Human Physiological Parameters Research Articles

A Review on the Role and Performance of Cellulose Nanomaterials in Sensors.

Sensors and biosensors play a key role as an analytical tool for the rapid, reliable, and early diagnosis of human diseases. Such devices can also be employed for monitoring environmental pollutants in air and water in an expedited way. More recently, nanomaterials have been proposed as an alternative in sensor fabrication to achieve gains in performance in terms of sensitivity, selectivity, and portability. In this direction, the use of cellulose nanomaterials (CNM), such as cellulose nanofibrils (CNF), cellulose nanocrystals (CNC), and bacterial cellulose (BC), has experienced rapid growth in the fabrication of varied types of sensors. The advantageous properties are related to the supramolecular structures that form the distinct CNM, their biocompatibility, and highly reactive functional groups that enable surface functionalization. The CNM can be applied as hydrogels and xerogels, thin films, nanopapers and other structures interesting for sensor design. Besides, CNM can be combined with other materials (e.g., nanoparticles, enzymes, carbon nanomaterials, etc.) and varied substrates to advanced sensors and biosensors fabrication. This review explores recent advances on CNM and composites applied in the fabrication of optical, electrical, electrochemical, and piezoelectric sensors for detecting analytes ranging from environmental pollutants to human physiological parameters. Emphasis is given to how cellulose nanomaterials can contribute to enhance the performance of varied sensors as well as expand novel sensing applications, which could not be easily achieved using standard materials. Finally, challenges and future trends on the use of cellulose-based materials in sensors and biosensors are also discussed.

Effects of urban street trees on human thermal comfort and physiological indices: a case study in Changchun city, China

Planting trees along urban streets is one of the most important strategies to improve the urban thermal environment. However, the net impacts of urban street trees on human thermal comfort and physiological parameters are still less clear. On three similar east–west orientated streets with different degrees of tree cover—low (13%), medium (35%), and high (75%), urban microclimatic parameters and human physiological indices for six male students were simultaneously measured on three cloudless days in summer 2018. The results show that the differences in tree cover were predominant in influencing urban thermal environment and comfort. The street with the highest tree cover had significantly lower physiological equivalent temperature (PET) and more comfortable than the other two streets. The frequency of strong heat stress (PET > 35 °C) was 64%, 11%, and 0%, respectively, for streets with low, medium, and high tree cover. For the six male university students, human physiological indices varied greatly across the three streets with different tree cover. Systolic blood pressure, diastolic blood pressure, and pulse rate increased with decreasing tree cover. The results also suggest that urban thermal environment and comfort had considerable impact on human physiological parameters. Our study provides reasons for urban planners to plant trees along streets to improve the thermal environment and promote urban sustainability.

Human risk assessment of di-isobutyl phthalate through the application of a developed physiologically based pharmacokinetic model of di-isobutyl phthalate and its major metabolite mono-isobutyl phthalate.

Di-isobutyl phthalate (DiBP) is a substance used in the production of objects frequently used in human life. Mono-isobutyl phthalate (MiBP), a major in vivo metabolite of DiBP, is a biomarker for DiBP exposure assessment. Therefore, risk assessment studies on DiBP and MiBP, which have not yet been reported in detail, are needed. The aim of this study was to develop and evaluate a physiologically based pharmacokinetic (PBPK) model for DiBP and MiBP in rats and extend this to human risk assessment based on human exposure. Pharmacokinetic studies were performed in male rats following the administration of 5-100mg/kg DiBP, and these results were used for the development and validation of the PBPK model. In addition, the previous pharmacokinetic results in female rats following DiBP administration and the pharmacokinetic results in both males and females according to multiple exposures to DiBP were used to develop and validate the PBPK model. The metabolism of DiBP to MiBP in the body was very significant and rapid, and the biodistribution of MiBP was broad and major. Furthermore, the amount of MiBP in the body showed a correlation with DiBP exposure, and from this, a PBPK model was developed to evaluate the external exposure of DiBP from the internal exposure of MiBP. The predicted rat plasma, urine, fecal, and tissue concentrations using the developed PBPK model fitted well with the observed values. The established PBPK model for rats was extrapolated to a human PBPK model of DiBP and MiBP based on human physiological parameters and allometric scaling. The reference dose of 0.512mg/kg/day of DiBP and external doses of 6.14-280.90μg/kg/day DiBP for human risk assessment were estimated using Korean biomonitoring values. Valuable insight and approaches to assessing human health risks associated with DiBP exposure were provided by this study.

Exercise Thermal Sensation: Physiological Response to Dynamic-Static Steps at Moderate Exercise.

The study of exercise thermal sensation is more difficult than that of static thermal sensation in the human body. This work’s main purpose was to examine specific changes in human physiological parameters and subjective perceptions during the exercise process, especially around dynamic–static steps, and to assess exercise thermal sensation. Experiments were conducted in a climate chamber. A total of 16 subjects participated in two activities of different intensities on a treadmill, namely at 4.5 km/h and 6 km/h. The experimental procedure was set to static–dynamic–static. Skin temperature (Tsk), oral temperature (Tor), heart rate (HR), heart rate variability (HRV) parameters, and electrodermal activity (EDA) were measured at fixed time points, and thermal sensation values, thermal comfort values, and sweat feeling index were collected. The results showed complex changes in physiological indicators around the dynamic–static steps. Some important physio-logical indicators can be used as valid parameters for exercise thermal sensation models, such as Tsk, Tor, and EDA. This study highlighted that prediction models using average change and rate of change of measurements were better than using the original measurements. Our findings suggest that the exercise thermal sensation prediction models should be constructed according to the dynamic–static state and that psychological factors cannot be ignored.

Evaluation on non-visual effects of lighting based on both physiological parameters and subjective ratings

This article explores the influence of different colors of lights on human physiological parameters and at the same time the effects on human emotions, and tries to explore the inner connection of the changes. It helps us to better understand the color of lighting and apply it in daily life or commercial purpose, Then the experience of visual sensation will be enhanced. This article examines the changes in physiological parameters and the subjective emotional ratings triggered by the six lighting conditions such as red, green, blue, warm white, cold white and darkness. From the experiment results, the changes of physiological parameters and subjective emotions can be observed under different lighting conditions, which can be applied to guide lighting design and projects.

Structure-rheology elucidation of human blood via SPP framework and TEVP modeling

Recent work modeling the rheological behavior of human blood indicates that it has all the hallmark features of a complex material, including shear-thinning, viscoelastic behavior, a yield stress, and thixotropy. After decades of modeling steady state blood data, and the development of simple steady state models, like the Casson and Herschel-Bulkley the advancement and evolution of blood modeling to incorporate more thixo-elasto-visco-plastic (TEVP) features to accurately capture transient flow has renewed interest. With recently collected steady state and oscillatory shear flow rheological data from a DHR-3 using human blood, we show modeling efforts with a contemporary thixo-elasto-visco-plastic (TEVP) model. Best fit rheological model parameters are used to determine values for normal, healthy blood and corroborate correlations from literature. Series of physical processes (SPP) analysis is incorporated to illustrate how mechanical properties are tied to the transient, evolving microstructure of human blood and physiological parameters. Using LAOS data predictions of the structure parameter, λ is compared, and correlated with the transient elastic modulus, G t ′ .

The early warning research on nursing care of stroke patients with intelligent wearable devices under COVID-19.

Stroke patients under the background of the new crown epidemic need to be home-based care. However, traditional nursing methods cannot take care of the patients’ lives in all aspects. Based on this, based on machine learning algorithms, our work combines regression models and SVM to build a smart wearable device system and builds a system prediction module to predict patient care needs. The node is used to collect human body motion and physiological parameter information and transmit data wirelessly. The software is used to quickly process and analyze the various motion and physiological parameters of the patient and save the analysis and processing structure in the database. By comparing the results of nursing intervention experiments, we can see that the smart wearable device designed in this paper has a certain effect in stroke care.

Retracted] Sports Injury Risk Assessment Based on Blockchain and Internet of Things

With the increase of people’s exercise in today’s society, how to exercise scientifically and healthily has attracted much attention. Therefore, sports injury risk assessment and monitoring system has attracted more and more attention in real‐time, flexibility, intelligence, and other aspects. To solve the above problems, this paper proposes a sports injury risk assessment based on blockchain and Internet of Things. By introducing computational power weight, a computational power balance D‐H algorithm based on Internet of Things blockchain network architecture is proposed. It can provide a secure and trusted interactive environment for the Internet of Things. On the basis of blockchain and Internet of Things, a multisensor data fusion algorithm is proposed to be applied to the analysis and evaluation of sports injury. A variety of physiological parameters of human motion state are collected through multisensor, the collected physiological parameters are processed by data fusion, and finally, sports injury risk assessment is carried out. The built system takes the embedded esp8266wifi module as the hardware processing core and uses body temperature sensor, blood pressure sensor, EMG sensor, and pulse sensor to form wearable devices. By wearing wearable devices, four human physiological parameters such as body temperature, blood pressure, electromyography, and pulse can be collected. In the process of decision level fusion, different weights are set for the focal elements causing information conflict, and the optimized D‐S evidence theory algorithm is used. Thus, according to the data detected by multisensor, the injury risk of user motion state is evaluated.

Некоторые эколого-медицинские проблемы Севера

Based on the literature and our own data, a biogeochemical characteristic of the regions of the North is given. The poor knowledge of the microelement status of the inhabitants in the northern areas is shown. Data on technogenic pollution of the North's biosphere by heavy metals are presented. Emphasis is placed on the accumulation in the biosphere of mercury, whith its a neurotoxic effect. Exemplified by mercury and selenium, the antagonistic relationships between chemical elements in a living organism are shown, which may result in the formation of secondary microelements. The reason for the violation of the human physiological and biochemical parameters in the current North conditions against the background of environmental pollution is a violation of the nutrition structure and quality of. It is concluded that, in order to preserve the health of migrants and indigenous people, it is necessary to conduct geochemical zoning of modern Arctic regions, to identify the гpredispositionд of certain territories to biogeochemical endemicity, as well as to take preventive measures for protecting the population against environmentally dependent diseases.

Development of a support vector machine learning and smart phone Internet of Things-based architecture for real-time sleep apnea diagnosis

BackgroundThe breathing disorder obstructive sleep apnea syndrome (OSAS) only occurs while asleep. While polysomnography (PSG) represents the premiere standard for diagnosing OSAS, it is quite costly, complicated to use, and carries a significant delay between testing and diagnosis.MethodsThis work describes a novel architecture and algorithm designed to efficiently diagnose OSAS via the use of smart phones. In our algorithm, features are extracted from the data, specifically blood oxygen saturation as represented by SpO2. These features are used by a support vector machine (SVM) based strategy to create a classification model. The resultant SVM classification model can then be employed to diagnose OSAS. To allow remote diagnosis, we have combined a simple monitoring system with our algorithm. The system allows physiological data to be obtained from a smart phone, the data to be uploaded to the cloud for processing, and finally population of a diagnostic report sent back to the smart phone in real-time.ResultsOur initial evaluation of this algorithm utilizing actual patient data finds its sensitivity, accuracy, and specificity to be 87.6%, 90.2%, and 94.1%, respectively.DiscussionOur architecture can monitor human physiological readings in real time and give early warning of abnormal physiological parameters. Moreover, after our evaluation, we find 5G technology offers higher bandwidth with lower delays ensuring more effective monitoring. In addition, we evaluate our algorithm utilizing real-world data; the proposed approach has high accuracy, sensitivity, and specific, demonstrating that our approach is very promising.ConclusionsExperimental results on the apnea data in University College Dublin (UCD) Database have proven the efficiency and effectiveness of our methodology. This work is a pilot project and still under development. There is no clinical validation and no support. In addition, the Internet of Things (IoT) architecture enables real-time monitoring of human physiological parameters, combined with diagnostic algorithms to provide early warning of abnormal data.

1D Nanomaterial‐Based Highly Stretchable Strain Sensors for Human Movement Monitoring and Human–Robotic Interactive Systems

AbstractThis paper describes a facile strategy of micromolding‐in‐capillary process to fabricate stretchable strain sensors wherein, the sensing material is wrapped within silicone rubber (Dragon Skin [DS]) to form a sandwich‐like structure. Two different 1D sensing materials are exploited to fabricate and study strain sensing performance of such device structure, namely multi‐walled carbon nanotubes (MWCNTs) and silver nanowires (AgNWs). The fabricated strain sensors using MWCNT exhibits wide sensing range (2–180%), and moderately high sensing performance with outstanding durability (over 6000 cycles). It is found that MWCNT presents a strong strain‐dependent character in the 45–120% elongation regime. On the other hand, very high gauge factor of >106 is achieved using AgNWs at 30% strain with good stability (over 100 cycles). Sensing mechanisms for both 1D conductive sensing materials are discussed. They can be applied for human motion monitoring such as finger, knee, and wrist bending movements to enable human physiological parameters to be registered and analyzed continuously. They are also employed in multichannel and interactive electronic system to be used as a control mechanism for teleoperation for robotic end‐effectors. The developed sensors have potential applications in health diagnosis and human–machine interaction.

RK-Energy Efficient Routing Protocol for Wireless Body Area Sensor Networks

Wireless Body Area Sensor Networks are related to the monitoring of human physiological parameters. In these small sized machines called sensors are used to observe the physiological parameters. They are small in size which makes them easy to carry around but on the same time they have a serious problem that they can carry with them a very small sized battery. The sensors deplete their energy while sensing the parameter, communication of the sensed data to the base station and also in processing of the observed data. The sensors cannot be charged on regular intervals because they are attached to human body and charging them may not be an easy option. In this paper an energy efficient routing protocol is presented which uses sensors in WBASN to observe parameter in much efficient way. The concept of multi hopping has been utilized with forwarder node. Forwarder node accepts data from sensor nodes which are far from the sink. After accepting data the forwarder node forwards this data to the sink node. This scheme is compared with an existing scheme with which it has been compared in terms of four parameters which are residual energy, network stability and life time, throughput and path loss.

Wearable bracelets with variable sampling frequency for measuring multiple physiological parameter of human

With the acceleration of modernization and the marked improvement in the quality of life, more and more people pay attention to their own health. Sports health has become the first choice for most people because of its natural and healthy way. Therefore, devices with step counting functions such as smart bracelets and smart phones came into being. This paper proposes a design method for measuring the human body’s multiple physiological parameters with multiple sampling frequencies. It collects three physiological parameters of blood oxygen saturation, exercise energy consumption and body temperature of a sports human body. At the same time, in order to improve the endurance of a healthy bracelet, a variable sampling frequency scheme is designed using the BP neural network algorithm, and all physical health information can be determined according to the heart rate frequency. STM32 combined with host computer is used to verify the design method. It can monitor real-time human physiological parameters and conduct a comprehensive assessment of human health recording changes in human health information. Through the system test and analysis of the experimental results, it is verified that the physiological data collected has higher accuracy based on improving the endurance.

A wearable device for health management detection of multiple physiological parameters based on ZigBee wireless networks

Abstract Detecting physiological signals in the daily life of the population can effectively grasp the health status of the people in the area, and provide an important reference for the diagnosis and prevention of chronic diseases. This paper demonstrates that ZigBee technology meets the requirements of this system. A wireless sensor network system of human physiological parameters based on Zig Bee protocol is built. This wearable device consists of smart hardware and wearables. Intelligent hardware consists of multiple physiological signal detection modules and core processing platforms, and is mainly responsible for the collection and signal processing of multiple physiological signals. Based on the system platform, the entire system was tested for joint debugging. The system's wireless transmission stability and power consumption were tested, and various physiological parameters and motion signals were collected and tested. The test results verify the effectiveness of this system.

Quantification of drudgery and ergonomics assessment of weeding activity in vegetable production system

Weeding activity in vegetable cultivation carried out by women is one of the drudgery prone activities. Women are regularly adopting poor and static posture while doing weeding for long hours by traditional equipment, resulting in various physical load on spine or lumbo-sacral region leads to drudgery coupled with work related musculoskeletal disorders (MSDs). The research paper aims to assess and quantify the drudgery with weeding activity by rural women in which physiological ergonomics evaluation was done for traditional method and with improved technological support with three replications of fifteen minutes work cycle without rest pause. Ergonomic parameters namely Heart Rate, Energy Expenditure Rate, Total Cardiac Cost of Work, Physiological Cost of Work, VO2 Max were measured during the experiments. Observations were recorded on farm women worker with normal health, without any major illness, regularly involved in farm operations and were in the age group of 25 to 45 years. Weeding activity of farm women was quantified and a sample of 10 physically fit rural women having permissible limit of physiological parameters were selected for the study. It was found from investigation that there was reduction in human physiological parameters, viz. heart rate, energy expenditure rate, Total Cardiac Cost of Work, Physiological Cost of work, Blood pressure and Oxygen Saturation.

Detection of Artifacts in Ambulatory Electrodermal Activity Data

Recent wearable devices enable continuous and unobtrusive monitoring of human's physiological parameters, like e.g., electrodermal activity and heart rate, over long periods of time in everyday life settings. Continuous monitoring of these parameters enables the creation of systems able to predict affective states and stress with the goal of providing feedback to improve them. Deployment of such systems in everyday life settings is still complex and prone to errors due to the low quality of the collected data impacted by the presence of artifacts. In this paper we present an automatic approach to detect artifacts in electrodermal activity (EDA) signals collected in-the-wild over long periods of time. To this end we first perform a systematic literature review and compile a set of guidelines for human annotators to label artifacts manually and we use these labels as ground-truth to test our automatic approach. To evaluate our approach, we collect physiological data from 13 participants in-the-wild and two human annotators label 107.56 hours of this data set. We make the data set publicly available to other researchers upon request. Our model achieves a recall of 98% for clean and shape artifacts classification on data collected in-the-wild using leave-one-subject-out cross-validation, which is 42 percentage points higher than the baseline. We show that state of the art approaches do not generalize well when tested with completely in-the-wild data and identify only 17% of the artifacts present in our data set, even after manual adaption. We further test the robustness of our approach over time using leave-one-day-out and achieve very similar performance. We then introduce a new metric to evaluate the quality of EDA segments that considers the impact of not only artifacts in the shape of EDA but also artifacts generated by environmental temperature changes or user's high intensity movement. Our results imply that we can eliminate the need for human annotators or significantly reduce the time they need to label data. Also, our approach can be used in an online manner to automatically detect artifacts in EDA signals.

Use of Wearable Technology to Measure Influence of Driving Stress on Heart Rate of Professional Drivers

Background: Developments in wearable gadgets provides opportunity to measure human physiological parameters withaccuracy and low cost.Objective: To determine the effect of driving stress on heart rate (HR) among professional drivers with a wrist-worn smartband. Cross-sectiona study conducted in . Department of Medicine B.V. Hospital Bahawalpur from November 2018 toDecember 2018.Material and Methods: Thirty professional minivan drivers were recruited, Heart Rate was measured during real-time drivingconditions with Xiaomi Mi Band 3; we also recorded HR of passengers during the journey for comparison.Results: Heart Rate of drivers remained significantly higher as compared to passengers at different times during journey. HRof drivers at 30 min of journeying was 98.8+ 19.4, (P = 0.0001) at 60 min of journeying was 110.8 + 16.7, (P = <0.0001) at 90min of journeying was 133.4 + 22.5, (P = <0.0001) at the end of journeying was 153.9 + 19.1.Conclusion: Driving is a notable stress factor affecting cardiovascular health.

Research progress on new materials and properties of electronic skin

Human skin is an extraordinary organ; it comprises an integrated, stretchable network of sensors that transmits information to the brain about tactile and thermal stimuli, enabling us to safely and efficiently operate in our environment. Researchers have become interested in large-scale electronic device networks inspired by human skin, motivated by the prospect of developing devices such as autonomous smart robots and bionic prostheses. Developing electronic networks consist of flexible, stretchable, and robust devices that are compliant with large-scale implementation and integrated with multiple functionalities is a testament to the progress in developing human-skin like electronic bodies. In the fields of human physiological parameter detection and robot tactile perception, electronic skin has been commonly used as a kind of flexible tactile biomimetic sensor. Conventional electronic skin tactile sensors based on metal and semiconductor materials do not meet the requirements for stretchability and portability during actual use because of poor flexibility and wearability. Attributed to the rapid development of flexible materials, and manufacturing and sensing technologies, new materials such as polydimethylsiloxane (PDMS), carbon nanotubes, and graphene have been used to prepare or support electronic skin sensors in recent years, thus enabling electronic skin to be more similar to human skin in terms of stretchability, compressibility, and spatial resolution of touch, and other properties. Now, multi-functional integrated electronic skin devices have realized interaction with smart devices to obtain further collection and processing of human body information. This study analyzed and discussed new electronic skin materials and sensing technologies used in electronic skin, including capacitive effects, piezoelectric effects, piezoresistive effects, optical effects, and wireless antenna sensing. We focused on the recent research progress in electronic skin in terms of stretch/compressibility, biocompatibility, biodegradability, self-power, self-healing, temperature sensitivity, and multi-functional integration. Moreover, we anticipate the future research directions of new electronic skin properties and possible ways to achieve large-area, low-cost, multi-function integrated electronic skin sensor arrays.

The impact of airborne pollution and exposure to solar ultraviolet radiation on skin: mechanistic and physiological insight.

For several decades air pollution has been recognized to hit drastically the skin of human body. Air pollutants predominantly accountable for aging, oxidative damage, and inflammatory allergic reactions led to psoriasis, dermatitis, acne, and skin cancer owing to the impaired functions of DNA, proteins, and lipid biomolecules. Elevated air pollution and its detrimental effects along with variations in physiological parameters of the skin are verily the scaffold for anti-pollution assertions and could be recognized as markers. The present article encompasses the salient features of air pollution and UV radiations besides dreadful effects on human skin physiological parameters and some anti-pollution approaches.

Risk assessment for humans using physiologically based pharmacokinetic model of diethyl phthalate and its major metabolite, monoethyl phthalate.

Diethyl phthalate (DEP) belongs to phthalates with short alkyl chains. It is a substance frequently used to make various products. Thus, humans are widely exposed to DEP from the surrounding environment such as food, soil, air, and water. As previously reported in many studies, DEP is an endocrine disruptor with reproductive toxicity. Monoethyl phthalate (MEP), a major metabolite of DEP in vivo, is a biomarker for DEP exposure assessment. It is also an endocrine disruptor with reproductive toxicity, similar to DEP. However, toxicokinetic studies on both MEP and DEP have not been reported in detail yet. Therefore, the objective of this study was to evaluate and develop physiologically based pharmacokinetic (PBPK) model for both DEP and MEP in rats and extend this to human risk assessment based on human exposure. This study was conducted in vivo after intravenous or oral administration of DEP into female (2mg/kg dose) and male (0.1-10mg/kg dose) rats. Biological samples consisted of urine, plasma, and 11 different tissues. These samples were analyzed using UPLC-ESI-MS/MS method. For DEP, the tissue to plasma partition coefficient was the highest in the kidney, followed by that in the liver. For MEP, the tissue to plasma partition coefficient was the highest in the liver. It was less than unity in all other tissues. Plasma, urine, and fecal samples were also obtained after IV administration of MEP (10mg/kg dose) to male rats. All results were reflected in a model developed in this study, including in vivo conversion from DEP to MEP. Predicted concentrations of DEP and MEP in rat urine, plasma, and tissue samples using the developed PBPK model fitted well with observed values. We then extrapolated the PBPK model in rats to a human PBPK model of DEP and MEP based on human physiological parameters. Reference dose of 0.63mg/kg/day (or 0.18mg/kg/day) for DEP and external doses of 0.246μg/kg/day (pregnant), 0.193μg/kg/day (fetus), 1.005-1.253μg/kg/day (adults), 0.356-0.376μg/kg/day (adolescents), and 0.595-0.603μg/kg/day (children) for DEP for human risk assessment were estimated using Korean biomonitoring values. Our study provides valuable insight into human health risk assessment regarding DEP exposure.