Non-invasive Monitoring System Research Articles

Deep Learning‐Coupled Metabolic Heat Integrated Sensing System for Noninvasive Continuous Monitoring of Blood Glucose

The dynamic and continuous monitoring of blood glucose (BG) concentration is crucial for the health management of diabetic patients. Despite its importance, significant challenges remain in the development of effective BG monitoring technologies. Metabolic heat conformation (MHC) offers a promising solution due to its noninvasiveness and reliability. However, progress in MHC technology is hindered by the complexities of multisensor integration and the intricate correlation between MHC and BG. Herein, a wearable BG continuous monitoring device based on metabolic heat integrated sensing (MHIS) is developed, combined with a deep learning network to enable continuous BG detection using MHC principles. The MHIS device integrates a miniaturized circuit and intelligent secondary signal processing, allowing for the simultaneous acquisition and integrated operation of various physiological parameters, including metabolic heat production. By integrating a gate recurrent unit neural network, a model is established to facilitate continuous BG monitoring. The wearable device has a certain accuracy, and when analyzed in comparison with commercial noninvasive glucose meters, the mean absolute relative error meets international standards. The deep learning‐enhanced MHIS system proposed in this work enables noninvasive BG monitoring, paving the way for advancements in personalized healthcare management and offering new opportunities in digital healthcare consultation.

Performance Evaluation of Deep Learning Architectures for Blood Pressure Estimation Using Photoplethysmography

High blood pressure (BP) monitoring Blood pressure (BP) is one of the common cardiovascular diseases and therefore the early high blood pressure (hypertension) detection, management, and prevention are mandatory. One promising method of continuous, non-invasive blood pressure estimation is photoplethysmography (PPG). In this study, a novel method was proposed to introduce the AlexNet framework into the time-frequency domain for classification of BP levels based on PPG signals. The study was conducted using the publicly available Figshare dataset which offers PPG signals, and the blood pressure labels against them. Data balancing techniques were used to alleviate class imbalances. Preprocessing and Feature Extraction of PPG Signals. The PPG signals were preprocessed with noise filtering and signals were then transformed from 1D-time to image to facilitate robust feature extraction. The proposed classification model, based on AlexNet showed the best result, with 98.89% accuracy, recall, and precision, and 99.44% specificity. This model outperformed alternative models (VGG16, DenseNet, ResNet50, GoogleNet) for classifying BP levels into the JNC 7 report standard categories normotension, prehypertension and hypertension. This study has two primary contributions. Initially, it demonstrates the efficacy of AlexNet model to extract meaningful features from PPG signals by its hierarchical convolutional and max-pooling layers thereby enabling accurate classification of BP levels. This study underscores the potential of deep learning and PPG signals for developing a highly accurate and truly non-invasive BP monitoring system. In the second aspect, the study offers a systematic assessment and comparison of the proposed over other well-known deep-learning networks, presenting the effectiveness of the AlexNet-based one. These results are of critical importance in the development of novel non-invasive BP monitoring modalities and optimization of cardiovascular health managements and personalized health cares.

Artificial Intelligence-Assisted Environmental DNA Metabarcoding and High-Resolution Underwater Optical Imaging for Noninvasive and Innovative Marine Environmental Monitoring

To effectively protect the marine environment, it is crucial to establish effective environ mental monitoring platforms. Traditional marine environmental monitoring methods heavily rely on morphological identification and field expertise, with the sampling process being disruptive and potentially destructive to vulnerable marine environments. In light of emerging biomonitoring needs and biodiversity declines, we reviewed the urgently needed, ongoing advances in developing effective, noninvasive, and innovative monitoring methods and systems to examine the complex marine environment for better strategic conservation and protection, using the coral ecosystem as one of the representative forefront examples in marine protection. This review summarizes current trends and efforts in transitioning into more standardizable and automatable utilizations of environmental DNA metabarcoding-based monitoring strategies and high-resolution underwater optical imaging monitoring systems as two of the promising pillars for the next generation of noninvasive biomonitoring and associated applications. The assistance of artificial intelligence for environmental DNA metabarcoding and high-resolution underwater optical imaging into an empowered, all-rounded monitoring platform for enhanced monitoring capacity is discussed as a highly potent direction for future research exploration. This review will be a cornerstone reference for the future development of artificial intelligence-assisted, noninvasive, and innovative marine environmental monitoring systems.

Completely noninvasive multi-analyte monitoring system for cell culture processes.

Although online monitoring of dissolved O2, pH, and dissolved CO2 is critical in bioprocesses, nearly all existing technologies require some level of direct contact with the cell culture environment, posing risks of contamination. This study addresses the need for an accurate, and completely noninvasive technique for simultaneous measurement of these analytes. A "non-contact" technique for simultaneous monitoring of dissolved O2, pH, and dissolved CO2 was developed. Instead of direct contact with the culture media, the measurements were made through permeable membranes via either a sampling port in the culture vessel wall or a flow cell. The efficacy of the "non-contact" technique was validated in Escherichia coli (E.coli), Chinese hamster ovary (CHO) culture processes, and dynamic environments created by sparging gases in cell culture medium. The measurements obtained through the developed techniques were comparable to those obtained through control methods. The noninvasive monitoring system can offer accurate, and contamination-minimized monitoring of critical process parameters including dissolved O2, pH, and dissolved CO2. These advancements will enhance the control and optimization of cell culture processes, promising improved cell culture performance.

Magnetic Field Mapping-Based Battery Management System (BMS) for Lead Acid Application

Lead Acid Batteries (LAB) are a widely used technology in Energy Storage Systems (ESS) due to their abundant and low-cost materials, non-flammable water-based electrolyte, and high recyclability rate. The main applications for LABs are in mobility, data centers, and telecommunications. According to the Consortium of Battery Innovation (CBI), the market for lead acid batteries is projected to increase 150 GWh between 2025 and 2030. Therefore, it is fundamental to identify emerging failure modes in LABs in ESS systems to reduce battery degradation and to extend the use life.An important failure mode in flooded LABs is acid stratification. This research explores H+ concentration changes in H2SO4 (sulfuric acid) electrolyte as a correlation of acid stratification. The objective is to develop a non-invasive, non-destructive, and accurate real-time battery monitoring system. The Magnetic Field Probing (MFP) technique, popular in the medical imaging field, shows a promising solution for ESS performance evaluation. This technique can measure induced magnetic field changes based on variation in H+ concentration in the cell during cycling. See Figure. 1 (a). MFP provides early onset notification of stratification in flooded LABs with capability to discern other failure modes in non-flooded lead batteries.A set of experimental trials measured pH and magnetic field variation of H2SO4 electrolyte. See Figure. 1 (a). The preliminary results demonstrated changes in electrolyte pH due to possible stratification. See Figure. 1 (b). A correlation between the output voltage resulting from the interaction of the induced magnetic field and the changes in the electrolyte concentration was found. See Figure 1(c) The optimal AC input frequency maximizes the induced magnetic field response. The value found for optimal AC input frequency was 32kHz, which depends on the physical properties of the magnetic field inductors (air core solenoid coils). The experimental results showed and concluded that the induced magnetic field across the tested cell is also inversely proportional to the distance between coils attached to the cell.Further research involves the use of magneto-resistors to establish a magnetic field mapping in lead acid cells as they are being cycled. Magnetic field response is measured while lead acid cells are cycled from 100% to 20% state of charge range. In the future, this study will provide insights and findings to model and predict cell stratification in lead acid cells with electrochemical computational simulations.Keywords: Energy storage systems, lead acid batteries, acid stratification, magnetic field, non-invasive method Figure 1

Altered Microvascular Reactivity During a Skin Thermal Challenge Is Associated With Organ Dysfunction and Slow Recovery After Cardiac Surgery

ObjectivesTo assess microvascular reactivity during a skin thermal challenge early post-cardiac surgery and its association with outcome. DesignNon-invasive physiological study. SettingThirty-five-bed department of intensive care. ParticipantsPatients admitted to the ICU post-cardiac surgery. InterventionsThermal challenge. Measurements and Main ResultsForty-six patients were included; 14 needed vasoactive support or ventilatory support for at least 48 hours (slow recovery) and 32 had a more rapid recovery. Skin blood flow (SBF) was measured on the anterior proximal forearm using skin laser Doppler (SLD). A thermal challenge was performed by abruptly increasing local skin temperature from 37°C to 43°C while monitoring SBF. The ratio between the SBFs at 43°C and 37°C was calculated as a measure of microvascular reactivity. SBF at 37°C was not significantly different in patients with a slow recovery and those with a rapid recovery, but SBF after 9 min at 43°C was lower [48.5 (17.3-69.0) vs 85.1 (45.2-125.7), p<0.01], resulting in a lower SBF ratio [2.8 (1.5-4.7) vs 4.8 (3.7-7.8), p<0.01]. Patients with lower SBF ratios were more likely to have dysfunction of at least 1 organ (as assessed using the sequential organ dysfunction [SOFA] score) 48 hours post-cardiac surgery than those with higher ratios: 88% vs 40% vs 27% (p<0.01), respectively, for the lowest, middle, and highest tertiles of SBF ratio. In multivariable analysis, lower SBF ratio was an independent risk factor for slow recovery. ConclusionsEarly alterations in microvascular reactivity as evaluated by a skin thermal challenge are correlated with organ dysfunction. These observations may help in the development of new, simple, non-invasive monitoring systems in postoperative patients.

Smart Contact Lens for Colorimetric Visualization of Glucose Levels in the Body Fluid.

Frequent blood glucose monitoring is a crucial routine for diabetic patients. Traditional invasive methods can cause discomfort and pain and even pose a risk of infection. As a result, researchers have been exploring noninvasive techniques. However, a limited number of products have been developed for the market due to their high cost. In this study, we developed a low-cost, highly accessible, and noninvasive contact lens-based glucose monitoring system. We functionalized the surface of the contact lens with boronic acid, which has a strong but reversible binding affinity to glucose. To achieve facile conjugation of boronic acid, we utilized a functional coating layer called poly(tannic acid). The functionalized contact lens binds to glucose in body fluids (e.g., tear) and releases it when soaked in an enzymatic cocktail, allowing for the glucose level to be quantified through a colorimetric assay. Importantly, the transparency and oxygen permeability of the contact lens, which are crucial for practical use, were maintained after functionalization, and the lenses showed high biocompatibility. Based on the analysis of colorimetric data generated by the smartphone application and ultraviolet-visible (UV-vis) spectra, we believe that this contact lens has a high potential to be used as a smart diagnostic tool for monitoring and managing blood glucose levels.

Design and Development of a Non-invasive Opto-Electronic Sensor for Blood Glucose Monitoring Using a Visible Light Source.

Background The management of diabetes is critically dependent on the continuous monitoring of blood glucose levels. Contemporary approaches primarily utilize invasive methods, which often prove to be uncomfortable and can deter patient adherence. There is a pressing need for the development of novel strategies that improve patient compliance and simplify the process of glucose monitoring. Aim and objectives The primary objective of this research is to develop a non-invasive blood glucose monitoring system (NIBGMS) that offers a convenient alternative to conventional invasive methods. This study aims to demonstrate the feasibility and accuracy of using visible laser light at a wavelength of 650 nm for glucose monitoring and to address physiological and technical challenges associated with in vivo measurements. Methods Our approach involved the design of a device that exploits the quantitative relationship between glucose concentration and the refraction phenomena of laser light. The system was initially calibrated and tested using glucose solutions across a range of concentrations (25-500 mg/dL). To get around the problems that come up when people's skin and bodies are different, we combined an infrared (IR) transmitter (800 nm) and receiver that checks for changes in voltage, which are indicative of glucose levels. Results The prototype device was compared with a commercially available blood glucose monitor (Accu-Chek active machine; Roche Diabetes Care, Inc., Mumbai, India). The results demonstrated an average linearity of 95.7% relative to the Accu-Chek machine, indicating a high level of accuracy in the non-invasive measurement of glucose levels. Conclusions The findings suggest that our NIBGMS holds significant promise for clinical application. It reduces the discomfort associated with blood sampling and provides reliable measurements that are comparable to those of existing invasive methods. The successful development of this device paves the way for further commercial translation and could significantly improve the quality of life for individuals with diabetes, by facilitating easier and more frequent monitoring.

Using Artificial Intelligence to Improve the Accuracy of a Wrist-Worn, Noninvasive Glucose Monitor: A Pilot Study.

Self-monitoring of glucose is important to the successful management of diabetes; however, existing monitoring methods require a degree of invasive measurement which can be unpleasant for users. This study investigates the accuracy of a noninvasive glucose monitoring system that analyses spectral variations in microwave signals. An open-label, pilot design study was conducted with four cohorts (N = 5/cohort). In each session, a dial-resonating sensor (DRS) attached to the wrist automatically collected data every 60 seconds, with a novel artificial intelligence (AI) model converting signal resonance output to a glucose prediction. Plasma glucose was measured in venous blood samples every 5 minutes for Cohorts 1 to 3 and every 10 minutes for Cohort 4. Accuracy was evaluated by calculating the mean absolute relative difference (MARD) between the DRS and plasma glucose values. Accurate plasma glucose predictions were obtained across all four cohorts using a random sampling procedure applied to the full four-cohort data set, with an average MARD of 10.3%. A statistical analysis demonstrates the quality of these predictions, with a surveillance error grid (SEG) plot indicating no data pairs falling into the high-risk zones. These findings show that MARD values approaching accuracies comparable to current commercial alternatives can be obtained from a multiparticipant pilot study with the application of AI. Microwave biosensors and AI models show promise for improving the accuracy and convenience of glucose monitoring systems for people with diabetes.

Non-invasive Blood Glucose Monitoring System

The project presents a novel approach to blood glucose monitoring, offering a non-invasive and pain-free method for individuals with diabetes to track their glucose levels. The system utilizes advanced sensor technology coupled with signal processing techniques to detect glucose levels in the body without the need for traditional needle pricking. The key components of the system include a power supply, voltage regulator, ESP32 microcontroller, LCD display, photo sensor, amplifier circuit, and linearization circuit, all seamlessly integrated to provide accurate and real-time glucose readings. This breakthrough technology holds great promise in revolutionizing diabetes management, enhancing the quality of life for millions of affected individuals.By placing the sensor on the individual’s hand, the Glucometer system employs a combination of photonic and electronic principles to non-invasively measure glucose levels. The photo sensor captures specific wavelengths of light that interact with glucose molecules within the tissue, generating a signal proportional to the glucose concentration. This signal is then amplified and processed through the integrated circuits, ensuring accurate linearization and calibration. The ESP32 microcontroller orchestrates these operations, facilitating seamless communication with the LCD display to present the glucose reading in a clear and user-friendly format.The Glucometer’s innovative design eliminates the discomfort associated with traditional blood sampling methods, making it a highly accessible and convenient tool for diabetes management. The system’s efficiency in providing accurate glucose readings without the need for invasive procedures represents a significant advancement in healthcare technology. With further development and refinement, the Glucometer holds the potential to significantly enhance the daily lives of individuals living with diabetes, offering a reliable and painless means of monitoring blood glucose levels.

Non-Invasive Hemodynamic Monitoring System Integrating Spectrometry, Photoplethysmography, and Arterial Pressure Measurement Capabilities.

Minimally invasive and non-invasive hemodynamic monitoring technologies have recently gained more attention, driven by technological advances and the inherent risk of complications in invasive techniques. In this article, an experimental non-invasive system is presented that effectively combines the capabilities of spectrometry, photoplethysmography (PPG), and arterial pressure measurement. Both time- and wavelength-resolved optical signals from the fingertip are measured under external pressure, which gradually increased above the level of systolic blood pressure. The optical channels measured at 434-731 nm divided into three groups separated by a group of channels with wavelengths approximately between 590 and 630 nm. This group of channels, labeled transition band, is characterized by abrupt changes resulting from a decrease in the absorption coefficient of whole blood. External pressure levels of maximum pulsation showed that shorter wavelengths (<590 nm) probe superficial low-pressure blood vessels, whereas longer wavelengths (>630 nm) probe high-pressure arteries. The results on perfusion indices and DC component level changes showed clear differences between the optical channels, further highlighting the importance of wavelength selection in optical hemodynamic monitoring systems. Altogether, the results demonstrated that the integrated system presented has the potential to extract new hemodynamic information simultaneously from macrocirculation tomicrocirculation.

NON-INVASIVE BLOOD GLUCOSE AND THYROID MONITORING SYSTEM

Frequent blood tests have historically been necessary to monitor thyroid function and blood glucose levels, which can be painful and inconvenient for patients. In this work, we provide a unique non-invasive blood glucose and thyroid monitoring device. With the use of cutting-edge sensor technology, our system can precisely measure thyroid and glucose via the skin without the need for blood draws or needles. This method provides a simple and easy substitute for conventional techniques, which may increase patient adherence to monitoring schedules. This non-invasive monitoring system's creation marks a substantial leap forward in medical science and could improve the way thyroid and diabetic diseases are managed. Additional investigation and clinical studies are necessary to confirm the efficacy and dependability of this novel technology in real world. Key Words: Non-invasive, Blood glucose, Thyroid, Monitoring system, Sensor technology, Healthcare innovation

Non-invasive glucometer monitoring system through optical based near-infrared sensor method

ABSTRACT Diabetes is a fast-developing medical issue that causes most renal and cardiac illnesses. Thus, diabetes management requires regular glucose monitoring. One potential technology is non-invasive glucometer monitoring. This work aims to develop a user-friendly near-infrared sensor-based non-invasive glucose monitoring system, correlating sensor output voltage variations with glucose levels, to provide accurate and convenient glucose monitoring for diabetes management. The objective is to validate the system’s accuracy against existing fingerpick methods and analyze its performance across different age groups and food intake conditions through experimental testing and Clarke grid analysis. In our research, we propose a near-infrared sensor-based non-invasive-type glucose monitoring technique which is a user-friendly system. The experimental setup and prototype system are designed and implemented for measuring the variation of glucose level with respect to a sensor output voltage. Using Beer Lambert’s law, the established results correlated the absorbance property of light with the sample concentration level. Demonstration of testing for different aged people was done under various food intake conditions. The obtained results are tabulated and validated with the existing fingerpick method and achieved an accuracy of 97.8%. Also, Clarke grid analysis has been done and depicted the pattern obtained.

Cost-Effective Non-Invasive Blood Glucose Monitoring System with Mobile Application for Management of Diabetic Patients

Cost-Effective Non-Invasive Blood Glucose Monitoring System with Mobile Application for Management of Diabetic Patients

A non-invasive smart healthcare monitoring system based on the Internet of things

&lt;p&gt;A worldwide increase in healthcare problems and disorders underscored the significance of across the globe healthcare monitoring (HCM). Several countries have increased lifespans due to technological medical developments, government health initiatives, and personal cleanliness. Globalization has led to a gradually rising elderly population and a decrease in fertility, which might result in problems with socioeconomic status. In order to assist older adults, HCM technologies must be cost-effective and simple to implement. Autonomous HCM could be feasible, incorporating wearable devices with sensors, actuators, and communication. The above approach enables practical and efficient personal medical care for older people, minimizing their requirement for costly hospital treatment. The entire study emphasizes the non-invasive blood glucose monitoring (NIBGM). The idea for the project includes developing a non-invasive medical device with RFID tags login, data storage, and smart sensors that allow the monitoring of several organs. The small in size, interconnected sensor monitors body temperature (BT), blood glucose level (BGL), blood pressure (BP), heart rate (HR), and oxygen saturation (OS). The internet-based monitoring device permits healthcare providers to keep track of patient’s health metrics in real-time and send data to remote places. The new method allows healthcare professionals to present rapid assistance and reinforcement, enhancing the results for patients.&lt;/p&gt;

Multisensory system for non-invasive monitoring and measuring of laboratory animal welfare

This paper presents a novel, cost-effective multisensory system designed for animal monitoring in research settings. The system aims to objectively assess animal welfare and discomfort during experiments, addressing the need for affordable monitoring solutions in research laboratories. Developed and validated in compliance with European regulations on animal experimentation and in accordance with the requirements of the Animal Research Centre at the University of Alcala´ in Madrid. The system integrates Raspberry Pi 4 Model B and Arduino Uno boards with various sensors, including temperature, humidity, ammonia, airborne particles, and an RGB camera. A user-friendly web interface allows remote monitoring and management of the system. This innovation promises to improve the efficiency and feasibility of animal research, enabling more precise and ethical experimentation while advancing scientific knowledge and animal welfare.

A Wireless Potentiostat Exploiting PWM-DAC for Interfacing of Wearable Electrochemical Biosensors in Non-Invasive Monitoring of Glucose Level

In this paper, a wireless potentiostat code-named ElectroSense, for interfacing of wearable electrochemical biosensors, will be presented. The system is devoted to non-invasive monitoring of glucose in wearable medical applications. Differently from other potentiostats in literature, which use digital-to-analog converters (DACs) as discrete components or integrated in high-end microcontrollers, in this work the pulse width modulation (PWM) technique is exploited through PWM-DAC approach to generate signals. The ubiquitous presence of integrated PWM peripherals in low-end microcontrollers, which generally also integrate analog-to-digital converters (ADCs), enables both the generation and acquisition of read-out signals on a single cheap electronic device without additional hardware. By this way, system’s production costs, power consumption, and system’s size are greatly reduced with respect to other solutions. All these features allow the system’s adoption in wearable healthcare Internet-of-things (IoT) ecosystems. A description of both the sensing technology and the circuit will be discussed in detail, emphasizing advantages and drawbacks of the PWM-DAC approach. Experimental measurements will prove the efficacy of the proposed electronic system for non-invasive monitoring of glucose in wearable medical applications.

Internet of Things enabled open source assisted real-time blood glucose monitoring framework

Regular monitoring of blood glucose levels is essential for the management of diabetes and the development of appropriate treatment protocols. The conventional blood glucose (BG) testing have an intrusive technique to prick the finger and it can be uncomfortable when it is a regular practice. Intrusive procedures, such as fingerstick testing has negatively influencing patient adherence. Diabetic patients now have an exceptional improvement in their quality of life with the development of cutting-edge sensors and healthcare technologies. intensive care unit (ICU) and pregnant women also have facing challenges including hyperglycemia and hypoglycemia. The worldwide diabetic rate has incited to develop a wearable and accurate non-invasive blood glucose monitoring system. This research developed an Internet of Things (IoT) - enabled wearable blood glucose monitoring (iGM) system to transform diabetes care and enhance the quality of life. The TTGOT-ESP32 IoT platform with a red and near-infrared (R-NIR) spectral range for blood glucose measurement has integrated into this wearable device. The primary objective of this gadget is to provide optimal comfort for the patients while delivering a smooth monitoring experience. The iGM gadget is 98.82 % accuracy when used after 10 hours of fasting and 98.04 % accuracy after 2 hours of breakfast. The primary objective points of the research were continuous monitoring, decreased risk of infection, and improved quality of life. This research contributes to the evolving field of IoT-based healthcare solutions by streaming real-time glucose values on AWS IoT Core to empower individuals with diabetes to manage their conditions effectively. The iGM Framework has a promising future with the potential to transform diabetes management and healthcare delivery.

Advancing spinal cord injury care through non-invasive autonomic dysreflexia detection with AI

This paper presents an AI-powered solution for detecting and monitoring Autonomic Dysreflexia (AD) in individuals with spinal cord injuries. Current AD detection methods are limited, lacking non-invasive monitoring systems. We propose a model that combines skin nerve activity (SKNA) signals with a deep neural network (DNN) architecture to overcome this limitation. The DNN is trained on a meticulously curated dataset obtained through controlled colorectal distension, inducing AD events in rats with spinal cord surgery above the T6 level. The proposed system achieves an impressive average classification accuracy of 93.9% ± 2.5%, ensuring accurate AD identification with high precision (95.2% ± 2.1%). It demonstrates a balanced performance with an average F1 score of 94.4% ± 1.8%, indicating a harmonious balance between precision and recall. Additionally, the system exhibits a low average false-negative rate of 4.8% ± 1.6%, minimizing the misclassification of non-AD cases. The robustness and generalizability of the system are validated on unseen data, maintaining high accuracy, F1 score, and a low false-negative rate. This AI-powered solution represents a significant advancement in non-invasive, real-time AD monitoring, with the potential to improve patient outcomes and enhance AD management in individuals with spinal cord injuries. This research contributes a promising solution to the critical healthcare challenge of AD detection and monitoring.

Application of the Bair Hugger™ core body temperature at wrist region with upper body warming blanket: a prospective observational study.

Body temperature monitoring is essential during the perioperative period. However, core body temperature measurement requires invasive device that may cause complications. This study aimed to evaluate the accuracy of non-invasive Bair Hugger™ core body temperature monitoring system (BHTMS) at the wrist compared with esophageal temperature under general anesthesia. Twenty adult patients of the American Society of Anesthesiologists physical status I or II were enrolled. BHTMS sensor was applied at wrist region. After tracheal intubation, an esophageal probe was inserted. Bair Hugger™ upper body warming blankets were used. Esophageal temperature (Teso) and BHTMS at wrist (Twrist) were recorded every 10 min. Total of 257 pairs of data sets were analyzed: Teso and Twrist had no statistically significant difference (P = 0.103). Median of Teso and Twrist were 36.5°C and 36.4°C. Bland-Altman analysis showed Teso - Twrist of 0.14°C ± 1.44. Subsequently, 99 pairs of 0-40 min data set were analyzed and showed significant difference at 0 and 10 min (P < 0.001) but no significant difference at 20, 30 and 40 min. Bland- Altman plot by times showed difference (Teso - Twrist) of 1.49°C ± 2.00, 0.82°C ± 1.30, 0.29°C ± 1.32, -0.03°C ± 0.84, and -0.12°C ± 0.82 at 0, 10, 20, 30 and 40 min respectively. BHTMS at wrist area under the upper body warming blanket is a potential alternative other than esophageal temperature for monitoring body temperature after 30 min of anesthesia induction.