energy harvesting. Traditional force sensors, limited by their reliance on single-modal electrical readouts and continuous external power sources, are insufficient for comprehensive sensory applications. Our TENG-UCF sensor overcomes these limitations by leveraging the unique properties of MXene/P ANI composites and SEBS tribo layers, enabling simultaneous force sensing and energy generation. This innovative sensor generates electricity through triboelectric charges produced during contact and separation cycles, with output voltage and capacitance directly correlating with applied force. The TENG-UCF achieves high sensitivity and stability, with a maximum output voltage of 700V and a power density of 816.6 mW/m2under optimal conditions. Extensive testing demonstrated the sensor's mechanical stability, and long-term operational viability without significant degradation. Our TENG-UCF sensor represents a significant advancement in sustainable, renewable, and multimodal force sensing technologies, with broad implications for the development of self-powered sensor systems.

This paper details the development and optimization of a novel NFC-based wearable pressure sensor designed for arterial pulse monitoring with a focus on energy efficiency and data transmission management. By integrating this sensor with various machine learning models for human activity recognition using arterial pulse signals, we evaluate their impact on optimizing the sampling rate. This optimization directly influences power consumption and data transmission rates. A comparative analysis reveals that the 1-dimensional Convolutional Neural Network (1D-CNN) maintains an accuracy of over 92% while allowing the sampling rate to be lowered from 100 Hz to 40 Hz. This reduction leads to a 35% decrease in power consumption and a 50% decrease in data transmission needs. In contrast, traditional models such as Random Forest (RF) and Support Vector Machine (SVM) require a minimum of 70 Hz to maintain comparable accuracy, achieving only a 19% reduction in power and a 30% reduction in data transmission rates. Our findings emphasize the critical role of model selection in optimizing the arterial pulse sampling rate, enhancing the energy efficiency and operational effectiveness of our wearable health monitoring device.

There is a need for fast, accurate, contactless, and continuous vital sign measurements to provide insights into overall health and to enhance medical care in hospitals and other healthcare facilities. Remote Photoplethysmotraphy (rPPG) is a contactless method that can enable quick and non-invasive monitoring of vital signs such as heart rate (HR) and respiration rate (RR). There have been a growing number of studies creating and testing different signal decomposition techniques with rPPG for frequency-based vital measurement of HR and RR; however, there are still numerous shortcomings. Methods including Dynamic Mode Decomposition (DMD) and Variational Mode Decomposition (VMD) are sparsely discussed with respect to rPPG, and have yet to be applied to rPPG for RR estimation. Additionally, there are no comprehensive analyses on these techniques for rPPG, including DMD, VMD, and other techniques such as Ensemble Empirical Mode Decomposition (EEMD) and Fast Fourier Transform (FFT) which are more established in the rPPG space. This study provides a comparative analysis on EEMD, VMD, DMD, and FFT for rPPG HR and RR estimation. DMD outperformed the other methods in HR estimation with an MAE and RMSE of 3.08 and 5.05 beats per minute (bpm), respectively. For RR estimation, VMD performed the best with an MAE and RMSE of 3.36 and 4.08 breaths per minute (bpm), respectively. These results highlight the importance of developing new rPPG methods with DMD and VMD for HR and RR estimation.

Traditional stress assessment methods, including questionnaires and laboratory assays, suffer from limitations such as non-quantitative nature, lack of standardization, and impracticality for continuous monitoring. To address these challenges, we have developed a wireless battery-free stress monitoring wearable system equipped with machine learning (ML) using Ag electrodes as epidermal sensors for galvanic skin response (GSR) signal monitoring. These ultrathin, stretchable electrodes are fabricated using cost-efficient screen-printing methods, ensuring low contact impedance, high signal-to-noise ratio, and excellent mechanical stability. For signal collection, we employed a battery-free wireless readout system. The system utilizes Near-Field Communication (NFC) technology for wireless power and data transmission, eliminating the need for bulky batteries and enhancing wearability and comfort. The collected GSR data is transmitted to a mobile phone for real-time processing and analysis. Machine learning models were employed to realize stress event classification, accurately distinguishing between stress and rest states based on the GSR signals. We conducted in-situ experiments to evaluate the sensor's capability to detect stressful events during a Stroop test. The results demonstrated the system's effectiveness in providing continuous, real-time stress monitoring in everyday settings. Our innovative wearable device represents a significant advancement in stress assessment technology, offering a practical and efficient solution for continuous health monitoring.