This work was supported by the project "Electromagnetic Characterization in Smart Environments of Healthcare, and their involvement in Personal, Occupational, and Environmental Health," (DGPY-1285/15, PI14CIII/00056), and with the human resources of the project "Network Platform for the Development of Telemedicine in Spain" (DGPY-1301/08-1-TS-3), both funding from Sub-Directorate-General for Research Assessment and Promotion (Carlos III Health Institute).

The proposed protocol follows the guidelines of Carlos III Health Institute&#180;s human research ethics committee.
1. Enclosure Selection and Control Test of Electromagnetic Environment
<ol>
	<li>Select a spacious enclosure, at least 20 m3 in volume, that is large enough that signal fading is noticeable in the PEM logged data. Preferably, the enclosure should be empty, although this is not absolutely necessary as small obstacles, such as furniture, are not taken into account in the propagation models that are used to predict the E-field levels in indoor enclosures16.</li>
	<li>Turn off the Wi-Fi interface of nearby devices, such as mobile phones, computers, laptops, access points, etc. One PEM uncertainty is the non-specific identification of the radiation source, that is, PEMs measure the E-field for each frequency without identifying each transmitting device. Therefore, make sure there are no Wi-Fi devices operating at the 2.4 GHz band that could interfere with the experiment.</li>
	<li>Configure one PEM with a sampling rate of 4 s with the specific software that is provided with the PEM.</li>
	<li>Place the PEM at waist height, although in these preliminary measurements, the location where the PEM is worn is not relevant.</li>
	<li>Start the PEM, and have the user walk from one end of the enclosure towards the other, at a pace of about 10 cm/s. E-field levels are the logged data by the PEM while the user is walking.</li>
	<li>Download the logged data with the specific software that is provided with the PEM. Check that all logged data are at the lowest limit of the sensitivity range of the PEM, 0.05 V/m for the frequency band of 2.4 GHz.</li>
	<li>Perform the control measurements on different days to ensure the repeatability of the experiment and to obtain consistency in the results, with no significant variations that could affect their reliability. 
	NOTE: If the control tests are verified on different days, an absence of Wi-Fi radiation sources can be assumed, and the logged data may be due solely to the contribution of the radiation source of the experiment.</li>
</ol>
2. Fixing the Position of the Measuring Device
<ol>
	<li>Carry out this preliminary test in one of the indoor enclosures using three PEMs. The positions of the three PEMs will be evaluated simultaneously to fix the position of the PEM that best avoids the influence of the wearer in the logged data.</li>
	<li>Configure the three PEMs with a sampling rate of 4 s using the configuration software that is provided with each PEM.</li>
	<li>Place the first dosimeter on the lower part of the back in the lumbar area, where the body is maximally shielding the PEM. Place the second dosimeter at waist height, in line of sight (LoS) with the radiation source.</li>
	<li>Place the third dosimeter one meter away from the user (at the end of a tube held by the user to their shoulder) where it will be unaffected by the BSE. Use a carton tube of 1 m in length; e.g., a map holder. The locations of the three PEMs are shown in Figure 1.</li>
	<li>Use a real access point as a radiation source.</li>
	<li>Turn on the PEMs simultaneously just before carrying out the measurements. 
	NOTE: A small gap between different PEMs&#39; data may occur; this will not be relevant to the results. Usually this gap is about 2 or 3 samples, and the total number of samples is about 300.</li>
	<li>Have the user walk slowly towards, then away from the radiation source at a pace of 10 cm/s, with the AP situated ahead of and behind the user, respectively. Figure 2 is a diagram of the experimental enclosure and shows the directions of the predefined paths and the positions of the PEMs.</li>
	<li>Download the data from the PEMs.</li>
</ol>
3. Radiation Source
<ol>
	<li>For the radiation source used in step 4, use an analog signal generator connected to a biconical antenna with a low-loss cable. The biconical antenna is a broadband antenna covering the frequency range of 80 MHz to 3 GHz.</li>
	<li>Configure the analog signal generator to generate a continuous signal, without modulation, and at the frequency of 2,437 MHz, as this is one of the most commonly used frequencies by Wi-Fi systems.</li>
	<li>Configure the generated signal with an equivalent isotropic radiated power (EIRP) of 100 mW, the maximum EIRP that is allowed in Europe.</li>
	<li>Place the biconical antenna in the center of one side of the enclosure (Figure 2) to facilitate the realization of the experiment in dynamic conditions.</li>
	<li>Align the biconical antenna with the user, so that the user directly faces the source, in order to detect the maximum BSE underestimation in the logged data by the non-line of sight (NLoS) PEM, with respect to the logged levels by the PEM unaffected by the BSE.</li>
</ol>
4. Measurement Methodology
<ol>
	<li>Carry out measurements using two PEMs. Configure the PEMs with a sampling period of 4 s with the configuration software that is provided with each PEM.</li>
	<li>Center the first dosimeter on the back, completely NLoS with the radiation source, and where the body is maximally shielding the PEM.</li>
	<li>Place the second dosimeter at a distance of 1 m away from the user (at the end of the tube held by the user to their shoulder) in order to avoid the influence of the human body. This position was determined in step 2. The positions of both PEMs are indicated in Figure 3.</li>
	<li>Place the biconical antenna in a vertical position.</li>
	<li>Turn on the PEMs simultaneously just before carrying out the measurements. As in step 2.6, a small gap here will not be relevant to the results.</li>
	<li>Have the user walk slowly from the opposite side of the corridor towards the radiation source, according to the defined route shown in Figure 3, at a continuous slow pace of approximately 10 cm/s. While the user is walking, the PEM is logging E-field data.</li>
	<li>Download the data from the PEMs using the provided software.</li>
	<li>Repeat steps 4.5, 4.6, and 4.7 with the biconical antenna in a horizontal position, in order to detect the influence of the polarization type.</li>
</ol>
5. Ray Tracing Modelling
<ol>
	<li>Develop or use ray-tracing software based on image theory (a strategy used in ray-tracing techniques for analyzing the propagation of electromagnetic fields16) in order to check the effectiveness of the methodology by comparing experimental and simulated results. The model should predict the E-field levels in empty spaces, and allow interaction of electromagnetic waves with the surrounding environment. When developing this software, follow these steps:
	<ol>
		<li>Develop the model in different stages in order to produce 3D paths based on 2D image generation, in both vertical and horizontal planes. Calculate the E-field as the vector sum of the main ray and other contributions due to the reflections and diffractions of the electromagnetic waves that are registered at each evaluation point within the surrounding environment. Compute the E-field value in an evaluation point as the vector sum of all the contributions (rays) from the source after a given maximum number of interactions with the environment. Use the number of reflections on the walls of the enclosure as an input parameter, with 10 as the maximum value11.</li>
		<li>Employ an extension of Holm&#39;s heuristic diffraction coefficient for diffraction modelling, as proposed by Nechayev and Constantinou and used in Rodr&#237;guez et al.10</li>
	</ol>
	</li>
	<li>As configuration parameters, use features of the experimental setup: the dimensions and the permittivity and conductivity of the materials that form part of each enclosure being tested. Table 111 shows the electromagnetic parameters of the materials used in the simulation. The reflection coefficient associated with conductive materials has a higher magnitude. The value of the reflection coefficients of non-magnetic and non-conducting media is high enough to influence the E-field, calculated as the sum of the main contribution of the direct ray and the other contributions from diffractions and reflections.</li>
	<li>Introduce as configuration parameters the properties of the biconical antenna, the radiation pattern, and polarization.</li>
	<li>Introduce as configuration parameters the frequency (2,437 MHz) and power (20 dBm) of the analog signal generator.</li>
	<li>Run the program after correctly including all the inputs.</li>
	<li>Quantify the results at intervals of 0.01 V/m, with the purpose of emulating the working conditions of the PEM.</li>
	<li>Substitute the results that are below the lowest limit of PEM sensitivity with a value of 0.05 V/m, in order to reproduce the non-detects logged by the PEMs.</li>
</ol>

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The authors have nothing to disclose.

The aspect of this protocol that is essential for the reliable collection of exposure data, without the influence of the PEM uncertainties, is the location of the PEM. The PEM must be situated 1 m away from the user in order to avoid underestimation caused by the influence of the body, and implicitly, to avoid a high number of non-detects in the logged data. There are aspects of the protocol that can be altered; modifications and limitations of the proposed technique are assessed as follows.
The measurement instrument selected to carry out the experiment is the PEM, which has been used in numerous studies for the analysis of EMF exposure in outdoor environments, dynamically, and in large geographical areas24,25,26. Although data measured with the PEMs are not as accurate as the measurements provided by a spectrum analyzer (SA), numerous epidemiological studies use PEMs due to their easy handling and measuring rate26, 4 s being the minimum sampling period. The PEMs used in the work have a minimum limit of sensitivity range of 0.05 V/m. More modern PEMs have been marketed with wider sensitivity ranges, 0.005 V/m being the lowest limit for the frequency band of 2.4 GHz, so the number of non-detects will be lower when the body is shielding the PEM. However, this fact is not relevant for this experiment, since the obtained results without the BSE uncertainty were always greater than 0.05 V/m. There are other models of PEMs with lower sampling periods, but the model used in this experiment has been selected because it is easily portable on the body, at waist height, where the body is maximally shielding the PEM.
In preliminary experiments, a Wi-Fi AP operating at the Wi-Fi frequency band of 2.4 GHz was employed as a radiation source. After assessing the power emitted by the AP with a SA, a check was carried out to confirm that the information packets were not transmitted continuously and that there were periods of time without transmission27,28. As a consequence, a substantial proportion of RF EMF levels were below the detection limit (0.05 V/m) of the PEMs. The minimum Wi-Fi AP duty cycle was fixed by beacon signals and was around 0.01%. Meanwhile, a continuous signal, with the upper duty cycle limit of 100%, reproduces the worst-case exposure conditions, while avoiding the non-detects uncertainty. For this reason, a signal generator and a biconical antenna were used as radiation sources to generate a continuous wave of 100 mW power, at the Wi-Fi frequency, and without modulation.
The E-field levels, in the four selected indoor enclosures, have been predicted with a ray-tracing software based on image theory. The evaluation of the experimental results using another experimental technique, such as a SA with a probe, has not been considered, since the objective is to analyze the influence of the BSE and other PEM uncertainties, and not the PEM&#39;s ability to operate as another measuring device. The limitations of image theory are due to the non-ideal environmental conditions, that is, when the reflecting surfaces are not thin, flat, or planar. The propagation model results collect the uncertainty of reflection coefficients when the environmental conditions are non-ideal. When the surfaces are limited in extent, it is possible to eliminate the rays that do not intercept with them. As the number of reflections increases, the size of the Fresnel ellipsoids increases, and the approximation is worse. However, the rays from multiple reflections will be weaker and have less influence on the final results.
The na&#239;ve approach is applied to solve the uncertainty of the non-detects. This method consists of the substitution of the values below the sensitivity range limit with the lower detection limit29. Other methods exist to correct the uncertainty of non-detects with the substitution of logged data. The robust regression on order statistics (ROS) method predicts the undetected values, considering that they follow a lognormal distribution. Other methods can be applied to the data, but the estimates always present a margin of error. The method of substitution by the lower detection limit has been used, as the substitution by a fixed value allows the identification of the non-detects. Additionally, this region of the CDFs does not present relevant differences among the several cases under analysis.
The uncertainty of the shadow effect of the human body must be addressed with special interest, given that PEMs are designed to be worn by the user, and the presence of the wearer is the cause of this uncertainty. In addition, the underestimation of the BSE may involve an increase in non-detects. The BSE can also be avoided by wearing several PEMs on different parts of the body30,31; averaging the logged data of two PEMs situated on opposite sides of the body leads to a smaller underestimation, and a smaller uncertainty than the logged data of one single PEM5. Another alternative method is to take into account the alteration of the exposure levels due to the BSE in the interpretation of the exposure data and to apply appropriate correction factors. However, these have to be determined individually in function of the activity and the environment, and are very complex to apply correctly. Also, the technique used in this study proposes a practical way to avoid the BSE that only requires a single PEM, avoiding the processing of data.
Taking into account the advances in mobile technology, and the interest in the human body attenuation in future 5G (fifth generation) radio systems32, the technique presented in this study can be used to evaluate human exposure to new generation networks avoiding the aforementioned uncertainties.

The use of wireless local area networks (WLAN) has become considerably more widespread in recent years. Wireless technologies have become alternatives to traditional fixed access ones, and consequently, a large number of access points (AP) have been installed in residential, occupational, and public areas1,2. This large number of AP and personal communication devices has led to substantial interest in the possible risks related to electromagnetic field (EMF) exposure3.
Personal exposimeters (PEMs) are portable devices for the measurement of individual exposure, typically used in the field of epidemiology. Several studies have detected uncertainties when using PEMs in EMF measurements. These findings show the effects that PEMs have on the level of reliability in the obtained results4. Some solutions have been proposed to minimize the effect of these uncertainties, such as good PEM-wearing techniques, small sampling intervals, and measurements of sufficient length5.
Certain authors have published work on the importance of considering the duty factor (or duty cycle) in the exposure measurements. In real-world situations, Wi-Fi devices never transmit with a full duty cycle. Wi-Fi signals consist of intermittent bursts of radiofrequency (RF) energy and periods without any transmissions. Consequently, there is a large proportion of reported exposure measurements that are very low, often falling below the sensitivity range, and which are logged as non-detects by PEMs. Several works propose the use of factors to obtain real values via a theoretical calculation6.
The uncertainty of the shadow effect of the human body has been addressed with special interest, as PEMs are designed to be worn by the user, with the presence of the wearer causing uncertainty in the logged data. Knowledge and quantification of the BSE help provide correct interpretations of the exposure data, without which, it would be necessary to carry out strict measurement procedures. The BSE can be avoided by wearing several PEMs, located on different parts of the human body7, or by applying correction factors to the obtained results5,9,10,11,12. Meanwhile, in other cases, the body has been substituted in simulation techniques with the use of cylinders13. Some works propose implementing specific measurement techniques in order to avoid the influence of the human body13. The present study proposes a measurement methodology that avoids the influence of the body in real indoor enclosures without manipulating exposure data.
One feature of PEMs is the non-identification of the radiation source. PEMs measure the electric field (E-field) levels in certain frequency bands, but if several sources or devices radiate at the same frequency, the PEM measures E-field levels without identifying the contribution from each particular source.
Therefore, because of these sources of uncertainty in the PEMs&#39; logged data, exposure level analysis requires procedures for the experimental evaluation and the numerical prediction of the EMF levels in order to obtain reliable results. This work presents a suitable methodology that can be used to evaluate the exposure to E-fields (2.4 GHz frequency) in indoor enclosures. Using this methodology, the previously mentioned uncertainties caused by underestimation due to the BSE, overestimation caused by non-detects, and the unreliability of the non-identification of the radiation source are avoided. This enhanced reliability means that the data obtained using the proposed method provide an upper bound in the case of adverse conditions in the EMF exposure. The exposure limits established in the national and international standards for health protection were defined for unperturbed EMF data, unaltered by any effect or agent. The proposed experimental procedure is appropriate in terms of regulatory test compliance, since the uncertainties are avoided in the logged data, providing reliable information that can be contrasted with the exposure thresholds.
After implementing the experimental protocol, the obtained results have been compared to the thresholds and recommended exposure values in European legislation. This has been done in order to check the regulatory compliance of EMF exposure due to Wi-Fi systems, in typical indoor environments, which in turn represent common workplace contexts. Currently, a Wi-Fi frequency of 2.4 GHz is one of the communication bands for which there is more widely available data on exposure to the general public. The political interest in this specific band is due to widespread concerns regarding the possible health effects of exposure to RF energy emitted by wireless-enabled devices in sensitive environments, such as healthcare centers, hospitals, schools, and even household settings15.
This work presents a protocol to provide unperturbed measurements regarding E-field exposure conditions, avoiding the uncertainties associated with the use of PEMs. The aim of this work is to improve the use of PEMs as measuring devices in compliance tests.

<table><tbody><tr><td>Personal exposimeter</td><td>SATIMO</td><td>EME SPY 121/100</td><td>Worn personal exposimer to log expsure data</td></tr><tr><td>Personal exposimeter</td><td>ANTENNESSA</td><td>EME SPY 121/120</td><td>Worn personal exposimer to log expsure data</td></tr><tr><td>Wi-Fi Access Point</td><td>CISCO</td><td>Aironet 1130</td><td>Wi-Fi access point, vertial polarization&amp;nbsp;</td></tr><tr><td>Analog Signal Generator&amp;nbsp;</td><td>AGILENT</td><td>N5181A MXG&amp;nbsp;</td><td>Analog Signal Generator&amp;nbsp;</td></tr><tr><td>Precision Conical Dipole&amp;nbsp;</td><td>SEIBERSDORF&amp;nbsp;</td><td>PCD 8250</td><td>Broadband antenna 80 MHz - 3 GHz. Dipole-like radiation pattern that is omnidirectional in the horizontal plane</td></tr><tr><td>Cable</td><td>ROHDE &amp;amp; SCHWARZ</td><td>LARG-214/U&amp;nbsp;</td><td>Low loss cable</td></tr></tbody></table>

effective analysis of human exposure conditions with body-worn dosimeters in the 2.4 ghz band

A well-defined experimental procedure is put forward to evaluate maximum exposure conditions in a worst-case scenario whilst avoiding the uncertainties caused by the use of personal exposimeters (PEMs) as measuring devices: the body shadow effect (BSE), the limited sensitivity range, and the non-identification of the radiation source. An upper bound for exposure levels to EMF in several indoor enclosures has been measured and simulated. The frequency used for the study is 2.4 GHz, as it is the most commonly used band in indoor communications. Although recorded values are well below the International Commission for Non-Ionizing Radiation Protection (ICNIRP) reference levels, there is a particular need to provide reliable exposure levels within particularly sensitive environments. In terms of electromagnetic field (EMF) exposure, limits established in national and international standards for health protection have been set for unperturbed exposure conditions; that is, for real and objective exposure data that have not been altered in any way.

Four indoor enclosures of different sizes were selected to perform the experimental measurements, whose volumes were 63 m3 (dimensions of 12 &#215; 1.26/3 &#215; 2.45 m), 162 m3 (27.15 &#215; 1.93 &#215; 3.1 m), 57 m3 (9 &#215; 2.56 &#215; 2.47 m), and 63 m3 (10 &#215; 2.56 &#215; 2.47 m). The width of the first enclosure was not constant. In the first and second enclosures, the length of the predefined path was 12 m. In the third and fourth enclosures, the length of the predefined path was of the maximum dimension, that is, 9 and 10 m, respectively. One factor that affects the BSE is the type of materials making up the indoor enclosures, as exposure levels increase in the case of environments with conductive materials. Specifically, the enclosures we used were composed of non-reflective materials. In those conditions, the BSE becomes relevant, as the reflected rays logged by PEM under BSE are weaker than in the case of conductive materials.
The results obtained in the preliminary stage are summarized in Figure 4, which compares the logged data by the three PEMs (one at the back, another in the front, and the third situated 1 m away) while the user was walking towards and away from the AP. E-field levels logged by the worn PEM in LoS with the radiation source are very similar to those recorded by the PEM located 1 m away from the wearer, both in LoS with the radiation source, although it is appreciable that the PEM in contact with the body registers lower levels7. For both paths, levels collected by worn PEMs in the shadow area are lower than data collected by worn and not worn PEMs in LoS.
E-field levels logged by the PEMs in each position were very similar in both paths, but there were some differences. Considering the path away from the AP, finite-difference time-domain (FDTD) analysis showed that incident waves can bend around the body user and reach the worn PEM on the opposite side, and even the PEM situated 1 m away, where the BSE is weaker. This effect is more significant in indoor environments, as the shadowed region of the body is small. This was why the data logged by PEMs situated 1 m away from the user in both paths was similar to the exposed conditions.
Regarding the worn PEMs, the effect of the coupling with the body causes a distortion in the PEM radiation pattern (RD) that subsequently affects the logged data. However, as logged data by worn PEMs in LoS tend to be similar, but lower than logged data by PEMs situated 1 m away, it can be concluded that in LoS conditions, the human body has a negligible influence in comparison with the distortions due to the BSE.
As seen in Figure 4, in all PEM positions the E-field levels tend to be lower for the path towards the AP, where the user position is frontal to the radiation source. In the GHz range, the SAR in the whole body (SARWB) is slightly higher under a frontally incident plane wave because of human morphology: larger skin areas and rougher surfaces (toes, feet, chin, face) are contained on the frontal side of the body. The E-field can effectively impinge on these small body parts, which are typical peak SAR locations in the GHz range17.
The transmission from the AP is discontinuous, so many of the logged levels by the PEMs do not reach the lower sensitivity threshold, and the number of non-detects becomes too large. The percentage of non-detects considered as acceptable is below 60%, where substitution might be acceptable, as explained by Helsel18. Although in the results shown in Figure 4, the maximum number of non-detects is 50%, close to the accepted level of 60%, the tests with an AP are reliable enough to confirm that 1 m is an optimal distance to avoid the BSE.
Therefore, the position of the PEM situated 1 m away from the user is optimal to log reliable levels of exposure to the E-field, and is unaffected by the underestimation caused by the influence of the body. Taking into account these considerations, the measurements were performed in the four selected environments, in both horizontal and vertical polarizations and following the methodology described in the previous section: with two PEMs, one worn by the user and in NLoS, and the second situated 1 m away from the user and in LoS with the radiation source.
Figure 5 and Figure 6 show the E-field levels in the first and second enclosures, in a semi-logarithmic scale and in both polarizations along the path towards the radiation source comprised of a biconical antenna and a signal generator. The BSE underestimation is directly dependent on the size of the environment: the underestimation is greater in the second enclosure, and in turn, the effect is greater in outdoor, rather than indoor, enclosures. It is notable that BSE underestimation is larger with vertical than with horizontal polarization, since the polarization type of the main radiation source affects the degree of influence of the BSE. In order to avoid the high number of non-detects in the case of shadow without a further treatment of logged data, the measurements in both polarizations were repeated with a transmission power of 25 dBm (316.12 mW) in the second enclosure. Figure 6 presents the rescaled measurements to 20 dB in both polarizations, and in a semi-logarithmic scale to perceive the E-field levels in the case of shadow. In the case of horizontal polarization, the non-detects have been avoided, although in vertical polarization, the percentage is still considerable.
Measurements in both polarizations were performed in all enclosures under test conditions. Figure 5 shows the results of the first enclosure, shadowed data being similar in both polarizations. However, from the results of the second enclosure, the largest one, shown in Figure 6, the difference of shadowed data in both polarizations is more notable than in Figure 5.
In order to quantify the difference of shadowed data in both polarizations in each enclosure, Table 2 presents the polarization factor (PF) that relates the ratios between the means of non-shadowed and shadowed data in both polarizations, as is shown in (1):
<img alt="Equation 1" src="/files/ftp_upload/56525/56525eq1v2.jpg" />(1)
From Table 2 it can be deduced that the larger the enclosure is, the greater the differences found between non-shadowed and shadowed data for vertical polarization. The results of this study show a more significant underestimation in vertical than in horizontal polarization, because for frequencies around 2,100 MHz, the localized SAR in limbs and head/trunk is higher for vertical polarization, in a standing position, and when waves impinge on the body from the front or back17. In addition, the user is not small in comparison to the wavelength, so the vertical polarization is at a worst-case level in terms of absorption of the incident wave24. When the major axis of the human body is parallel to the electric field vector (which happens when the polarization of the biconical antenna is vertical), the specific absorption rate (SAR) of the human body reaches maximum values19. Theoretically, the vertically polarized waves are largely shielded by the human body, in comparison to the horizontally polarized waves. This is due to the fact that in vertical polarization, the E-field oscillates parallel to the long axis of the wearer8. As the polarization of the antenna is a key factor in the BSE, the proper polarization is vertical, in order to detect the maximum influence of the presence of the user on the measurements of the worn PEM and in NLoS20.
The exposure levels obtained in the four enclosures under test conditions are shown in Figure 7 in a semi-logarithmic scale. The simulation results are shown together with the measurements at each point of the predefined route, demonstrating that both types of data vary in the same way in relation to their distance from the radiation source.
Table 3 summarizes the measured and simulated E-field levels, respectively. For each indoor enclosure the mean, standard deviation, and the maximum and minimum values are provided. It is worth noting the similarity between the statistical values of the experimental and simulated data. The similarity between each pair of experimental and simulated data series has also been checked in terms of the p-value obtained with the Kolmogorov-Smirnov (KS) test. The p-values are shown in Table 3. The p-values were always greater than the significance level of 0.05, so there is an adequate match between each pair of experimental and simulated data series. In addition, it has also been confirmed using the KS test that the cumulative distribution function (CDF) of each series, experimental or simulated, always follows the lognormal statistical distribution in both polarizations.
Figure 7 shows the measured and simulated data in the indoor enclosures being used for testing and the compliance with the thresholds established in the European legislation based on the ICNIRP, which forms the basis of many exposure standards presently applied worldwide in general, domestic, and occupational contexts. In the case of the general population, the limit of exposure to non-ionizing radiation at the 2.4 GHz frequency is 61 V/m. The value of 61 V/m established in the ICNIRP is not the most restrictive limit in terms of human exposure. Other standards exist around the world: in North America, IEEE establishes less restrictive limits: 66.7 V/m for uncontrolled environments, the equivalent for the general public in the ICNIRP. In addition, more restrictive regulation exists in Eastern Europe, such as the case of Russia where the strictest limit for the general population is 3.14 V/m. In Figure 7, the measurements compared with the ICNIRP threshold are not affected by the uncertainties of the PEM, providing reliability in the extracted conclusions with regard to regulation compliance.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/56525/56525fig1.jpg" /> 
Figure 1: Location of the PEMs during the experiment.
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/56525/56525fig2.jpg" /> 
Figure 2: Predefined paths of control tests, towards and away from the radiation source, and position of the three dosimeters.
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/56525/56525fig3.jpg" /> 
Figure 3: Predefined path of the measurement performed in the four enclosures, towards the radiation source, and positions of the dosimeters. The length of the test area within the first and second enclosures, 12 m, is shown.
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/56525/56525fig4.jpg" /> 
Figure 4: CDFs of the results of the three PEMs in different positions. Results are shown 1 m away, worn by the user in LoS, and worn by the user in NLoS for both predefined paths-towards and away from the radiation source.
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/56525/56525fig5.jpg" /> 
Figure 5: Experimental data obtained in the 63 m3 first enclosure. Data are shown for (a) vertical and (b) horizontal polarization, with and without body influence, with a transmission power of 100 mW. The data are shown in function of the number of samples logged by the PEM while the user is walking towards the source. The results are shown in a semi-logarithmic scale.
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/56525/56525fig6.jpg" /> 
Figure 6: Experimental data obtained in the 162 m3 second enclosure. Data are shown for (a) vertical and (b) horizontal polarization, with and without body influence, with a transmission power of 25 dBm (316.12 mW) and rescaled to 20 dBm (100 mW). The data are shown as a function of the number of samples logged by the PEM while the user is walking towards the source. The results are shown in a semi-logarithmic scale.
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/56525/56525fig7.jpg" /> 
Figure 7: Measured and simulated levels of the E-field for vertical polarization. Levels are shown for the (a) first (63 m3), (b) second (162 m3), (c) third (57 m3), and (d) fourth (63 m3) enclosures. The levels are shown as a function of the percentage of the ICNIRP exposure limit of 61 V/m for the general population and for the 2.4 GHz band. The data are shown as a function of the number of samples logged by the PEM while the user is walking towards the source.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td rowspan="2">Material</td>
			<td>Conductivity&#160;</td>
			<td>Relative</td>
		</tr>
		<tr>
			<td>(S/m)</td>
			<td>permittivity</td>
		</tr>
		<tr>
			<td>Ceiling &#8211; chipboard</td>
			<td>0.001</td>
			<td>2.5</td>
		</tr>
		<tr>
			<td>Floor &#8211; Marble</td>
			<td>0.00022</td>
			<td>7</td>
		</tr>
		<tr>
			<td>Lateral walls</td>
			<td>0.005</td>
			<td>3</td>
		</tr>
		<tr>
			<td>Metal</td>
			<td>100</td>
			<td>3</td>
		</tr>
		<tr>
			<td>Glass</td>
			<td>1E-10</td>
			<td>6</td>
		</tr>
		<tr>
			<td>Wood</td>
			<td>0.0006</td>
			<td>2</td>
		</tr>
	</tbody>
</table>
Table 1: Electromagnetic parameters used in the simulation.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td rowspan="2">Enclosure</td>
			<td>Volume</td>
			<td>Polarization</td>
		</tr>
		<tr>
			<td>(m3)</td>
			<td>Factor</td>
		</tr>
		<tr>
			<td>1</td>
			<td>63</td>
			<td>1.0635</td>
		</tr>
		<tr>
			<td>2</td>
			<td>162</td>
			<td>1.3325</td>
		</tr>
		<tr>
			<td>3</td>
			<td>57</td>
			<td>1.0235</td>
		</tr>
		<tr>
			<td>4</td>
			<td>63</td>
			<td>1.0590</td>
		</tr>
	</tbody>
</table>
Table 2: Polarization factor for each enclosure, calculated as the relation between the means of non-shadow and shadow data. The sizes of the enclosures are indicated.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td rowspan="2">Enclosure</td>
			<td>Size</td>
			<td colspan="2">Mean (V/m)</td>
			<td colspan="2">Std (V/m)</td>
			<td colspan="2">Max (V/m)</td>
			<td colspan="2">Min (V/m)</td>
			<td>p-value</td>
			<td>p-value</td>
		</tr>
		<tr>
			<td>(m3)</td>
			<td>Exp</td>
			<td>Sim</td>
			<td>Exp</td>
			<td>Sim</td>
			<td>Exp</td>
			<td>Sim</td>
			<td>Exp</td>
			<td>Sim</td>
			<td>PolV</td>
			<td>PolH</td>
		</tr>
		<tr>
			<td>1</td>
			<td>63</td>
			<td>0.27</td>
			<td>0.29</td>
			<td>0.17</td>
			<td>0.22</td>
			<td>1.45</td>
			<td>1.36</td>
			<td>0.05</td>
			<td>0.05</td>
			<td>0.7296</td>
			<td>0.8924</td>
		</tr>
		<tr>
			<td>2</td>
			<td>162</td>
			<td>0.22</td>
			<td>0.24</td>
			<td>0.2</td>
			<td>0.23</td>
			<td>1.47</td>
			<td>1.41</td>
			<td>0.05</td>
			<td>0.05</td>
			<td>0.4579</td>
			<td>0.3802</td>
		</tr>
		<tr>
			<td>3</td>
			<td>57</td>
			<td>0.25</td>
			<td>0.26</td>
			<td>0.15</td>
			<td>0.17</td>
			<td>1.18</td>
			<td>0.9</td>
			<td>0.05</td>
			<td>0.05</td>
			<td>0.3740</td>
			<td>0.3452</td>
		</tr>
		<tr>
			<td>4</td>
			<td>63</td>
			<td>0.23</td>
			<td>0.25</td>
			<td>0.20</td>
			<td>0.21</td>
			<td>1.24</td>
			<td>1.18</td>
			<td>0.05</td>
			<td>0.05</td>
			<td>0.4679</td>
			<td>0.4263</td>
		</tr>
	</tbody>
</table>
Table 3: Main statistical values of the experimental and simulated results in the four enclosures under test conditions for vertical and horizontal polarization. The sizes of the enclosures are indicated.

Watch this Scientific Journal Video about Effective Analysis of Human Exposure Conditions with Body-worn Dosimeters in the 2.4 GHz Band at JoVE.com

Effective Analysis of Human Exposure Conditions with Body-worn Dosimeters in the 2.4 GHz Band

This study describes a protocol to measure exposure levels in the 2.4 GHz band, avoiding the uncertainties caused by the use of personal exposimeters as measuring devices. These alterations of the exposure levels should be taken into account, especially in compliance testing, where exposure limits are defined from non-perturbed data.

A well-defined experimental procedure is put forward to evaluate maximum exposure conditions in a worst-case scenario whilst avoiding the uncertainties ...

effective-analysis-human-exposure-conditions-with-body-worn

Research

JoVE Journal

Engineering

6.9K Views.  Instituto de Salud Carlos III. This study describes a protocol to measure exposure levels in the 2.4 GHz band, avoiding the uncertainties caused by the use of personal exposimeters as measuring devices. These alterations of the exposure levels should be taken into account, especially in compliance testing, where exposure limits are defined from non-perturbed data. 

Video: Effective Analysis of Human Exposure Conditions with Body-worn Dosimeters in the 2.4 GHz Band

Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification (ADCI) and Dose Estimation

Biological radiation dose can be estimated from dicentric chromosome frequencies in metaphase cells. Performing these cytogenetic dicentric chromosome assays is traditionally a manual, labor-intensive process not well suited to handle the volume of samples which may require examination in the wake of a mass casualty event. Automated Dicentric Chromosome Identifier and Dose Estimator (ADCI) software automates this process by examining sets of metaphase images using machine learning-based image processing techniques. The software selects appropriate images for analysis by removing unsuitable images, classifies each object as either a centromere-containing chromosome or non-chromosome, further distinguishes chromosomes as monocentric chromosomes (MCs) or dicentric chromosomes (DCs), determines DC frequency within a sample, and estimates biological radiation dose by comparing sample DC frequency with calibration curves computed using calibration samples. This protocol describes the usage of ADCI software. Typically, both calibration (known dose) and test (unknown dose) sets of metaphase images are imported to perform accurate dose estimation. Optimal images for analysis can be found automatically using preset image filters or can also be filtered through manual inspection. The software processes images within each sample and DC frequencies are computed at different levels of stringency for calling DCs, using a machine learning approach. Linear-quadratic calibration curves are generated based on DC frequencies in calibration samples exposed to known physical doses. Doses of test samples exposed to uncertain radiation levels are estimated from their DC frequencies using these calibration curves. Reports can be generated upon request and provide summary of results of one or more samples, of one or more calibration curves, or of dose estimation.

Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification ADCI and Dose Estimation

The cytogenetic dicentric chromosome (DC) assay quantifies exposure to ionizing radiation. The Automated Dicentric Chromosome Identifier and Dose Estimator software accurately and rapidly estimates biological dose from DCs in metaphase cells. It distinguishes monocentric chromosomes and other objects from DCs, and estimates biological radiation dose from the frequency of DCs.

Biological radiation dose can be estimated from dicentric chromosome frequencies in metaphase cells. Performing these cytogenetic dicentric chromosome ...

Genetics

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

An evaluation method that includes continuous activities in a daily-living environment was developed for Wearable Mobility Monitoring Systems (WMMS) that attempt to recognize user activities. Participants performed a pre-determined set of daily living actions within a continuous test circuit that included mobility activities (walking, standing, sitting, lying, ascending/descending stairs), daily living tasks (combing hair, brushing teeth, preparing food, eating, washing dishes), and subtle environment changes (opening doors, using an elevator, walking on inclines, traversing staircase landings, walking outdoors). 
To evaluate WMMS performance on this circuit, fifteen able-bodied participants completed the tasks while wearing a smartphone at their right front pelvis. The WMMS application used smartphone accelerometer and gyroscope signals to classify activity states. A gold standard comparison data set was created by video-recording each trial and manually logging activity onset times. Gold standard and WMMS data were analyzed offline. Three classification sets were calculated for each circuit: (i) mobility or immobility, ii) sit, stand, lie, or walking, and (iii) sit, stand, lie, walking, climbing stairs, or small standing movement. Sensitivities, specificities, and F-Scores for activity categorization and changes-of-state were calculated.
The mobile versus immobile classification set had a sensitivity of 86.30% &#177; 7.2% and specificity of 98.96% &#177; 0.6%, while the second prediction set had a sensitivity of 88.35% &#177; 7.80% and specificity of 98.51% &#177; 0.62%. For the third classification set, sensitivity was 84.92% &#177; 6.38% and specificity was 98.17 &#177; 0.62. F1 scores for the first, second and third classification sets were 86.17 &#177; 6.3, 80.19 &#177; 6.36, and 78.42 &#177; 5.96, respectively. This demonstrates that WMMS performance depends on the evaluation protocol in addition to the algorithms. The demonstrated protocol can be used and tailored for evaluating human activity recognition systems in rehabilitation medicine where mobility monitoring may be beneficial in clinical decision-making.

A standardized evaluation method was developed for Wearable Mobility Monitoring Systems (WMMS) that includes continuous activities in a realistic daily living environment. Testing with a series of daily living activities can decrease activity recognition sensitivity; therefore, realistic testing circuits are encouraged for valid evaluation of WMMS performance.

An evaluation method that includes continuous activities in a daily-living environment was developed for Wearable Mobility Monitoring Systems (WMMS) that ...

Behavior

An Automated Microscopic Scoring Method for the &#947;-H2AX Foci Assay in Human Peripheral Blood Lymphocytes

Ionizing radiation is a potent inducer of DNA damage and a well-documented carcinogen. Biological dosimetry comprises the detection of biological effects induced by exposure to ionizing radiation to make an individual dose assessment. This is pertinent in the framework of radiation emergencies, where health assessments and planning of clinical treatment for exposed victims are critical. Since DNA double strand breaks (DSB) are considered to be the most lethal form of radiation-induced DNA damage, this protocol presents a method to detect DNA DSB in blood samples. The methodology is based on the detection of a fluorescent labelled phosphorylated DNA repair protein, namely, &#947;-H2AX. The use of an automated microscopy platform to score the number of &#947;-H2AX foci per cell allows a standardized analysis with a significant decrease in the turn-around time. Therefore, the &#947;-H2AX assay has the potential to be one of the fastest and sensitive assays for biological dosimetry. In this protocol, whole blood samples from healthy adult volunteers will be processed and irradiated in vitro in order to illustrate the usage of the automated and sensitive &#947;-H2AX foci assay for biodosimetry applications. An automated slide scanning system and analysis platform with an integrated fluorescence microscope is used, which allows the fast, automatic scoring of DNA DSB with a reduced degree of bias.

This protocol presents the slide preparation and automated scoring of the &#947;-H2AX foci assay on peripheral blood lymphocytes. To illustrate the method and sensitivity of the assay, isolated lymphocytes were irradiated in vitro. This automated method of DNA DSB detection is useful for fast and high-throughput biological dosimetry applications.

Ionizing radiation is a potent inducer of DNA damage and a well-documented carcinogen. Biological dosimetry comprises the detection of biological effects ...

Cancer Research

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India

Here, we present a visual representation of standard procedures to collect population-level data on personal exposures to household air pollution (HAP) from two different study sites in a resource-constrained setting of Tamil Nadu, India. Particulate matter PM2.5 (particles smaller than 2.5 microns in aerodynamic diameter), carbon monoxide (CO), and black carbon (BC) were measured in pregnant mothers (M), other adult women (OAW), and children (C) at various times over a 4 year period. In addition, stove usage monitoring (SUMs) with data-logging thermometers and ambient measurements of air pollution were carried out. Furthermore, the feasibility of collecting biological samples (urine and dried blood spots [DBSs]) from study participants at the field sites was successfully demonstrated. Based on findings from this and earlier studies, the methods used here have enhanced the data quality and avoided issues with household air pollution and biological sample collection in resource-constrained situations. The procedures established may be a valuable educational tool and resource for researchers conducting similar air pollution and health studies in India and other low- and middle-income countries (LMICs).

We detail the consistent, high-quality procedures used throughout air and biological sampling processes at Indian field sites during a large randomized controlled trial. Insights gathered from the oversight of applications of innovative technologies, adapted for exposure assessment in rural regions, enable better field data collection practices with more reliable outcomes.

Here, we present a visual representation of standard procedures to collect population-level data on personal exposures to household air pollution (HAP) ...

Environment

A Cross-Disciplinary and Multi-Modal Experimental Design for Studying Near-Real-Time Authentic Examination Experiences

Over the past ten years, research into students&#39; emotions in educational environments has increased. Although researchers have called for more studies that rely on objective measures of emotional experience, limitations on utilizing multi-modal data sources exist. Studies of emotion and emotional regulation in classrooms traditionally rely on survey instruments, experience-sampling, artifacts, interviews, or observational procedures. These methods, while valuable, are mainly&#160;dependent on participant or observer subjectivity and is limited in its authentic measurement of students&#39; real-time performance to a classroom activity or task. The latter, in particular, poses a stumbling block to many scholars seeking to objectively measure emotions and other related measures in the classroom, in real-time.
The purpose of this work is to present a protocol to experimentally study students&#39; real-time responses to exam experiences during an authentic assessment situation. For this, a team of educational psychologists, engineers, and engineering education researchers designed an experimental protocol that retained the limits required for accurate physiological sensor measurement, best-practices of salivary collection, and an authentic testing environment. In particular, existing studies that rely on physiological sensors are conducted in experimental environments that are disconnected from educational settings (e.g., Trier Stress Test), detached in time (e.g., before or after a task), or introduce analysis error (e.g., use of sensors in environments where students are likely to move). This limits our understanding of students&#39; real-time responses to classroom activities and tasks. Furthermore, recent research has called for more considerations to be covered around issues of recruitment, replicability, validity, setups, data cleaning, preliminary analysis, and particular&#160;circumstances (e.g., adding a variable in the experimental design) in academic emotions research that relies on multi-modal approaches.

An experimental design was developed to investigate the real-time influences of an examination experience to assess the emotional realities students experience in higher education settings and tasks. This design is the result of a cross-disciplinary (e.g., educational psychology, biology, physiology, engineering) and multi-modal (e.g., salivary markers, surveys, electrodermal sensor) approach.

Over the past ten years, research into students&#39; emotions in educational environments has increased. Although researchers have called for more studies ...

Virtual Scene Generation for Blast Wave Simulations in Military Weapon Training

Generation of Warfighter Avatars from Weapon Training Scene Images for Blast Exposure Simulations

Military personnel involved in weapon training are subjected to repeated low-level blasts. The prevailing method of estimating blast loads involves wearable blast gauges. However, using wearable sensor data,&#160;blast loads to the head or other organs&#160;cannot be accurately estimated without knowledge of the service member&#39;s body posture. An image/video-augmented complementary experimental-computational platform for conducting safer weapon training was developed. This study describes the protocol for the automated generation of weapon training scenes from video data for blast exposure simulations. The blast scene extracted from the video data at the instant of weapon firing involves the service member body avatars, weapons, ground, and other structures. The computational protocol is used to reconstruct service members&#39; positions and postures using this data. Image or video data extracted from service member body silhouettes are used to generate an anatomical skeleton and the key anthropometric data. These data are used to generate the 3D body surface avatars segmented into individual body parts and geometrically transformed to match extracted service member postures. The final virtual weapon training scene is used for 3D computational simulations of weapon blast wave loading on service members. The weapon training scene generator has been used to construct 3D anatomical avatars of individual service member bodies from images or videos in various orientations and postures. Results of the generation of a training scene from shoulder-mounted assault weapon system and mortar weapon system image data are presented. The Blast Overpressure (BOP) tool uses the virtual weapon training scene for 3D simulations of blast wave loading on the service member avatar bodies. This paper presents 3D computational simulations of blast wave propagation from weapon firing and corresponding blast loads on service members in training.

Author Spotlight: Development of an Automated Camera-Based System for Real-Time Blast Overpressure Monitoring and TBI Risk Assessment in Military Training

A computational framework was developed to automatically reconstruct virtual scenes comprising 3D avatars of soldiers using image and/or video data. The virtual scenes help estimate blast overpressure exposure during weapon training. The tool facilitates a realistic representation of human posture in blast exposure simulations in various scenarios.

Military personnel involved in weapon training are subjected to repeated low-level blasts. The prevailing method of estimating blast loads involves ...

Diffuse Optical Spectroscopy for the Quantitative Assessment of Acute Ionizing Radiation Induced Skin Toxicity Using a Mouse Model

Acute skin toxicities from ionizing radiation (IR) are a common side effect from therapeutic courses of external beam radiation therapy (RT) and negatively impact patient quality of life and long term survival. Advances in the understanding of the biological pathways associated with normal tissue toxicities have allowed for the development of interventional drugs, however, current response studies are limited by a lack of quantitative metrics for assessing the severity of skin reactions. Here we present a diffuse optical spectroscopic (DOS) approach that provides quantitative optical biomarkers of skin response to radiation. We describe the instrumentation design of the DOS system as well as the inversion algorithm for extracting the optical parameters. Finally, to demonstrate clinical utility, we present representative data from a pre-clinical mouse model of radiation induced erythema and compare the results with a commonly employed visual scoring. The described DOS method offers an objective, high through-put evaluation of skin toxicity via functional response that is translatable to the clinical setting.

We present a diffuse optical spectroscopic (DOS) approach that provides quantitative optical biomarkers of skin response to radiation. We describe DOS instrumentation design, optical parameters extraction algorithms and the animal handling procedures required to yield representative data from a pre-clinical mouse model of radiation induced erythema.

Acute skin toxicities from ionizing radiation (IR) are a common side effect from therapeutic courses of external beam radiation therapy (RT) and ...

Medicine

A Detailed Protocol for Perspiration Monitoring Using a Novel, Small, Wireless Device

Perspiration monitoring can be utilized for the detection of certain diseases, such as thermoregulation and mental disorders, particularly when the patients are unaware of such disorders or are having difficulty expressing their symptoms. Until now, several devices for perspiration monitoring have been developed; however, such devices tend to have a relatively large exterior, considerable power consumption, and/or less sensitivity.
Recently, we developed a small, wireless device for perspiration monitoring. The device consists of a temperature/relative humidity (T/RH) sensor, battery-driven small data logger, and silica gel as a desiccant in a small cylindrical exterior. The T/RH sensor is placed between the detection windows (through which the water vapor from the skin enters) and the silica gel. The underlying principle of the perspiration monitoring device is based on Fick&#39;s law of diffusion, which means that water vapor flux from the skin to the silica gel (i.e.&#160;transepidermal water loss and perspiration) can be captured by change in humidity at the T/RH sensor. In addition, a baseline subtraction method was adopted to distinguish perspiration and transepidermal water loss.
As shown in the previous report, the developed device can monitor the perspiration at any sites of the body in an easy, wireless manner. However, detailed methods of how to use the device have not been disclosed yet. In this article, therefore, we would like to show the point-by-point tutorials of how to use the device for perspiration monitoring, by showing the sympathetic activity test with the sympathetic skin response monitoring as an example.

Recently, we developed a small wireless device for perspiration monitoring. In this article, we present detailed protocols on how to use the device for perspiration monitoring with an example of the sympathetic activity test.

Perspiration monitoring can be utilized for the detection of certain diseases, such as thermoregulation and mental disorders, particularly when the ...

Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies

Wearable sensors, which are often embedded in commercial smartwatches, allow for continuous and non-invasive health measurements and exposure assessments in clinical studies. Nevertheless, the real-life application of these technologies in studies involving a large number of participants for a significant observation period may be hindered by several practical challenges. 
In this study, we present a modified protocol from a previous intervention study for the mitigation of health effects from desert dust storms. The study involved two distinct population groups: asthmatic children aged 6-11 years and elderly patients with atrial fibrillation (AF). Both groups were equipped with a smartwatch for the assessment of physical activity (using a heart rate monitor, pedometer, and accelerometer) and location (using GPS signals to locate individuals in indoor "at home" or outdoor microenvironments). The participants were required to wear the smartwatch equipped with a data collection application on a daily basis, and data were transmitted via a wireless network to a centrally administered data collection platform for the near real-time assessment of compliance. 
Over a period of 26 months, more than 250 children and 50 patients with AF participated in the aforementioned study. The main technical challenges identified included restricting access to standard smartwatch features, such as gaming, internet browser, camera, and audio recording applications, technical issues, such as loss of GPS signal, especially in indoor environments, and the internal smartwatch settings interfering with the data collection application.
The aim of this protocol is to demonstrate how the use of publicly available application lockers and device automation applications made it possible to address most of these challenges in a simple and cost-effective way. In addition, the inclusion of a Wi-Fi received signal strength indicator significantly improved indoor localization and largely minimized GPS signal misclassification. The implementation of these protocols during the roll-out of this intervention study in the spring of 2020 led to significantly improved results in terms of data completeness and data quality.

Commercial smartwatches equipped with wearable sensors are increasingly being used in population studies. However, their utility is often constrained by their limited battery duration, memory capacity, and data quality. This report provides examples of cost-effective solutions to real-life technical challenges encountered during studies involving asthmatic children and elderly cardiac patients.

Wearable sensors, which are often embedded in commercial smartwatches, allow for continuous and non-invasive health measurements and exposure assessments ...

Bioengineering

Precision Implementation of Minimal Erythema Dose (MED) Testing to Assess Individual Variation in Human Inflammatory Response

Minimal erythema dose (MED) testing is frequently used in clinical settings for determining the smallest amount of ultraviolet (UV) irradiation necessary to produce erythema (inflammatory reddening) on the surface of the skin. In this context, the MED is regarded as a key factor in determining starting doses for UV phototherapy for common skin conditions such as psoriasis and eczema. In research settings, MED testing also has potential to be a powerful tool for assessing within- and between-persons variation in inflammatory responses. However, MED testing has not been widely adopted for use in research settings, likely owing to a lack of published guidelines, which is a barrier to obtaining reproducible results from this assay. Also, protocols and equipment for establishing MED vary widely, making it difficult to compare results across laboratories. Here, we describe a precise and reproducible method to induce and measure superficial erythema using newly designed protocols and methods that can easily be adapted to other equipment and laboratory environments. The method described here includes detail on procedures that will allow extrapolation of a standardized dosage schedule to other equipment so that this protocol can be adapted to any UV radiation source.

Precision Implementation of Minimal Erythema Dose MED Testing to Assess Individual Variation in Human Inflammatory Response

Minimal erythema dose (MED) testing is used to establish dosage schedules for ultraviolet radiation phototherapy. It can assess individual variation in inflammatory response but lacks methodology for achieving reproducible results. Here, we present a precision implementation of MED and demonstrate its ability to capture individual variation in inflammatory response.

Minimal erythema dose (MED) testing is frequently used in clinical settings for determining the smallest amount of ultraviolet (UV) irradiation necessary ...