Signal Monitoring Research Articles

Stiff gel-protected fiber-shaped supercapacitors based on CNFA/silk composite fiber with superhigh interference-resistant ability as self-powered temperature sensor

The development of self-powered sensors with interference-resistant detection is a priority area of research for the next generation of wearable electronic devices. Nevertheless, the presence of multiple stimuli in the actual environment will result in crosstalk with the sensor, thereby hindering the ability to obtain an accurate response to a singular stimulus. Here, we present a self-powered sensor composed of silk-based conductive composite fibers (CNFA@ESF), which is capable of energy storage and sensing. The fabricated CNFA@ESF exhibits excellent mechanical performance, as well as flexibility that can withstand various deformations. The CNFA@ESF provides a good areal capacitance (44.44 mF cm−2), high-rate capability, and excellent cycle stability (91 % for 5000 cycles). In addition, CNFA@ESF also shows good sensing performance for multiple signals including strain, temperature, and humidity. It was observed that the assembly of the symmetrical device with a stiff hydrogel surface layer for protection enabled the real-time, interference-free monitoring of temperature signals. Also, the CNFA@ESF can be woven into fabrics and integrated with a solar cell to form a self-powered sensor system, which has been proven to convert and store solar energy to power electronic watches, indicating its huge potential for future wearable electronics.

Intensity Analysis Method of Acoustic Emission Signals for the Damage Monitoring of Post-Tensioned Concrete Beams.

Acoustic emission (AE) is well suited for the real-time monitoring and detection of damage in reinforced concrete structures. In this study, loading/unloading cycles up to failure were applied on three different full-scale beams, each with varying defect morphologies. An intensity analysis method was employed to assess the damage sensitivities of the defective structures under stress conditions. Specifically, the calm ratio, load ratio, severity, and historical index were identified as statistical parameters that can provide global information on the damage level. Consequently, they can be easily used as damage evolution indexes for reinforced concrete structures. Correlations between these parameters were investigated to better discriminate between their potentials and identify critical levels that might not be evident using parametric analysis. The AEI chart helps locate damaging areas, aiding focused repairs. For defected beams with broken strands, at low load, HI and SI fall in zone B (damage detected). At cycles 4 and 6, with significant deflection, they fall into a critical zone, E (severe damage). Comparing post-tensioned beams revealed defects correlating with damage susceptibility. B3 beams with diffused defects displayed high activity at higher loads. Applying a load-calm ratio chart, initial minor damage worsened progressively. Severe damage was prominent in defective B2 and B3 beams, reaching zone 3. The variation in the acquired parameters over time can then be considered as an affordable and reliable indicator of damage progression.

(Invited) Low-Frequency Noise in Flexible Carbon-Nanotube Analog Circuits

Since flexible devices can come into close contact with the human body, they are suitable for wearable sensor applications for long-term and non-invasive monitoring of biological and physiological signals in various fields such as medical/health care and sports, among others. In order to achieve a fully flexible sensor system, various electronic circuits need to be integrated on a flexible film, including the analog frontend (AFE) and radio-frequency (RF) frontend, as well as the sensors. Recently, we have demonstrated low-voltage CNT-based analog/digital mixed-signal circuits with excellent stability and robustness against variability, including the AFE. One remaining issue with CNT-based analog circuits is low-frequency noise, which directly impacts the sensitivity of the sensor system within the frequency range of physiological signals such as ECG and EEG.Here, we study the low-frequency noise of flexible carbon nanotube thin-film transistors and their influence on analog circuits. The low-frequency noise exhibits a typical 1/f spectrum. Inserting an interface layer between the CNT channel layer and gate insulator layer reduces Hooge's parameter of the 1/f noise. The results indicate that maintaining distance from the channel to traps in the gate oxide is important for reducing low-frequency noise.

(Invited) Battery-Free Wearable and Implantable Electronics for Chronic Disease Management

60% of Americans live with at least one chronic disease. These diseases and their associated comorbidities are now the leading causes of death in the United States. The effective management of complex chronic diseases requires body-wide, long-term, accurate, and continuous monitoring of multiple physiological signals from wearable and implantable devices to precisely determine the pathological state. Wearable physiological signal monitoring can dramatically reduce the demand for physician visits and increase patients' engagement and treatment adherence rates. Specifically, battery-free wearables and implantable electronics reduce device volume and mechanical stiffness, significantly improving wear comfort, which is highly desirable for next-generation wearable and implantable electronics. However, battery-free wearables and implantable electronics still face many challenges, mainly wireless energy and data transfer. To address these challenges, my research has been involved in the exploration of rational system design concepts, material and device fabrication innovation, and tailored algorithms to enable smart battery-free wearables and implantable electronics targeting next-generation chronic disease management.Here, I would like to discuss two of my developed technology platforms to elaborate on the concept of battery-free wearable and implantable systems. First, I will describe an RFID-based body area sensor network technology platform. This technology uses electromagnetic waves and RF antennas as energy and data transmission media and has broad applications in sleep tracking, workout monitoring, chronic wound healing, and inflammation management. Second, I will describe a triboelectric transducer-based implantable battery-free device. This technology platform uses ultrasound waves and triboelectric transducers as energy and data transmission media and has broad applications in implantable sensing. Overall, the developed technology platforms can assess multiple health outcomes and treatment responses to various chronic diseases. Ultimately, this technology will help reduce the burden of chronic diseases, lower medical costs, and provide a better quality of life for patients. (1,2,3,4)

(Invited) Non-Invasive Injectable Nanotube-Hydrogel Formulations for Local Analyte Detection

Rapid, minimally-invasive in situ detection of analytes in vivo remains a goal to study inflammatory signaling molecules, disease biomarkers, and pharmacology of therapeutics. To achieve this, we are using the near-infrared fluorescent properties of single-walled carbon nanotubes (SWCNT) to design sensors for use in live mice. SWCNT fluorescence is responsive to the local environment, in the tissue-transparent window, and does not photobleach, allowing for long-term signal monitoring. In this work, we are exploring several formulations of SWCNT embedded within hydrogels which gel in situ. We have investigated the kinetics of penetration of several classes of analytes—proteins, small molecules, and ions—through the hydrogel and their interaction with SWCNT sensors in vitro. Then, we explored the long-term stability of these nanotube-hydrogel formulations in live mice prior to detection of the chemotherapeutic doxorubicin. In ongoing work, we are exploring disease biomarker-specific sensing and monitoring of inflammatory cytokines in mouse models of disease using this nanotube-hydrogel system. We anticipate long-term applications of this work may include minimally-invasive and rapid clinical diagnostic development.

Customized Flexible Osteoid Electrode for Traumatic Brain Injury Induced Posttraumatic Epilepsy Monitoring

AbstractTraumatic brain injury (TBI)‐induced post‐traumatic epilepsy (PTE) is a serious brain disease that causes the loss of motor functions, cognitive impairments, and even death. Real‐time and long‐term electrophysiological signal monitoring has significant implication for the treatment of patient with TBI. Brain–computer interfaces (BCIs) utilizing flexible electrodes enable accurate epilepsy identification by electrocorticography (ECoG) signal monitoring. In this study, a customized flexible osteoid electrode is developed, which contributes to demands and personal differentiation among patients with TBI. The end product of osteoid electrode after full osseointegration will be a stable and firmly integrated structure within the bone tissue, providing a secure platform for continuous electrophysiological signal monitoring. The flexibility and conductivity of the osteoid electrode enables high‐quality and long‐term epilepsy monitoring. Biocompatibility of the osteoid electrode is also demonstrated by in vitro cell experiments. Simultaneously, the porous osteoid electrode also exhibits osseointegration ability based on potential long‐term ECoG signal monitoring, which suggests its potential for the comprehensive treatment of TBI patients. This work not only introduces a potential therapeutic scaffold for TBI‐related diseases, but also provides promising techniques for clinical ECoG monitoring in patients with TBI.

Photodiode-based porosity prediction in laser powder bed fusion considering inter-hatch and inter-layer effects

Laser powder bed fusion, while promising, faces hurdles in certifying fabricated parts due to cost and complexity, with in-process monitoring emerging as a potential solution. Existing models focus on predicting defects at a given location using the monitoring signals from solely that same location. Hence, these models treat each track or layer independently of the previous and subsequent ones, neglecting potential interdependencies. This study proposed an in-situ, photodiode-based monitoring approach considering inter-hatch and inter-layer effects on porosity formation - factors often overlooked in existing research. Two Ti-6Al-4 V cuboids (10×10×5 mm3) were built with optimized process parameters, with the melt pool continuously monitored at 20 kHz via a co-axially mounted photodiode. The monitoring system captured the integral radiation in the near-infrared spectrum within a field of view centered on the melt pool. The porosity is assessed by X-ray computed tomography (X-CT), serving as ground truth to build supervised machine learning (ML) models. This study considered physical phenomena occurring during the printing process, including remelting of lack of fusion pores by the subsequent layer, keyholes penetrating the current layer hence introducing pores in the layer below, and overlap between adjacent scan tracks. These considerations are critical for a holistic understanding of pore formation mechanisms. Photodiode signals and computed tomography volumes were cropped using windows of four sizes to test the model's pore localization capability. A machine learning model, specifically a Convolutional Neural Network (CNN) - Long Short-Term Memory (LSTM) network, was trained to predict porosities using these window sequences. The CNN extracted spatial features from photodiode signals, addressing inter-hatch effects, while the LSTM captured temporal dependencies across layers, addressing inter-layer effects. The results, with the Area Under the Receiver Operating Characteristic curve (AUC) of 0.91 for pores exceeding 8000 μm3 in volume and 100 μm2 in cross-sectional area, demonstrate the feasibility of the proposed model in detecting pores-level defects. This high defect prediction and positioning accuracy are essential for process control, providing real-time status of the region of interest and informing the controller of pore positions, thus facilitating intra-layer or inter-layer correction.

Highly-sensitive ultrathin flexible pressure sensor based on quasi-2D conductive network for human physiological signal monitoring

Ultrathin flexible pressure sensors are highly desirable for human physiological signal monitoring due to their portability and ability to conform to the body. However, existing ultrathin pressure sensors face challenges in balancing the sensitivity and their range. A spatial-dimensionally constrained strategy was proposed to construct an ultrathin active layer. In detail, multi-walled carbon nanotubes (MWCNTs) with an average length of ∼1.3 μm were intentionally spin-coated into a thin thermoplastic polyurethane (TPU) film with a thickness of 1.5 μm. By doing so, a quasi-2D MWCNTs conductive network is constructed, which enabled the sensor to have ultrahigh sensitivity of 27.04 kPa−1, a wide liner detection range of 0.2–24 kPa, ultrafast response time of 40 ms and a recovery time of 10 ms, and high durability in 1500 cycles under 0.2 kPa with loading/unloading frequency of 2 Hz. Thanks to this superior performance, the ultrathin pressure sensor can be used to monitor human movement and physiological signals, including finger touch, finger bending, wrist bending, and deep breathing. Moreover, the sensor can be attached to the side wall of the tip of the renal ureteroscope, allowing for the monitoring of renal pelvic pressure. Therefore, this spatial-dimensionally constrained strategy will open up a new way to fabricate highly sensitive ultrathin pressure sensors and have great application prospects in health monitoring.

Different mechanisms of contextual inference govern associatively learned and sensory-evoked postural responses

Human standing balance relies on the continuous monitoring and integration of sensory signals to infer our body's motion and orientation within the environment. However, when sensory information is no longer contextually relevant to balancing the body (e.g., when sensory and motor signals are incongruent), sensory-evoked balance responses are rapidly suppressed, much earlier than any conscious perception of changes in balance control. Here, we used a robotic balance simulator to assess whether associatively learned postural responses are similarly modulated by sensorimotor incongruence and contextual relevance to postural control. Twenty-nine participants in three groups were classically conditioned to generate postural responses to whole-body perturbations when presented with an initially neutral sound cue. During catch and extinction trials, participants received only the auditory stimulus but in different sensorimotor states corresponding to their group: 1) during normal active balance, 2) while immobilized, and 3) throughout periods where the computer subtly removed active control over balance. In the balancing and immobilized states, conditioned responses were either evoked or suppressed, respectively, according to the (in)ability to control movement. Following the immobilized state, conditioned responses were renewed when balance was restored, indicating that conditioning was retained but only expressed when contextually relevant. In contrast, conditioned responses persisted in the computer-controlled state even though there was no causal relationship between motor and sensory signals. These findings suggest that mechanisms responsible for sensory-evoked and conditioned postural responses do not share a single, central contextual inference and assessment of their relevance to postural control, and may instead operate in parallel.

Abstract We093: Comparative Analysis of Theoretical Models and Fusion Frameworks for Early Cardiac Dysfunction Detection Using Multimodal Wearable Data

Background: Wearable devices have emerged as promising tools for continuous monitoring of physiological signals, offering opportunities for early detection of cardiac dysfunction. This study aims to compare various theoretical models and fusion frameworks to assess their effectiveness in early detection of cardiac dysfunction. Hypothesis: The study investigates the performance of signal processing-based, machine learning-based, and hybrid models for early cardiac dysfunction detection using multimodal wearable data. Additionally, it explores the efficacy of feature-level fusion, decision-level fusion, and sensor-level fusion frameworks in integrating information from diverse sensors for enhanced detection accuracy.The primary goal of the study is to identify the most effective theoretical models and fusion frameworks for early detection of cardiac dysfunction using multimodal wearable data. By comparing different approaches, the study aims to provide insights into the strengths and limitations of each method and contribute to the development of reliable wearable-based systems for proactive healthcare interventions. Methods/Approach: Three theoretical models—signal processing-based, machine learning-based, and hybrid—were implemented and evaluated for their performance in detecting early signs of cardiac dysfunction. Additionally, three fusion frameworks—feature-level fusion, decision-level fusion, and sensor-level fusion—were investigated to assess their effectiveness in integrating multimodal wearable data. Results/Data: The signal processing-based model achieved a sensitivity of 85%, specificity of 82%, and accuracy of 83% in detecting early signs of cardiac dysfunction. The machine learning-based model outperformed with a sensitivity of 92%, specificity of 88%, and accuracy of 90% . The hybrid model yielded intermediate results with a sensitivity of 89%, specificity of 85%, and accuracy of 87% . Among the fusion frameworks, sensor-level fusion demonstrated superior performance with a sensitivity of 94%, specificity of 90%, and accuracy of 92%. Conclusion: Machine learning-based approaches showed higher efficacy in early cardiac dysfunction detection compared to signal processing-based methods. Sensor-level fusion exhibited the best performance among data fusion frameworks, emphasising the importance of integrating information from diverse wearable sensors for improved accuracy.

Dynamic analysis of planetary gear transmission based on Lagrange interpolation polynomials

Abstract This paper proposes a novel dynamic model considering manufacturing errors and eccentricity errors to analyze the dynamics of planetary gear transmission (PGT). The dynamic model is established based on the fractional-order calculus (FOC) and solved by an enhanced fourth-order Lagrange interpolation polynomials (LIP) method. Three numerical examples and the vibration experiments of planetary gear transmission are employed for verification. The comparison results indicate that the proposed solution method has higher solution accuracy and efficient than the existing algorithms in solving fractional equations, and the relative errors of the proposed solution method in three examples are 0.32%, 0.78% and 0.16%, respectively. The proposed dynamic model of PGT has better agreement with the experimentally measured signal compared with the integer-order dynamic model, and the maximum error and average error of the characteristic frequency amplitude between the proposed dynamic model and the measured signal are 4.76% and 3.57%, respectively. The proposed method contributes to the theoretical foundation for the signal monitoring of PGT.

Understanding coaxial photodiode-based multispectral pyrometer measurements at the overhang regions in laser powder bed fusion for part qualification

Coaxial photodiode monitoring sensors provide a digital signature at the melt pool level in laser powder bed fusion (L-PBF), facilitating faster part qualification. However, current signatures are plagued by significant noise and signal variation and show counterintuitive trends such as a decrease in measured temperature near overhang surfaces. This paper investigates the behavior of coaxial photodiode-based melt pool monitoring (PD-MPM) thermal measurements at overhang regions by conducting a combination of experiments and multiphysics simulations. High-speed in situ synchrotron X-ray imaging is coupled with a coaxial photodiode system to enable comparison of the observed melt pool phenomena and monitoring signals during double-track AlSi10Mg experiments. A multiphysics model is developed to simulate the melt pool dynamics and concurrent sensor signals throughout the process. We propose a surrogate model that clarifies the correlation between sensor signals and melt pool temperature. Both experimental and simulation results emphasize the significant impacts of laser energy and keyhole formation on solid-liquid interface discontinuities and PD-MPM signals. Results reveal that a boiling region with a relatively smaller projected area, as near an overhang, can lead to a decrease in the measured melt pool temperature, even when the true peak temperature remains constant, meaning that PD-MPM temperature measurements cannot be used to estimate absolute melt pool temperature. Consequently, in overhang regions, interaction between the melt pool and underlying powder results in an abruptly deforming melt pool and a pronounced decrease in both individual photodiode intensities and overall measured melt pool temperature. This work illustrates how to correctly interpret the coaxial photodiode monitoring signals in L-PBF by uncovering the intricate dynamics within the melt pool, particularly in challenging overhang regions, and pave the foundation for data-driven part qualification.

Wearable Sensor Node for Safety Improvement in Workplaces: Technology Assessment in a Simulated Environment.

Personal protective equipment (PPE) has been universally recognized for its role in protecting workers from injuries and illnesses. Smart PPE integrates Internet of Things (IoT) technologies to enable continuous monitoring of workers and their surrounding environment, preventing undesirable events, facilitating rapid emergency response, and informing rescuers of potential hazards. This work presents a smart PPE system with a sensor node architecture designed to monitor workers and their surroundings. The sensor node is equipped with various sensors and communication capabilities, enabling the monitoring of specific gases (VOC, CO2, CO, O2), particulate matter (PM), temperature, humidity, positional information, audio signals, and body gestures. The system utilizes artificial intelligence algorithms to recognize patterns in worker activity that could lead to risky situations. Gas tests were conducted in a special chamber, positioning capabilities were tested indoors and outdoors, and the remaining sensors were tested in a simulated laboratory environment. This paper presents the sensor node architecture and the results of tests on target risky scenarios. The sensor node performed well in all situations, correctly signaling all cases that could lead to risky situations.

Mechanical-computing metastructure for self-powered vibration sensing

Triboelectric nanogenerator-based vibration sensors (TVSs) present a promising approach for online vibration monitoring, offering early detection of excessive vibrations that pose risks to the functionality of devices and infrastructure. However, the structural design of most current TVSs lacks consideration for information processing capabilities, resulting in limited detection bandwidth and displacement signal fidelity. This study first introduces a quasi-zero-stiffness (QZS) mechanical-computing metastructure aimed at enhancing the structural intelligence of TVSs and extending their vibration signal detection bandwidth. The optimization of the QZS metastructure involved adjusting the length ratio and inclination angle of the zero-order polynomial beam to achieve ultra-low resonant frequencies. The results indicate that the TVS based on the mechanical-computing metastructure (MCM-TVS) is capable of efficiently detecting complex random signals, achieving an average measurement accuracy above 85 % for the spectral components of random vibration signals. Furthermore, the sensor exhibited remarkable linearity (99 %) in measuring vibration amplitude and maintained consistent sensitivity (with less than 25.8 % fluctuation), facilitating the lossless conversion of displacement to electrical signals. Consequently, this novel approach of incorporating an MCM into TVS design offers substantial insights for the advancement of high-performance vibration signal monitoring.

Frequency response sensitivity to crack for piezoelectric FGM beam subjected to moving load

Since functionally graded material (FGM) is increasingly used in high-tech engineering, free and forced vibrations of FGM structures become an important issue. This report addresses the analysis of frequency response sensitivity to crack for piezoelectric FGM beams subjected to moving load. First, a frequency domain model of a cracked FGM beam with a piezoelectric layer is conducted to derive an explicit expression of the electrical charge produced in the piezoelectric layer under the moving load. It was shown in the previous works of the authors that the electrical charge is a reliable representation of the beam frequency response to moving load and can be efficiently employed as a measured diagnostic signal for structural health monitoring. Then, a damage indicator acknowledged as a spectral damage index (SDI) calculated from the electrical frequency response is introduced and used for sensitivity analysis of the response to crack. Under the sensitivity analysis the effect also of FGM and moving load parameters on the sensitivity is examined and illustrated by numerical results.

Application of MIMO Neural Networks in Wireless Signal Monitoring

Application of MIMO Neural Networks in Wireless Signal Monitoring

Human Vital Signs Signal Monitoring and Repairment with an Optical Fiber Sensor Based on Deep Learning

Optical fiber sensors have been widely applied for their advantages such as small size, lightweight, and strong electronic interference robustness. Compared with current electronic sensors, optical fiber sensors perform better in measuring parameters in harsh environments, which makes them suitable for more and more applications, such as target tracing and detection and monitoring of health signs in medical services. However, due to fiber optic sensor failure, improper transmission and storage, or other reasons, missing data occur from time to time. Therefore, effective missing value processing methods are desirable as they can be used to facilitate data processing or analysis. In the present study, gated recurrent unit (GRU) interpolation is performed by using the generative adversarial network (GAN) model to process the irregular delay relationship between the data before and after the collection of incomplete vital signs data. Furthermore, a data interpolation model based on VS-E2E-GAN is proposed to reconstruct vital signs signals. The ROC curve (AUC), metrics including mean squared error (MSE), and accuracy (ACC) of experiments reach 0.901, 0.777, and 0.908, respectively, which indicates that the proposed VS-E2E-GAN model performs well in terms of vital signs data imputation and repairment, has strong robustness when compared with other works, and has potential clinical application in health monitoring, smart home, and so on.

Ensemble Learning Improves the Efficiency of Microseismic Signal Classification in Landslide Seismic Monitoring.

A deep-seated landslide could release numerous microseismic signals from creep-slip movement, which includes a rock-soil slip from the slope surface and a rock-soil shear rupture in the subsurface. Machine learning can effectively enhance the classification of microseismic signals in landslide seismic monitoring and interpret the mechanical processes of landslide motion. In this paper, eight sets of triaxial seismic sensors were deployed inside the deep-seated landslide, Jiuxianping, China, and a large number of microseismic signals related to the slope movement were obtained through 1-year-long continuous monitoring. All the data were passed through the seismic event identification mode, the ratio of the long-time average and short-time average. We selected 11 days of data, manually classified 4131 data into eight categories, and created a microseismic event database. Classical machine learning algorithms and ensemble learning algorithms were tested in this paper. In order to evaluate the seismic event classification performance of each algorithmic model, we evaluated the proposed algorithms through the dimensions of the accuracy, precision, and recall of each model. The validation results demonstrated that the best performing decision tree algorithm among the classical machine learning algorithms had an accuracy of 88.75%, while the ensemble algorithms, including random forest, Gradient Boosting Trees, Extreme Gradient Boosting, and Light Gradient Boosting Machine, had an accuracy range from 93.5% to 94.2% and also achieved better results in the combined evaluation of the precision, recall, and F1 score. The specific classification tests for each microseismic event category showed the same results. The results suggested that the ensemble learning algorithms show better results compared to the classical machine learning algorithms.

Remaining useful life prediction method based on stacked autoencoder and generalized wiener process for degrading bearing

Abstract Remaining Useful Life (RUL) prediction using deep learning networks primarily produces point estimates of RUL, but capturing the inherent uncertainty in RUL prediction is difficult. The use of the stochastic process approach can reflect the uncertainty in RUL predictions. However, the amount of data generated during equipment operation cannot be effectively utilized. This paper aims to propose an adaptive RUL prediction method tailored for extensive datasets and prediction uncertainty, effectively harnessing the strengths of deep learning methods in managing massive data and stochastic process techniques in quantifying uncertainties. RUL prediction method, based on Stacked Autoencoder (SAE) combined with Generalized Wiener Process, employs SAE to extract profound underlying features from the monitoring signals. Principal Component Analysis (PCA) is then used to select highly trending features as inputs. The output of PCA accurately reflects health status. A Generalized Wiener Process is used to construct a model for the evolution of the health indicators. The estimation values for the model parameters are determined using the Maximum Likelihood Estimation method. Furthermore, an adaptive update is performed based on Bayesian theory. Utilizing the sense of the first hitting time concept, the Probability Density Function for RUL prediction is derived accurately. Finally, the effectiveness and superiority of the proposed method is verified using numerical simulations and experimental studies of bearing degradation data. The method improves the life prediction accuracy while reducing the prediction uncertainty.

Sulfur-Functionalized Carbon Nanotubes with Inlaid Nanographene for 3D-Printing Micro-Supercapacitors and a Flexible Self-Powered Sensing System.

Digital fabrication of miniaturized micro-supercapacitors (MSCs) holds immense promise for advancing customized, integrated microelectronic systems. As potential electrode materials, carbonaceous nanomaterials, such as carbon nanotubes (CNTs), stand out due to their excellent conductivity and mechanical robustness yet suffer from low ionic storage sites, which restrict further applications. Herein, we introduce a sulfur-assisted in situ activating strategy for obtaining sulfur-functionalized carbon nanotube frameworks integrated with inlaid graphene nanosheets (S-CNT/GNS). Specifically, sulfur functionality enriches the surface charge density with improved interfacial hydrophilicity, while the inlaid nanographene sheets provide abundant ionic adsorption sites. By direct 3D printing of the S-CNT/GNS ink, planar MSCs were fabricated with desirable functionality and outstanding electrochemical performance. Notably, the developed MSCs exhibit a high areal capacitance of 0.47 F cm-2, an exceptional energy density of 64.6 μWh cm-2, and a high-power density of 34.2 mW cm-2. Furthermore, an all-flexible self-powered sensing system with photovoltaic cells and a stretchable sensor was built upon the customized S-CNT/GNS MSCs, demonstrating a highly effective capability for real-time monitoring of human physiological signals and body movements. This work not only presents a promising approach for the development of high-performance MSCs but also lays the groundwork for the creation of advanced wearable/flexible microelectronics systems.