Monitoring Devices Research Articles

Sensors and Devices Guided by Artificial Intelligence for Personalized Pain Medicine.

Personalized pain medicine aims to tailor pain treatment strategies for the specific needs and characteristics of an individual patient, holding the potential for improving treatment outcomes, reducing side effects, and enhancing patient satisfaction. Despite existing pain markers and treatments, challenges remain in understanding, detecting, and treating complex pain conditions. Here, we review recent engineering efforts in developing various sensors and devices for addressing challenges in the personalized treatment of pain. We summarize the basics of pain pathology and introduce various sensors and devices for pain monitoring, assessment, and relief. We also discuss advancements taking advantage of rapidly developing medical artificial intelligence (AI), such as AI-based analgesia devices, wearable sensors, and healthcare systems. We believe that these innovative technologies may lead to more precise and responsive personalized medicine, greatly improved patient quality of life, increased efficiency of medical systems, and reducing the incidence of addiction and substance use disorders.

One-Channel Wearable Mental Stress State Monitoring System.

Assessments of stress can be performed using physiological signals, such as electroencephalograms (EEGs) and galvanic skin response (GSR). Commercialized systems that are used to detect stress with EEGs require a controlled environment with many channels, which prohibits their daily use. Fortunately, there is a rise in the utilization of wearable devices for stress monitoring, offering more flexibility. In this paper, we developed a wearable monitoring system that integrates both EEGs and GSR. The novelty of our proposed device is that it only requires one channel to acquire both physiological signals. Through sensor fusion, we achieved an improved accuracy, lower cost, and improved ease of use. We tested the proposed system experimentally on twenty human subjects. We estimated the power spectrum of the EEG signals and utilized five machine learning classifiers to differentiate between two levels of mental stress. Furthermore, we investigated the optimum electrode location on the scalp when using only one channel. Our results demonstrate the system's capability to classify two levels of mental stress with a maximum accuracy of 70.3% when using EEGs alone and 84.6% when using fused EEG and GSR data. This paper shows that stress detection is reliable using only one channel on the prefrontal and ventrolateral prefrontal regions of the brain.

A synergistic enhancement strategy for mechanical and conductive properties of hydrogels with dual ionically cross-linked κ-carrageenan/poly(sodium acrylate-co-acrylamide) network

Applying conductive hydrogels in electronic skin, health monitoring, and wearable devices has aroused great research interest. Yet, it remains a significant challenge to prepare conductive hydrogels simultaneously with superior mechanical, self-recovery, and conductivity performance. Herein, a dual ionically cross-linked double network (DN) hydrogel is fabricated based on K+ and Fe3+ ion cross-linked κ-carrageenan (κ-CG) and Fe3+ ion cross-linked poly(sodium acrylate-co-acrylamide) P(AANa-co-AM). Benefiting from the abundance of hydrogen bonds and metal coordination bonds, the conductive hydrogel has excellent mechanical properties (fracture strain up to 1420 %, fracture stress up to 2.30 MPa, and toughness up to 20.63 MJ/m3) and good self-recovery performance (the recovery rate of the toughness can reach 85 % after waiting for 1 h). Meanwhile, due to the introduction of dual metal ions of K+ and Fe3+, the ionic conductivity of conductive hydrogel is up to 1.42 S/m. Furthermore, the hydrogel strain sensor has good sensitivity with a gauge factor (GF) of 2.41 (0–100 %). It can be a wearable sensor that monitors different human motions, such as sit-ups. This work offers a new synergistic strategy for designing a hydrogel strain sensor with high mechanical, self-recovery, and conductive properties.

Advances in Portable and Wearable Acoustic Sensing Devices for Human Health Monitoring.

The practice of auscultation, interpreting body sounds to assess organ health, has greatly benefited from technological advancements in sensing and electronics. The advent of portable and wearable acoustic sensing devices marks a significant milestone in telemedicine, home health, and clinical diagnostics. This review summarises the contemporary advancements in acoustic sensing devices, categorized based on varied sensing principles, including capacitive, piezoelectric, and triboelectric mechanisms. Some representative acoustic sensing devices are introduced from the perspective of portability and wearability. Additionally, the characteristics of sound signals from different human organs and practical applications of acoustic sensing devices are exemplified. Challenges and prospective trends in portable and wearable acoustic sensors are also discussed, providing insights into future research directions.

Performance of a Novel Continuous Glucose Monitoring Device in People With Diabetes.

In this multicenter study, performance of a novel continuous glucose monitoring (CGM) system was evaluated. Adult participants with diabetes were included in the study. They each wore three sensors of the CGM system on the upper arms for up to 14 days. During four in-clinic visits, frequent comparison measurements with capillary blood glucose (BG) samples were performed. The primary endpoint was the 20/20 agreement rate (AR): the percentage of CGM readings within ±20 mg/dL (at BG values <100 mg/dL) or ±20% (at BG values ≥100 mg/dL) of the comparator. Further evaluations included mean absolute relative difference (MARD) and 20/20 AR in different BG ranges and across the wear time. Data from 48 participants and 139 sensors were analyzed. During in-clinic sessions the 20/20 AR was 90.5% and the MARD was 9.2%. For BG ranges <70, 70-180, and >180 mg/dL, 20/20 AR was 94.3%, 89.0%, and 92.5%, respectively. At the beginning, middle, and end of sensor wear time, 20/20 AR was 92.8%, 91.5%, and 85.9%, respectively. The 14-day survival probability was 82.4%. Pain and bleeding after sensor insertion were within the expected range. Based on the study outcome, the use of the device is regarded as safe. The system showed a good performance compared to capillary BG measurements. This level of accuracy could be shown over the entire measurement range, especially in the low glycemic range, and the whole wear time of the sensors. The results of this study are supporting a non-adjunctive use of the device.

Prediction of intraoperative hypotension using deep learning models based on non-invasive monitoring devices.

Intraoperative hypotension is associated with adverse outcomes. Predicting and proactively managing hypotension can reduce its incidence. Previously, hypotension prediction algorithms using artificial intelligence were developed for invasive arterial blood pressure monitors. This study tested whether routine non-invasive monitors could also predict intraoperative hypotension using deep learning algorithms. An open-source database of non-cardiac surgery patients ( https://vitadb.net/dataset ) was used to develop the deep learning algorithm. The algorithm was validated using external data obtained from a tertiary Korean hospital. Intraoperative hypotension was defined as a systolic blood pressure less than 90 mmHg. The input data included five monitors: non-invasive blood pressure, electrocardiography, photoplethysmography, capnography, and bispectral index. The primary outcome was the performance of the deep learning model as assessed by the area under the receiver operating characteristic curve (AUROC). Data from 4754 and 421 patients were used for algorithm development and external validation, respectively. The fully connected model of Multi-head Attention architecture and the Globally Attentive Locally Recurrent model with Focal Loss function were able to predict intraoperative hypotension 5min before its occurrence. The AUROC of the algorithm was 0.917 (95% confidence interval [CI], 0.915-0.918) for the original data and 0.833 (95% CI, 0.830-0.836) for the external validation data. Attention map, which quantified the contributions of each monitor, showed that our algorithm utilized data from each monitor with weights ranging from 8 to 22% for determining hypotension. A deep learning model utilizing multi-channel non-invasive monitors could predict intraoperative hypotension with high accuracy. Future prospective studies are needed to determine whether this model can assist clinicians in preventing hypotension in patients undergoing surgery with non-invasive monitoring.

Simultaneous Detection of Biomarkers in Urine Using a Multicalibration Potentiometric Sensing Array Combined with a Portable Analyzer.

Domestic monitoring devices make real-time and long-term health monitoring possible, allowing people to track their health status regularly. Uric acid (UA), creatinine, and urea in urine are three important biomarkers for various diseases, especially kidney diseases. This work proposed a 10-channel potentiometric sensing array containing a UA electrode group, a creatinine electrode group, a urea electrode group, a pH electrode group, and one pair of reference channels, which could be connected with a portable potentiometric analyzer, realizing the simultaneous detection of UA, creatinine, urea, and pH in urine. The prepared Pt/carbon nanotubes (CNTs)-uricase, creatinine deiminase, Au@urease, and polyaniline were employed as the sensing materials, showing responses to four targets with high sensitivity and selectivity. To improve the accuracy of domestic monitoring, a calibration channel was integrated into each electrode group to calibrate the basic potential of the sensing channels, and the influences of pH and temperature on the responses were investigated through the pH electrode group and an external temperature probe to calibrate the slope and intercept. With the preset of the deduced calibration parameters and computational formula for the four targets in the analyzer in Lab Mode, the concentrations of UA, creatinine, and urea and the pH of the human urine samples were directly displayed on the screen of the analyzer in Practical Mode. The agreement of these results with those obtained from commercial kits and pH meters reveals the high potential of these methods for developing domestic devices to facilitate health monitoring.

Innovation and challenges of artificial intelligence technology in personalized healthcare

As the burgeoning field of Artificial Intelligence (AI) continues to permeate the fabric of healthcare, particularly in the realms of patient surveillance and telemedicine, a transformative era beckons. This manuscript endeavors to unravel the intricacies of recent AI advancements and their profound implications for reconceptualizing the delivery of medical care. Through the introduction of innovative instruments such as virtual assistant chatbots, wearable monitoring devices, predictive analytic models, personalized treatment regimens, and automated appointment systems, AI is not only amplifying the quality of care but also empowering patients and fostering a more interactive dynamic between the patient and the healthcare provider. Yet, this progressive infiltration of AI into the healthcare sphere grapples with a plethora of challenges hitherto unseen. The exigent issues of data security and privacy, the specter of algorithmic bias, the requisite adaptability of regulatory frameworks, and the matter of patient acceptance and trust in AI solutions demand immediate and thoughtful resolution .The importance of establishing stringent and far-reaching policies, ensuring technological impartiality, and cultivating patient confidence is paramount to ensure that AI-driven enhancements in healthcare service provision remain both ethically sound and efficient. In conclusion, we advocate for an expansion of research efforts aimed at navigating the ethical complexities inherent to a technology-evolving landscape, catalyzing policy innovation, and devising AI applications that are not only clinically effective but also earn the trust of the patient populace. By melding expertise across disciplines, we stand at the threshold of an era wherein AI's role in healthcare is both ethically unimpeachable and conducive to elevating the global health quotient.

Assessment of blood pressure variability: characteristics and comparison of blood pressure measurement methods.

Previous studies have reported that blood pressure variability (BPV) is associated with the risk of cardiovascular events independent of blood pressure (BP) levels. While there is little evidence from intervention trials examining whether suppressing BPV is useful in preventing cardiovascular disease, it is suggested that detection of abnormally elevated BPV may be useful in reducing cardiovascular events adding by complementing management of appropriate BP levels. Cuffless BP devices can assess beat-to-beat BPV. Although cuffless BP monitoring devices have measurement accuracy issues that need to be resolved, this is an area of research where the evidence is accumulating rapidly, with many publications on beat-to-beat BPV over several decades. Ambulatory BP monitoring (ABPM) can assess 24-hour BPV and nocturnal dipping patterns. Day-to-day BPV and visit-to-visit BPV are assessed by self-measured BP monitoring at home and office BP measurement, respectively. 24 h, day-to-day, and visit-to-visit BPV have been reported to be associated with cardiovascular prognosis. Although there have been several studies comparing whether ABPM and self-measured BP monitoring at home is the superior measurement method of BPV, no strong evidence has been accumulated that indicates whether ABPM or self-measured home BP is superior. ABPM and self-measured BP monitoring have their own advantages and complement each other in the assessment of BPV.

Patient Driven EKG Device Performance in Adults with Fontan Palliation.

The aim of this study was to evaluate the accuracy of the KardiaMobile (KM) device in adults with a Fontan palliation, and to assess the KM function as a screening tool for atrial arrhythmias. While patient driven electrocardiogram (EKG) devices are becoming a validated way to evaluate cardiac arrhythmias, their role for patients with congenital heart disease is less clear. Patients with single ventricle Fontan palliation have a high prevalence of atrial arrhythmias and represent a unique cohort that could benefit from early detection of atrial arrhythmias. This single center prospective study enrolled adult patients with Fontan palliation to use the KM heart rhythm monitoring device for both symptomatic episodes and asymptomatic weekly screening over a 1-year period. Accuracy was assessed by comparing the automatic KM interpretation (KM-auto) to an electrophysiologist overread (KM-EP) and traditional EKG. Fifty patients were enrolled and 510 follow-up transmissions were received. The sensitivity and specificity of enrollment KM-auto compared to EKG was 65% and 100%, respectively. The sensitivity and specificity of enrollment KM-auto compared to the KM-EP was 75% and 96%, respectively. In the adult Fontan palliation, the accuracy of the KM device to detect a normal rhythm was reliable and best with a physician overread. Abnormal or uninterpretable KM-auto device interpretations, symptomatic transmissions, and any transmissions with a high heart rate compared to a patient's normal baseline should warrant further review.

Airborne particulate matter measurement and prediction with machine learning techniques

Air quality is a fundamental component of a healthy environment for human beings. Monitoring networks for air pollution have been established in numerous industrial zones. The data collected by the pervasive monitoring devices can be utilized not only for determining the current environmental condition, but also for forecasting it in the near future. This paper considers the applications of different machine learning methods for the prediction of the two most widely used quantities. Particulate matter (PM) with a diameter of 2.5 and 10 µm, respectively. The data are collected via a proprietary monitoring station, designated as the Ecolumn. The Ecolumn monitors a number of key parameters, including temperature, pressure, humidity, PM 1.0, PM 2.5, and PM 10, in a timely manner. The data were employed in the development of multiple models based on selected machine learning methods. The decision tree, random forest, recurrent neural network, and long short-term memory models were employed. Experiments were conducted with varying hyperparameters and network architectures. Different time scales (10 min, 1 h, and 24 h) were examined. The most optimal results were observed for the Long Short-Term Memory algorithm when utilizing the shortest available time spans (shortest averaging times). The decision tree and random forest algorithms demonstrated unexpectedly high performance for long averaging times, exhibiting only a slight decline in accuracy compared to neural networks for shorter averaging times. Recommendations for the potential applicability of the tested methods were formulated.

Clinical Impact of Self-Recognition of Recurrent Acute Myocardial Infarction: From KRMI-RCC.

Background/Objectives: Self-recognition of recurrent myocardial infarction (re-MI) may be essential for reducing prehospital time contrast to awareness of re-MI symptoms. However, data on the current status and clinical impact of self-recognition of re-MI are limited in the contemporary period. Thus, this study aimed to increase this body of knowledge. Methods: We enrolled 1018 patients with re-MI using data from the Korean Registry of Acute Myocardial Infarction for Regional Cardiocerebrovascular Centres. The patients were classified into self-recognised MI and unrecognised MI groups, and the differences between them were compared. Results: The rate of self-recognition among the patients with previous experience of MI was only 52.4%. Among the patients with re-MI, factors associated with self-recognition included recent first MI within 3 years, prior dyslipidaemia, two or more MI symptoms, and the male gender (p < 0.05). Factors associated with a lack of recognition were older age (≥70 years), prior stroke, and cancer history (p < 0.05). The proportion of symptoms-to-emergency room arrival time within 90 min among the patients with ST-elevation MI was significantly higher in the self-recognised group than in the unrecognised group (52.6% vs. 31.6%, p < 0.001). The self-recognised group showed a lower in-hospital mortality rate (1.5% vs. 6.2%, p < 0.001), and this benefit was maintained even after 1 year (hazard ratio: 0.53; p < 0.001). Conclusions: Only half of the patients who previously experienced a MI recognised a re-MI when it occurred. This recognition reduced prehospital delay and led to higher survival rates, which highlights the importance of patient education as well as objective monitoring devices, irrespective of individual recognition ability for immediate response.

Developing IoT Sustainable Real-Time Monitoring Devices for Food Supply Chain Systems Based on Climate Change Using Circular Intuitionistic Fuzzy Set

Developing IoT Sustainable Real-Time Monitoring Devices for Food Supply Chain Systems Based on Climate Change Using Circular Intuitionistic Fuzzy Set

Efficient Sleep Stage Identification Using Piecewise Linear EEG Signal Reduction: A Novel Algorithm for Sleep Disorder Diagnosis.

Sleep is a vital physiological process for human health, and accurately detecting various sleep states is crucial for diagnosing sleep disorders. This study presents a novel algorithm for identifying sleep stages using EEG signals, which is more efficient and accurate than the state-of-the-art methods. The key innovation lies in employing a piecewise linear data reduction technique called the Halfwave method in the time domain. This method simplifies EEG signals into a piecewise linear form with reduced complexity while preserving sleep stage characteristics. Then, a features vector with six statistical features is built using parameters obtained from the reduced piecewise linear function. We used the MIT-BIH Polysomnographic Database to test our proposed method, which includes more than 80 h of long data from different biomedical signals with six main sleep classes. We used different classifiers and found that the K-Nearest Neighbor classifier performs better in our proposed method. According to experimental findings, the average sensitivity, specificity, and accuracy of the proposed algorithm on the Polysomnographic Database considering eight records is estimated as 94.82%, 96.65%, and 95.73%, respectively. Furthermore, the algorithm shows promise in its computational efficiency, making it suitable for real-time applications such as sleep monitoring devices. Its robust performance across various sleep classes suggests its potential for widespread clinical adoption, making significant advances in the knowledge, detection, and management of sleep problems.

Soybean protein-reinforced highly extensible and self-adhesive hydrogels with ultra-sensitive strain sensing for flexible electronic devices

There is a growing interest in utilizing sustainable and renewable biomass resources to construct high-performance hydrogels for flexible electronics. In this study, soybean protein isolate (SPI) with abundant functional groups was introduced into the hydrogel matrix, while the connection between SPI, MXene and the polymer network was strengthened by hydrogen bonding. The sensible strategy overcame the problems of easy oxidation, agglomeration and weak interactions that existed in the binding of conductive filler MXene nanosheets with polymer chains. The prepared composite hydrogel exhibited integrated properties of high stretchability (>800 %), excellent self-adhesive property, plasticity, injectability, cyclic stability and durability in extreme environments (-20–60°C). Noteworthily, this hydrogel demonstrated high sensitivity and stable electrical resistance response to minor changes in tension, compression, bending, and temperature. In addition, this hydrogel exhibited excellent UV isolation and perspiration properties, making it ideal for practical applications of flexible devices in sports environments. The preparation of green, multipurpose, flexible electronics and human motion monitoring devices is made easier by the practical approach in this study.

Deep learning-aided respiratory motion compensation in PET/CT: addressing motion induced resolution loss, attenuation correction artifacts and PET-CT misalignment.

Respiratory motion (RM) significantly impacts image quality in thoracoabdominal PET/CT imaging. This study introduces a unified data-driven respiratory motion correction (uRMC) method, utilizing deep learning neural networks, to solve all the major issues caused by RM, i.e., PET resolution loss, attenuation correction artifacts, and PET-CT misalignment. In a retrospective study, 737 patients underwent [18F]FDG PET/CT scans using the uMI Panorama PET/CT scanner. Ninety-nine patients, who also had respiration monitoring device (VSM), formed the validation set. The remaining data of the 638 patients were used to train neural networks used in the uRMC. The uRMC primarily consists of three key components: (1) data-driven respiratory signal extraction, (2) attenuation map generation, and (3) PET-CT alignment. SUV metrics were calculated within 906 lesions for three approaches, i.e., data-driven uRMC (proposed), VSM-based uRMC, and OSEM without motion correction (NMC). RM magnitude of major organs were estimated. uRMC enhanced diagnostic capabilities by revealing previously undetected lesions, sharpening lesion contours, increasing SUV values, and improving PET-CT alignment. Compared to NMC, uRMC showed increases of 10% and 17% in SUVmax and SUVmean across 906 lesions. Sub-group analysis showed significant SUV increases in small and medium-sized lesions with uRMC. Minor differences were found between VSM-based and data-driven uRMC methods, with the SUVmax was found statistically marginal significant or insignificant between the two methods. The study observed varied motion amplitudes in major organs, typically ranging from 10 to 20mm. A data-driven solution for respiratory motion in PET/CT has been developed, validated and evaluated. To the best of our knowledge, this is the first unified solution that compensates for the motion blur within PET, the attenuation mismatch artifacts caused by PET-CT misalignment, and the misalignment between PET and CT.

PIONEERING ADVANCEMENTS IN UROLOGY: ARTIFICIAL INTELLIGENCE, GENOMIC INSIGHTS, NOVEL TREATMENTS, DIAGNOSTICS, AND TARGETED THERAPIES TRANSFORMING PATIENT CARE

Recent advancements in urology are transforming patient care by integrating artificial intelligence (AI), genomic insights, and novel therapeutic approaches. This review explores the significant strides made in the field, particularly the application of AI in enhancing diagnostic precision and treatment strategies. The role of genomics in identifying molecular targets and pathways has enabled the development of targeted and personalized treatments, revolutionizing the management of urological disorders. We highlight novel pharmacological and non-pharmacological treatments that have shown efficacy in improving patient outcomes. Additionally, advancements in diagnostic methodologies, including non-invasive tests and precision imaging, have facilitated early and accurate disease detection. The review also addresses the emerging trends in biological therapies and the use of portable monitoring devices that support continuous care. By synthesizing current research, this article provides a comprehensive overview of the cutting-edge technologies and approaches that are setting the stage for future innovations in urology, aiming to enhance patient outcomes and optimize care delivery.

Renewable regeneration optic fiber glucose sensor based on succinylaminobenzenoboronic acid modified excessively tilted fiber grating

BackgroundOptical fiber sensors have been used to detect glucose owing to advantages such as low cost, small size, and ease of operation etc. phenylboronic acid is one of the commonly used receptors for glucose detection, however phenylboronic acid based regenerative optical fiber sensors are commonly cumulative regeneration, renewable regeneration sensor has been missing from the literature. ResultsIn this work, instead of using phenylboronic acid, we synthesized succinylaminobenzenoboronic acid molecule (BPOA) by introducing a short chain containing carboxyl group at the other end of phenylboronic acid then covalently bonded BPOA on the surface of excessively tilted fiber grating (Ex-TFG). This provides a very stable platform for renewable regeneration and the regenerative buffer was also optimized. The proposed renewable regeneration method exhibited higher linearity and sensitivity (R2 = 0.9992, 8 pm/mM) in relative to the conventional cumulative regeneration method (R2 = 0.9718, 4.9 pm/mM). The binding affinity between BPOA and glucose was found to be almost constant over 140 bind/release cycles with a variation of less than 0.3 % relative standard deviation. SignificanceThe regenerative and label-free sensing capacity of the proposed device provides a theoretical foundation for label-free saccharide detection and the development of wearable glucose monitoring devices based on fiber optic sensors.

Establishment of the rapid electrochemical-SELEX monitoring system by using ACEF technique and its application to dengue virus aptasensor fabrication

Aptamers, which consist of single-stranded DNA or RNA, possess considerable potential as biomolecular recognition elements. The systematic evolution of ligands by exponential enrichment (SELEX) serves as a general method for synthesizing aptamers. However, the conventional SELEX process for monitoring aptamer-target binding is time-consuming and labor-intensive, highlighting the need for rapid monitoring and selection methods. To address this problem, this study introduces a novel approach called rapid electrochemical-SELEX (RE-SELEX) monitoring method, which significantly reduces the target-aptamer binding time from 3 h to 10 min. This process is achieved by employing an alternating current electrothermal flow (ACEF) technique. The Au micro gap-type electrode was used to RE-SELEX monitoring device. As a proof-concept of this method, the dengue virus (DENV) aptamers were synthesized. For confirming the feasibility of this DENV aptamer, we analyzed target binding of produced aptamers test including gel electrophoresis, dissociation constant analysis. Then, two aptamers were selected for constructing the rapid electrochemical DENV aptasensor. The RE biosensor was achieved by ACEF technique that reduce the target-aptamer binding within 10 min. The fabricated device can detect 37 fM of the target dengue envelope protein in diluted human serum well. Thus, the proposed method provides a reliable SELEX-monitoring method for the rapid synthesis of aptamers using a simple electrochemical platform.

Research and design of multi‐stage multi‐load wireless power supply system for high voltage online monitoring equipment

AbstractMany online monitoring devices in substations are placed at high potential differences from the ground, and it is not possible to supply power to these devices directly from the ground through wires. Placing magnetically coupled structures inside the substation pillar insulators for inductive power transfer is considered a feasible solution, and at this stage of research, systems that add multi‐stage repeating coil structures are mostly used to achieve stable power supply for individual online monitoring devices. However, the monitoring point generally requires multiple devices to monitor together, and the power requirements of different devices are different, so adding multiple repeating coils at the same time will increase the complexity of the system design, which is not conducive to engineering applications. Therefore, this paper designs and optimizes a multi‐stage multi‐load wireless power supply system magnetic coupling structure, using a three‐dimensional hollow solenoid combined with a ferrite core as the system repeating coil structure and an embedded magnetic coupling structure as the receiver end structure to reduce the number of system repeating coils while supplying power to devices with different power requirements. A prototype with a transmission distance of 1.02 m was built in the experiment, and the experimental results showed that the highest transmission efficiency of the system was 44.3% and 39.2% under single load and double load conditions, respectively. As the load resistance increases, the system can achieve constant voltage output under both single‐load and dual‐load operating conditions. Adjusting the number of turns of the receiving coil can change the output characteristics of the system and achieve constant voltage output of different voltage levels or constant current output of different current levels, which can meet the power supply needs of different loads.