Real-time Activity Monitoring Research Articles

Peran Sistem Informasi dalam Meningkatkan Kualitas Audit Keuangan Pemerintah Daerah

This study aims to examine the role of Information Systems (SI) in improving the quality of audits of local government financial statements. Audit of local government financial statements is a critical process in evaluating public finances and ensuring the reliability of financial information and compliance with applicable regulations. In recent years, the use of SI has become an important factor in improving audit quality by strengthening the efficiency, effectiveness, and accuracy of the audit process. A review of the literature reveals that the use of SI in auditing local government financial statements provides significant benefits. First, automation of the audit process using SI replaces error-prone manual tasks, thereby increasing audit efficiency. Second, SI facilitates efficient data collection and analysis, allowing auditors to access more accurate and comprehensive financial information. Third, SI strengthens internal supervision through real-time monitoring of financial activities and early detection of risks or policy violations. Finally, SI also provides protection against the security and integrity of sensitive data in auditing practices. However, the use of SI in auditing local government financial statements is also faced with challenges. These challenges include limited resources, complexity of system integration, security of sensitive data, and the need for increased auditor competence in understanding and using SI effectively. Through this research, it is hoped that a deeper understanding of the role of SI in improving the quality of audits of local government financial statements can be obtained. The results of this study can provide practical guidance and recommendations for auditors and local governments in making optimal use of SI, as well as overcoming challenges that may arise. Thus, it is expected that the practice of auditing local government financial statements can be more effective, accurate, and transparent, as well as provide significant benefits in public financial management.

Electrical Impedance Tomography for Real-Time Monitoring of Muscle Activity in Robot-Assisted Rehabilitation

This work looks at how Electrical Impedance Tomography (EIT) can be used in robot-assisted rehabilitation to make it easier to watch how muscle activity changes in real time. Quantitative methods are used in this study to look into how well EIT works, including full analyses and comparisons with common tracking methods like Electromyography (EMG). Both EIT and EMG are used to record the participants' mean and peak muscle activation levels as they do a set of Upper Limb, Lower Limb, and Full Body exercises. The study's findings show that EIT is incredibly flexible when it comes to capturing complex patterns of muscle activation across a wide range of rehabilitation routines. Each participant's involvement is shown by different patterns of muscle activation, and these patterns can be seen across different types of exercise. We can learn a lot about how muscles work during rehabilitation by measuring their mean and peak activation levels. This helps us see how well EIT works as a tracking tool. A very important step in validating EIT is to compare it to EMG readings. This shows how consistent and accurate EIT is in real-time monitoring situations. The high level of agreement between the two methods shows that EIT is a reliable alternative to standard monitoring methods, providing an easy to use and non-invasive way to measure muscle activity. The results of this study are even stronger because they include quantitative analyses and a detailed look at system performance data. We carefully check things like signal-to-noise ratio, reconstruction accuracy, and reliability, which shows how strong and dependable the combined EIT system is. These metrics are important for showing how well the system can measure things accurately and reliably in changing rehabilitation situations. This study adds to the growing amount of research on EIT uses in rehabilitation by showing that it can be a useful tool for keeping track of real-time muscle activity. This study opens the door for EIT to be widely used in rehabilitation by showing how muscle activation changes over time and proving its effectiveness against well-known methods. This will eventually lead to better patient outcomes and more effective rehabilitation.

Machine learning-guided engineering of genetically encoded fluorescent calcium indicators.

Here we used machine learning to engineer genetically encoded fluorescent indicators, protein-based sensors critical for real-time monitoring of biological activity. We used machine learning to predict the outcomes of sensor mutagenesis by analyzing established libraries that link sensor sequences to functions. Using the GCaMP calcium indicator as a scaffold, we developed an ensemble of three regression models trained on experimentally derived GCaMP mutation libraries. The trained ensemble performed an in silico functional screen on 1,423 novel, uncharacterized GCaMP variants. As a result, we identified the ensemble-derived GCaMP (eGCaMP) variants, eGCaMP and eGCaMP+, which achieve both faster kinetics and larger ∆F/F0 responses upon stimulation than previously published fast variants. Furthermore, we identified a combinatorial mutation with extraordinary dynamic range, eGCaMP2+, which outperforms the tested sixth-, seventh- and eighth-generation GCaMPs. These findings demonstrate the value of machine learning as a tool to facilitate the efficient engineering of proteins for desired biophysical characteristics.

Real-time monitoring of cortical brain activity in response to acute pain using wide-area Ca2+ imaging

Previous human and rodent studies indicated that nociceptive stimuli activate many brain regions that is involved in the somatosensory and emotional sensation. Although these studies have identified several important brain regions involved in pain perception, it has been a challenge to observe neural activity directly and simultaneously in these multiple brain regions during pain perception. Using a transgenic mouse expressing G-CaMP7 in majority of astrocytes and a subpopulation of excitatory neurons, we recorded the brain activity in the mouse cerebral cortex during acute pain stimulation. Both of hind paw pinch and intraplantar administration of formalin caused strong transient increase of the fluorescence in several cortical regions, including primary somatosensory, motor and retrosplenial cortex. This increase of the fluorescence intensity was attenuated by the pretreatment with morphine. The present study provides important insight into the cortico-cortical network during pain perception.

The Geomagnetic Stations Network of Mexico (REGMEX): The initial steps towards a real-time monitoring of geomagnetic activity in Mexico

In this paper we present our first steps towards the monitoring, in near real-time, of the geomagnetic activity in the continental territory of Mexico by the Space Weather National Laboratory (LANCE) via the Geomagnetic Stations Network of Mexico (REGMEX). REGMEX aims to monitor the geomagnetic field at five different sites in real-time. The collected data will be processed to promptly calculate and publish Mexico’s regional geomagnetic-activity indices (ΔH and K). Currently, Mexico lacks facilities to simultaneously monitor geomagnetic activity in real-time across its territory. This work shows the way in which LANCE endeavors to solve the lack of such geomagnetic data by presenting the REGMEX’s development plan and its current development status. In addition, we show geomagnetic storms as registered by our available regional geomagnetic indices and compare those records with their planetary counterparts.

Rationally Designed Functionalization of Single-Walled Carbon Nanotubes for Real-Time Monitoring of Cholinesterase Activity and Inhibition in Plasma.

Enzymes play a pivotal role in regulating numerous bodily functions. Thus, there is a growing need for developing sensors enabling real-time monitoring of enzymatic activity and inhibition. The activity and inhibition of cholinesterase (CHE) enzymes in blood plasma are fluorometrically monitored using near-infrared (NIR) fluorescent single-walled carbon nanotubes (SWCNTs) as probes, strategically functionalized with myristoylcholine (MC)- the substrate of CHE. A significant decrease in the fluorescence intensity of MC-suspended SWCNTs upon interaction with CHE is observed, attributed to the hydrolysis of the MC corona phase of the SWCNTs by CHE. Complementary measurements for quantifying choline, the product of MC hydrolysis, reveal a correlation between the fluorescence intensity decrease and the amount of released choline, rendering the SWCNTs optical sensors with real-time feedback in the NIR biologically transparent spectral range. Moreover, when synthetic and naturally abundant inhibitors inhibit the CHE enzymes present in blood plasma, no significant modulations of the MC-SWCNT fluorescence are observed, allowing effective detection of CHE inhibition. The rationally designed SWCNT sensors platform for monitoring of enzymatic activity and inhibition in clinically relevant samples is envisioned to not only advance the field of clinical diagnostics but also deepen further understanding of enzyme-related processes in complex biological fluids.

Multifunctional, Degradable Wearable Sensors Prepared with an Initiator and Crosslinker-Free Method.

The present zwitterionic hydrogel-based wearable sensor exhibits various limitations, such as limited degradation capacity, unavoidable toxicity resulting from initiators, and poor mechanical properties that cannot satisfy practical demands. Herein, we present an initiator and crosslinker-free approach to prepare polyethylene glycol (PEG)@poly[2-(methacryloyloxy)ethyl] dimethyl-(3-sulfopropyl) (PSBMA) interpenetrating polymer network (IPN) hydrogels that are self-polymerized via sunlight-induced and non-covalent crosslinking through electrostatic interaction and hydrogen bonding among polymer chains. The PEG@PSBMA IPN hydrogel possesses tissue-like softness, superior stretchability (∼2344.6% elongation), enhanced fracture strength (∼39.5 kPa), excellent biocompatibility, antibacterial property, reliable adhesion, and ionic conductivity. Furthermore, the sensor based on the IPN hydrogel demonstrates good sensitivity and cyclic stability, enabling effective real-time monitoring of human body activities. Moreover, it is worth noting that the excellent degradability in the saline solution within 8 h makes the prepared hydrogel-based wearable sensor free from the electronic device contamination. We believe that the proposed strategy for preparing physical zwitterionic hydrogels will pave the way for fabricating eco-friendly wearable devices.

Neural repair and regeneration interfaces: a comprehensive review.

Neural interfaces play a pivotal role in neuromodulation, as they enable precise intervention into aberrant neural activity and facilitate recovery from neural injuries and resultant functional impairments by modulating local immune responses and neural circuits. This review outlines the development and applications of these interfaces and highlights the advantages of employing neural interfaces for neural stimulation and repair, including accurate targeting of specific neural populations, real-time monitoring and control of neural activity, reduced invasiveness, and personalized treatment strategies. Ongoing research aims to enhance the biocompatibility, stability, and functionality of these interfaces, ultimately augmenting their therapeutic potential for various neurological disorders. The review focuses on electrophysiological and optophysiology neural interfaces, discussing functionalization and power supply approaches. By summarizing the techniques, materials, and methods employed in this field, this review aims to provide a comprehensive understanding of the potential applications and future directions for neural repair and regeneration devices.

FRET Sensor-Modified Synthetic Hydrogels for Real-Time Monitoring of Cell-Derived Matrix Metalloproteinase Activity using Fluorescence Lifetime Imaging.

Matrix remodeling plays central roles in a range of physiological and pathological processes and is driven predominantly by the activity of matrix metalloproteinases (MMPs), which degrade extracellular matrix (ECM) proteins. Our understanding of how MMPs regulate cell and tissue dynamics is often incomplete as in vivo approaches are lacking and many in vitro strategies cannot provide high-resolution, quantitative measures of enzyme activity in situ within tissue-like 3D microenvironments. Here, we incorporate a Förster resonance energy transfer (FRET) sensor of MMP activity into fully synthetic hydrogels that mimic many properties of the native ECM. We then use fluorescence lifetime imaging to provide a real-time, fluorophore concentration-independent quantification of MMP activity, establishing a highly accurate, readily adaptable platform for studying MMP dynamics in situ. MCF7 human breast cancer cells encapsulated within hydrogels highlight the detection of MMP activity both locally, at the sub-micron level, and within the bulk hydrogel. Our versatile platform may find use in a range of biological studies to explore questions in the dynamics of cancer metastasis, development, and tissue repair by providing high-resolution, quantitative and in situ readouts of local MMP activity within native tissue-like environments.

NMR spectroscopy for metabolomics in the living system: recent progress and future challenges.

Metabolism is a fundamental process that underlies human health and diseases. Nuclear magnetic resonance (NMR) techniques offer a powerful approach to identify metabolic processes and track the flux of metabolites at the molecular level in living systems. An in vitro study through in-cell NMR tracks metabolites in real time and investigates protein structures and dynamics in a state close to their most natural environment. This technique characterizes metabolites and proteins involved in metabolic pathways in prokaryotic and eukaryotic cells. In vivo magnetic resonance spectroscopy (MRS) enables whole-organism metabolic monitoring by visualizing the spatial distribution of metabolites and targeted proteins. One limitation of these NMR techniques is the sensitivity, for which a possible improved approach is through isotopic enrichment or hyperpolarization methods, including dynamic nuclear polarization (DNP) and parahydrogen-induced polarization (PHIP). DNP involves the transfer of high polarization from electronic spins of radicals to surrounding nuclear spins for signal enhancements, allowing the detection of low-abundance metabolites and real-time monitoring of metabolic activities. PHIP enables the transfer of nuclear spin polarization from parahydrogen to other nuclei for signal enhancements, particularly in proton NMR, and has been applied in studies of enzymatic reactions and cell signaling. This review provides an overview of in-cell NMR, in vivo MRS, and hyperpolarization techniques, highlighting their applications in metabolic studies and discussing challenges and future perspectives.

Antibacterial highly sensitive eco-friendly wearable piezoresistive sensor for monitoring multiple physiological parameters

Recent progress in wearable biomedical devices has made real-time monitoring possible through non-invasive wearable strain sensors, which can transform physical or biological signals into electrical responses. This work has fabricated multifunctional wearable strain sensors by coating composites (SiE-µG) of electrochemically exfoliated micro-sized multi-layer graphene flakes (µG) and Silicone elastomer (SiE) onto stretchable textile-based orthopedic bandage having ZnO adhesive (SiE-µG@Bandage). The composite coated textile sensors exhibited a remarkable sensitivity of 18330, large stretchability of 35%, superior mechanical durability of 50000 cycles, minute detection limit of 0.04% strain, and fast response time of 400 and recovery time 900 ms. The SiE-µG@Bandage also showed antibacterial properties against Escherichia coli (E. coli) bacteria. Additionally, zinc oxide-based skin-friendly and non-toxic adhesive on the backside of the bandage allowed stable and conformal attachment with the skin. Owing to the above merits, the sensors have shown applicability in real-time monitoring of various physical activities, physiological signals, joint movements, yoga postures, and meditation. Furthermore, the relevance of the sensors in the man-machine interaction has been demonstrated via real-time monitoring of traffic signals by finger movements. Moreover, the exceptional performance and specific features of the SiE-µG@Bandage sensors have enabled their application as electronics skins in health care and preventive care.

Feasibility of a remote heart rate variability biofeedback intervention for reducing anxiety in cardiac arrest survivors: A pilot trial

BackgroundHeart rate variability biofeedback (HRVB) is a promising non-pharmacologic approach for reducing anxiety. This intervention's feasibility needs testing in psychologically distressed cardiac patients for whom heart-related anxiety is a core concern. To enhance scalability and convenience, remote delivery of HRVB also needs to be assessed. Accordingly, we evaluated the feasibility of remote HRVB in survivors of cardiac arrest (CA) with elevated CA-related psychological distress. MethodsThe intervention was comprised of daily sessions of diaphragmatic paced breathing and real-time monitoring of cardiac activity guided by a smartphone app and heart rate monitor. This single-arm feasibility trial assessed the percentage of eligible contacted patients who consented and engaged in the study and the self-reported acceptability, feasibility, appropriateness, and usability of the intervention. Exploratory analyses assessed pre-to-post changes in trait anxiety, negative affect, cardiac-related interoceptive fear, and resting-state HRV. ResultsOf 12 eligible CA survivors contacted, 10 enrolled. All 10 patients completed the virtual study visits and the majority (>50 %) of prescribed training sessions. Ninety percent reported good scores for intervention acceptability and feasibility, and 80 % reported good scores for its appropriateness and usability for reducing fear. Trait anxiety decreased significantly pre-to-post intervention. There were no changes in negative affect, interoceptive fear, or resting state HRV. ConclusionA remotely delivered HRVB intervention was acceptable, feasible, and useable for cardiac patients with CA-related psychological distress. A phase 2 randomized controlled trial evaluating the efficacy of HRVB on cardiac patients’ psychological distress, health behaviors, and autonomic dysfunction may be warranted.

Stretchable, Self-Rolled, Microfluidic Electronics Enable Conformable Neural Interfaces of Brain and Vagus Neuromodulation.

Implantable neuroelectronic interfaces have gained significant importance in long-term brain-computer interfacing and neuroscience therapy. However, due to the mechanical and geometrical mismatches between the electrode-nerve interfaces, personalized and compatible neural interfaces remain serious issues for peripheral neuromodulation. This study introduces the stretchable and flexible electronics class as a self-rolled neural interface for neurological diagnosis and modulation. These stretchable electronics are made from liquid metal-polymer conductors with a high resolution of 30 μm using microfluidic printing technology. They exhibit high conformability and stretchability (over 600% strain) during body movements and have good biocompatibility during long-term implantation (over 8 weeks). These stretchable electronics offer real-time monitoring of epileptiform activities with excellent conformability to soft brain tissue. The study also develops self-rolled microfluidic electrodes that tightly wind the deforming nerves with minimal constraint (160 μm in diameter). The in vivo signal recording of the vagus and sciatic nerve demonstrates the potential of self-rolled cuff electrodes for sciatic and vagus neural modulation by recording action potential and reducing heart rate. The findings of this study suggest that the robust, easy-to-use self-rolled microfluidic electrodes may provide useful tools for compatible neuroelectronics and neural modulation.

Real-Time Monitoring of RAS Activity Using In Vitro and In-Cell NMR Spectroscopy.

The activation level of RAS can be determined by GTP hydrolysis rate (khy) and GDP-GTP exchange rates (kex). Either impaired GTP hydrolysis or enhanced GDP-GTP exchange causes the aberrant activation of RAS in oncogenic mutants. Therefore, it is important to quantify the khy and kex for understanding the mechanisms of RAS oncogenesis and drug development. Conventional methods have individually measured the kex and khy of RAS. However, within the intracellular environment, GTP hydrolysis and GDP-GTP exchange reactions occur simultaneously under conditions where GTP concentration is kept constant. In addition, the intracellular activity of RAS is influenced by endogenous regulatory proteins, such as RAS GTPase activating proteins (GAPs) and the guanine-nucleotide exchange factors (GEFs). Here, we describe the in vitro and in-cell NMR methods to estimate the khy and kex simultaneously by measuring the time-dependent changes of the fraction of GTP-bound ratio under the condition of constant GTP concentration.

A stand-alone proximity-based gaming wearable for remote physical activity monitoring

Abstract The Internet of Things (IoT) and wearable computing are crucial elements of modern information systems and applications in which advanced features for user interactivity and monitoring are required. However, in the fields of pervasive gaming, IoT has had limited real-world applications. In this work, we present a prototype of a wearable platform for pervasive games that combines IoT with wearable computing to enable the real-time monitoring of physical activity. The main objective of the solution is to promote the utilization of gamification techniques to enhance the physical activity of users through challenges and quests. This aims to create a symbolic link between the virtual gameplay and the real-world environment without the requirement of a smartphone. With the integration of sensors and wearable devices by design, the platform has the capability of real-time monitoring the users’ physical activity during the game. The system performance results highlight the efficiency and attractiveness of the wearable platform for gamifying physical activity.

The Prospect of Using Artificial Intelligence in TNI Ship Information Systems as a Manifestation of a Resilient Maritime Defense Industry

This research investigates the prospects of using Artificial Intelligence (AI) in the Indonesian Navy's ship information system as a crucial step in developing a robust and innovative maritime defense industry. The primary focus is on the application of AI technology in the ship information system, analyzing various aspects, including the potential implementation of AI in monitoring and analyzing maritime waters, technical and security challenges that may arise, expected benefits, and the strategic impact of AI implementation in Indonesia's maritime defense industry. The research method employs the Systematic Literature Review (SLR) method, providing a systematic and comprehensive approach to collecting, evaluating, and analyzing relevant literature in a specific field. This study involves literature searches using keywords such as artificial intelligence, information systems, maritime defense industry, Indonesian defense, and Indonesian Navy ships from 2011 to 2023 in various sources such as scholarly journals, conferences, and online databases. The research findings indicate that the use of artificial intelligence in the Indonesian Navy's ship information system has significant potential to improve maritime monitoring, threat detection, operational efficiency, and international collaboration. The integration of AI with advanced sensors such as radar, sonar, and thermal cameras enables more accurate real-time monitoring of activities in maritime waters. Higher accuracy in monitoring and threat detection also contributes to increased situational awareness and smarter decision-making by operational leaders in the Indonesian Navy. Despite the substantial benefits, AI implementation faces several challenges, including initial investment costs, infrastructure availability, personnel training, data security, ethical regulations, dependence on foreign technology, integration and compatibility issues, AI technology reliability, cultural and organizational changes, and long-term benefits. To address these challenges, it is recommended that the government allocates budgets wisely, develops adequate technological infrastructure, provides personnel training, and formulates ethical regulations. Strengthening independence in the development of domestic AI technology is also crucial.

Smart Lithium-Ion Battery Monitoring in Electric Vehicles: An AI-Empowered Digital Twin Approach

This paper presents a transformative methodology that harnesses the power of digital twin (DT) technology for the advanced condition monitoring of lithium-ion batteries (LIBs) in electric vehicles (EVs). In contrast to conventional solutions, our approach eliminates the need to calibrate sensors or add additional hardware circuits. The digital replica works seamlessly alongside the embedded battery management system (BMS) in an EV, delivering real-time signals for monitoring. Our system is a significant step forward in ensuring the efficiency and sustainability of EVs, which play an essential role in reducing carbon emissions. A core innovation lies in the integration of the digital twin into the battery monitoring process, reshaping the landscape of energy storage and alternative power sources such as lithium-ion batteries. Our comprehensive system leverages a cloud-based IoT network and combines both physical and digital components to provide a holistic solution. The physical side encompasses offline modeling, where a long short-term memory (LSTM) algorithm trained with various learning rates (LRs) and optimized by three types of optimizers ensures precise state-of-charge (SOC) predictions. On the digital side, the digital twin takes center stage, enabling the real-time monitoring and prediction of battery activity. A particularly innovative aspect of our approach is the utilization of a time-series generative adversarial network (TS-GAN) to generate synthetic data that seamlessly complement the monitoring process. This pioneering use of a TS-GAN offers an effective solution to the challenge of limited real-time data availability, thus enhancing the system’s predictive capabilities. By seamlessly integrating these physical and digital elements, our system enables the precise analysis and prediction of battery behavior. This innovation—particularly the application of a TS-GAN for data generation—significantly contributes to optimizing battery performance, enhancing safety, and extending the longevity of lithium-ion batteries in EVs. Furthermore, the model developed in this research serves as a benchmark for future digital energy storage in lithium-ion batteries and comprehensive energy utilization. According to statistical tests, the model has a high level of precision. Its exceptional safety performance and reduced energy consumption offer promising prospects for sustainable and efficient energy solutions. This paper signifies a pivotal step towards realizing a cleaner and more sustainable future through advanced EV battery management.

Real-time Interpretation of EEG Signals for Consciousness State Assessment

Assessing the level of consciousness is critical in clinical practice, especially for patients with traumatic brain injuriesor those in a coma or vegetative state. Traditional methods like the Glasgow Coma Scale have limitations, such asinter-observer variability and low sensitivity. In recent years, electroencephalography (EEG) has emerged as a promisingapproach for assessing consciousness, offering non-invasive, real-time monitoring of brain activity. In this study, we propose a real-time analysis system for assessing consciousness levels using a portable EEG device. Our system analyzes EEG signals and provides valuable insights into consciousness levels, enabling prompt clinical interventions. The real-time nature of our system allows for continuous monitoring and immediate assessment of consciousness levels. Compared to traditional methods, our system offers advantages in terms of real-time functionality, providing a comprehensive evaluation of consciousness. Through extensive experiments using real patient data, our systemdemonstrates its value as a valuable tool for assessing consciousness levels in clinical practice. It offers healthcare professionals an efficient and reliable method for evaluating consciousness.

Multiobjective optimization-based decision support for building digital twin maturity measurement

The digital twin (DT) represents a powerful tool for advancing construction industry to provide a cyber–physical integration that enables real-time monitoring of assets and activities and facilitates decision-making. Due to the inherent characteristics of the construction industry and the diverse possibilities with DT, proliferation of building digital twin (BDT) necessitates a comprehensive comprehension of its evolution and the creation of roadmaps. This paper aims to contribute to the formalization and standardization of BDT. It designs a novel assessment framework for the overall maturity measurement of existing BDT projects. The developed BDT maturity model incorporates a collective opinion generation paradigm based on a fairness-aware multiobjective optimization model to provide an expert-based evaluation system for evaluating the maturity of BDT projects. The effectiveness and feasibility of the proposed framework have been validated through a case study of an experimental BDT initiative. This paper establishes a generalizable framework for BDT maturity assessment that can offer insights into BDT maturity standards to construction practitioners to create effective strategies for the diffusion, development, and maturation of BDT.

SENSING MECHANISM AND APPLICATION OF MECHANICAL STRAIN SENSOR: A MINI-REVIEW

This study reviews the potential of flexible strain sensors based on nanomaterials such as carbon nanotubes (CNTs), graphene, and metal nanowires (NWs). These nanomaterials have excellent flexibility, conductivity, and mechanical properties, which enable them to be integrated into clothing or attached to the skin for the real-time monitoring of various activities. However, the main challenge is balancing high stretchability and sensitivity. This paper explains the basic concept of strain sensors that can convert mechanical deformation into electrical signals. Moreover, this paper focuses on simple, flexible, and stretchable resistive and capacitive sensors. It also discusses the important factors in choosing materials and fabrication methods, emphasizing the crucial role of suitable polymers in high-performance strain sensing. This study reviews the fabrication processes, mechanisms, performance, and applications of stretchable strain sensors in detail. It analyzes key aspects, such as sensitivity, stretchability, linearity, response time, and durability. This review provides useful insights into the current status and prospects of stretchable strain sensors in wearable technology and human–machine interfaces.