scholarly journals Estimation of Human Internal Temperature from Wearable Physiological Sensors

2021 ◽  
Vol 24 (2) ◽  
pp. 1763-1768
Author(s):  
Mark Buller ◽  
William Tharion ◽  
Reed Hoyt ◽  
Odest Jenkins
Keyword(s):  
Thermal State ◽  
Core Body Temperature ◽  
Wearable Sensors ◽  
Military Training ◽  
Model Performance ◽  
Field Studies ◽  
Internal Temperature ◽  
Physically Active ◽  
Ambient Temperatures ◽  
Non Invasive

We evaluated a Kalman filter (KF) approach to modeling the physiology of internal temperature viewed through “noisy” non-invasive observations of heart rate. Human core body temperature (Tcore) is an important measure of thermal state, e.g., hypo- or hyperthermia, but is difficult to measure using non-invasive wearable sensors. We estimated parameters for a discrete KF model from data collected during several Military training events and from distance runners (n=38). Model performance was evaluated in 25 physically-active subjects who participated in various laboratory and field studies involving exercise of 2-to-8 h duration at ambient temperatures of 20 to 40°C. Overall, the KF model’s estimate of Tcore had a root mean square error of 0.30±0.13 ºC from the observed Tcore, and was within ± 0.5 ºC over 85% of the time. The benefit of the KF approach is that it requires only one input while current state of the art models typically require multiple inputs including individual anthropometrics, metabolic rate, clothing characteristics, and environmental conditions. This state estimation problem in computational physiology illustrates the potential for collaboration between the artificial intelligence and ambulatory physiological monitoring communities.

scholarly journals Recent Advances in Wearable Devices for Non-Invasive Sensing

2021 ◽  
Vol 11 (3) ◽  
pp. 1235
Author(s):  
Su Min Yun ◽  
Moohyun Kim ◽  
Yong Won Kwon ◽  
Hyobeom Kim ◽  
Mi Jung Kim ◽  
...  
Keyword(s):  
Health Monitoring ◽  
Data Transmission ◽  
Wearable Sensors ◽  
Wearable Devices ◽  
Wireless Power ◽  
Non Invasive ◽  
Sensing Systems ◽  
Recent Advances ◽  
Wearable Systems ◽  
Precise Diagnosis

The development of wearable sensors is aimed at enabling continuous real-time health monitoring, which leads to timely and precise diagnosis anytime and anywhere. Unlike conventional wearable sensors that are somewhat bulky, rigid, and planar, research for next-generation wearable sensors has been focused on establishing fully-wearable systems. To attain such excellent wearability while providing accurate and reliable measurements, fabrication strategies should include (1) proper choices of materials and structural designs, (2) constructing efficient wireless power and data transmission systems, and (3) developing highly-integrated sensing systems. Herein, we discuss recent advances in wearable devices for non-invasive sensing, with focuses on materials design, nano/microfabrication, sensors, wireless technologies, and the integration of those.

scholarly journals Diabetes classification application with efficient missing and outliers data handling algorithms

2021 ◽  
Author(s):  
Hanaa Torkey ◽  
Elhossiny Ibrahim ◽  
EZZ El-Din Hemdan ◽  
Ayman El-Sayed ◽  
Marwa A. Shouman
Keyword(s):  
Deep Learning ◽  
Healthcare System ◽  
Wearable Sensors ◽  
Model Performance ◽  
Analysis Model ◽  
Human Errors ◽  
Web Based ◽  
Machine Learning Model ◽  
Complete Dataset ◽  
Smart Wearable

AbstractCommunication between sensors spread everywhere in healthcare systems may cause some missing in the transferred features. Repairing the data problems of sensing devices by artificial intelligence technologies have facilitated the Medical Internet of Things (MIoT) and its emerging applications in Healthcare. MIoT has great potential to affect the patient's life. Data collected from smart wearable devices size dramatically increases with data collected from millions of patients who are suffering from diseases such as diabetes. However, sensors or human errors lead to missing some values of the data. The major challenge of this problem is how to predict this value to maintain the data analysis model performance within a good range. In this paper, a complete healthcare system for diabetics has been used, as well as two new algorithms are developed to handle the crucial problem of missed data from MIoT wearable sensors. The proposed work is based on the integration of Random Forest, mean, class' mean, interquartile range (IQR), and Deep Learning to produce a clean and complete dataset. Which can enhance any machine learning model performance. Moreover, the outliers repair technique is proposed based on dataset class detection, then repair it by Deep Learning (DL). The final model accuracy with the two steps of imputation and outliers repair is 97.41% and 99.71% Area Under Curve (AUC). The used healthcare system is a web-based diabetes classification application using flask to be used in hospitals and healthcare centers for the patient diagnosed with an effective fashion.

Cardio-respiratory and biochemical responses of military training event at Jungle environment

2021 ◽  
Vol 12 (4) ◽  
pp. 15-23
Author(s):  
Angad Yadav ◽  
Tirthankar Chatterjee ◽  
Debojyoti Bhattacharyya ◽  
Somnath Singh ◽  
Madhusudan Pal
Keyword(s):  
Core Body Temperature ◽  
Statistical Significance ◽  
Military Training ◽  
Venous Blood ◽  
Post Exercise ◽  
Biochemical Responses ◽  
Significant Difference ◽  
Training Event ◽  
Training Activities ◽  
Exercise Induced

Background: In military environment, soldiers regularly practice or undergo different types of extreme training activities. However, globally the literatures available on the physiological and biochemical demand of different extreme military training activities are very scanty and less reported. Aims and Objective: The present study was undertaken to quantify the cardio-respiratory and biochemical responses of military training event in jungle environment. Materials and Methods: Mathew’s Mad Mile (MMM) activity is a type of specialized run of 1.5 mile in jungle environment. This training activity was conducted on rugged jungle terrain comprised of undulated uphill, downhill, muddy surface. Twenty-five SHAPE-1 healthy soldiers were volunteered into training event. Cardiorespiratory data was recorded continuously throughout the event and venous blood sample was drawn before and immediately after completion of the event. Statistical significance was considered at p<0.05. Results: There was no significant difference observed in heart rate and breathing rate while core body temperature was significantly (p = 0.02) higher in slow finisher as compared to fast finisher. In fast finisher, post exercise level of BDNF, BNP, SDH, cortisol and UCP1 increased significantly (p<0.05), whereas, BHB (p<0.01) decreased significantly in comparison to pre-exercise. In slow finishers, post exercise level of cortisol, KYNA and UCP1 increased significantly (p<0.05), whereas, BDNF, BNP and SDH decreased significantly (p<0.05), in comparison to pre-exercise. Conclusion: The outcome of this study indicated that the slow finishers were more susceptible to risk of injury due to higher exercise induced thermogenesis and mental stress in comparison to fast finisher.

scholarly journals Wearable sensors: modalities, challenges, and prospects

Lab on a Chip ◽  
2018 ◽  
Vol 18 (2) ◽  
pp. 217-248 ◽  
Author(s):  
J. Heikenfeld ◽  
A. Jajack ◽  
J. Rogers ◽  
P. Gutruf ◽  
L. Tian ◽  
...  
Keyword(s):  
Human Body ◽  
Wearable Sensors ◽  
Chemical Information ◽  
Non Invasive ◽  
Wearable Sensing ◽  
Sensing Technology

Non-invasive wearable sensing technology extracts mechanical, electrical, optical, and chemical information from the human body.

scholarly journals Non-Invasive Assessment of Mild Stress-Induced Hyperthermia by Infrared Thermography in Laboratory Mice

Animals ◽  
2022 ◽  
Vol 12 (2) ◽  
pp. 177
Author(s):  
Urša Blenkuš ◽  
Ana Filipa Gerós ◽  
Cristiana Carpinteiro ◽  
Paulo de Castro Aguiar ◽  
I. Anna S. Olsson ◽  
...  
Keyword(s):  
Infrared Thermography ◽  
Core Body Temperature ◽  
Mild Stress ◽  
Laboratory Mice ◽  
Induced Hyperthermia ◽  
Non Invasive ◽  
Body Regions ◽  
Plus Maze ◽  
Core Body ◽  
Typical Temperature

Stress-induced hyperthermia (SIH) is a physiological response to acute stressors in mammals, shown as an increase in core body temperature, with redirection of blood flow from the periphery to vital organs. Typical temperature assessment methods for rodents are invasive and can themselves elicit SIH, affecting the readout. Infrared thermography (IRT) is a promising non-invasive alternative, if shown to accurately identify and quantify SIH. We used in-house developed software ThermoLabAnimal 2.0 to automatically detect and segment different body regions, to assess mean body (Tbody) and mean tail (Ttail) surface temperatures by IRT, along with temperature (Tsc) assessed by reading of subcutaneously implanted PIT-tags, during handling-induced stress of pair-housed C57BL/6J and BALB/cByJ mice of both sexes (N = 68). SIH was assessed during 10 days of daily handling (DH) performed twice per day, weekly voluntary interaction tests (VIT) and an elevated plus maze (EPM) at the end. To assess the discrimination value of IRT, we compared SIH between tail-picked and tunnel-handled animals, and between mice receiving an anxiolytic drug or vehicle prior to the EPM. During a 30 to 60 second stress exposure, Tsc and Tbody increased significantly (p < 0.001), while Ttail (p < 0.01) decreased. We did not find handling-related differences. Within each cage, mice tested last consistently showed significantly higher (p < 0.001) Tsc and Tbody and lower (p < 0.001) Ttail than mice tested first, possibly due to higher anticipatory stress in the latter. Diazepam-treated mice showed lower Tbody and Tsc, consistent with reduced anxiety. In conclusion, our results suggest that IRT can identify and quantify stress in mice, either as a stand-alone parameter or complementary to other methods.

scholarly journals Solid-Contact Ion-Selective Electrodes: Response Mechanisms, Transducer Materials and Wearable Sensors

Membranes ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 128 ◽  
Author(s):  
Yan Lyu ◽  
Shiyu Gan ◽  
Yu Bao ◽  
Lijie Zhong ◽  
Jianan Xu ◽  
...  
Keyword(s):  
Biological Fluids ◽  
Wearable Sensors ◽  
Ion Selective Electrodes ◽  
Double Layer Capacitance ◽  
Solid Contact ◽  
General Picture ◽  
Non Invasive ◽  
Potential Stability ◽  
Electric Double Layer Capacitance ◽  
Redox Capacitance

Wearable sensors based on solid-contact ion-selective electrodes (SC-ISEs) are currently attracting intensive attention in monitoring human health conditions through real-time and non-invasive analysis of ions in biological fluids. SC-ISEs have gone through a revolution with improvements in potential stability and reproducibility. The introduction of new transducing materials, the understanding of theoretical potentiometric responses, and wearable applications greatly facilitate SC-ISEs. We review recent advances in SC-ISEs including the response mechanism (redox capacitance and electric-double-layer capacitance mechanisms) and crucial solid transducer materials (conducting polymers, carbon and other nanomaterials) and applications in wearable sensors. At the end of the review we illustrate the existing challenges and prospects for future SC-ISEs. We expect this review to provide readers with a general picture of SC-ISEs and appeal to further establishing protocols for evaluating SC-ISEs and accelerating commercial wearable sensors for clinical diagnosis and family practice.

scholarly journals Study on the Method of Calculating Temperature Rise of Transformer

2020 ◽  
Vol 179 ◽  
pp. 01027
Author(s):  
Tao Li ◽  
Xiaoping Du ◽  
Xuewu Sun ◽  
Yuanyuan Song
Keyword(s):  
Temperature Rise ◽  
Thermal State ◽  
Hot Spot ◽  
Scientific Basis ◽  
Internal Temperature ◽  
Insulation Aging ◽  
The Neural Network ◽  
Forecasting Method ◽  
Transformer Winding ◽  
Spot Temperature

The internal temperature of the transformer is a key parameter to measure the thermal state of the transformer. The service life of the transformer generally depends on the life of the insulating material, and high temperature is the main reason why cause insulation aging, this paper studies the temperature rise of transformer winding hot spot temperature for the key, using the neural network forecasting method, forecasts transformer winding hot spot temperature change rule, calculate the transformer internal temperature rise, provide the temperature of the scientific basis for the safe operation of the transformer.

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