Observational study of peripheral skin temperature changes following spinal anaesthesia for caesarean delivery

Introduction: Effective spinal anaesthesia for caesarean delivery (CD) is assumed to cause bilateral sympathetic blockade with increased feet skin temperature due to vasodilatation [1]. There has been no published study of peripheral skin temperature measurements during spinal anaesthesia for CD. Our study investigated foot skin temperature changes as spinal anaesthesia was established. Method(s): A single centre, prospective observational study with ethics committee approval (IRAS No. 263967). With informed consent, 60 healthy parturients, 37-42 weeks' gestation with singleton pregnancy scheduled for category 4 CD with spinal anaesthesia were recruited. Standard spinal anaesthesia used 0.5% hyperbaric bupivacaine and diamorphine with IV Phenylephrine and fluids. Skin temperature was measured on the dorsum of both feet with Covidien Mon-a-Therm© skin thermistor sensors prior to intrathecal injection and every minute after until completion of surgery. Theatre room temperature and ambient temperature under surgical drapes were recorded. Two controls were recruited. Result(s): All participants had successful spinal anaesthesia. The Figure shows mean (95% CI) skin temperature changes of both feet of participants during spinal anaesthesia and for controls. The maximum rate of skin temperature increase occurred 5-12 minutes after spinal injection with temperature change plateauing after 30 mins. The mean temperature range was 5.54degreeC (min = 29.7degreeC;max = 35.2degreeC). Discussion(s): This study characterises for the first time the peripheral temperature changes in the feet that occur with sympathetic block after spinal anaesthesia in parturients. Increased bilateral foot skin temperatures occur within 10 minutes of spinal injection. This may be useful for determining successful spinal anaesthesia for CD in addition to other assessments [2]. The insights may be useful for assessing epidural analgesia. The study was supported by an OAA research grant. Data collected by ROAR group.Copyright © 2023 Elsevier Ltd

Workout Detection by Wearable Device Data Using Machine Learning

There are many reports that workouts relieve daily stress and are effective in improving mental and physical health. In recent years, there has been a demand for quick and easy methods to analyze and evaluate living organisms using biological information measured from wearable sensors. In this study, we attempted workout detection for one healthy female (40 years old) based on multiple types of biological information, such as the number of steps taken, activity level, and pulse, obtained from a wristband-type wearable sensor using machine learning. Data were recorded intermittently for approximately 64 days and 57 workouts were recorded. Workouts adopted for exercise were yoga and the workout duration was 1 h. We extracted 3416 min of biometric information for each of three categories: workout, awake activities (activities other than workouts), and sleep. Classification was performed using random forest (RF), SVM, and KNN. The detection accuracy of RF and SVM was high, and the recall, precision, and F-score values when using RF were 0.962, 0.963, and 0.963, respectively. The values for SVM were 0.961, 0.962, and 0.962, respectively. In addition, as a result of calculating the importance of the feature values used for detection, sleep state (39.8%), skin temperature (33.3%), and pulse rate (13.2%) accounted for approximately 86.3% of the total. By applying RF or SVM to the biological information obtained from the wearable wristband sensor, workouts could be detected every minute with high accuracy.

Attempt to extract features and classify subjective poor physical conditions in facial images using deep metric learning.

With the spread of COVID-19, the need for remote detection of physical conditions is increasing, for example, there are several situations wherein the body temperature has to be measured remotely to detect febrile individuals. Aiming to remotely detect physical conditions, the study attempted to investigate anomaly detection based on facial color and skin temperature, which are indicators related to hemodynamics. Triplet loss was used to extract features related to subjective health feelings from facial images to evaluate whether there is a relationship between subjective health feelings and facial images. A classification of subjective health feelings related to poor physical conditions based on these features was also attempted. To obtain the data, an experiment was conducted for approximately 1 year to measure facial visual and thermal images, and subjective feelings related to physical conditions. Anomaly levels were defined based on subjective health feelings. Anomaly detection models were constructed by classifying anomaly and normal data based on subjective health feelings. Facial visible and thermal images were applied to the trained model to quantitatively evaluate the accuracy of the classification of anomaly conditions related to subjective health. At higher levels of anomaly, a combination of facial visible and thermal images resulted in the classification of subjective health feelings with moderate accuracy. Further, the results suggest that the eyes and sides of the nose may indicate subjective health feelings.

Prediction of Individual Dynamic Thermal Sensation in Subway Commute Using Smart Face Mask.

Wearable sensors and machine learning algorithms are widely used for predicting an individual's thermal sensation. However, most of the studies are limited to controlled laboratory experiments with inconvenient wearable sensors without considering the dynamic behavior of ambient conditions. In this study, we focused on predicting individual dynamic thermal sensation based on physiological and psychological data. We designed a smart face mask that can measure skin temperature (SKT) and exhaled breath temperature (EBT) and is powered by a rechargeable battery. Real-time human experiments were performed in a subway cabin with twenty male students under natural conditions. The data were collected using a smartphone application, and we created features using the wavelet decomposition technique. The bagged tree algorithm was selected to train the individual model, which showed an overall accuracy and f-1 score of 98.14% and 96.33%, respectively. An individual's thermal sensation was significantly correlated with SKT, EBT, and associated features.

Evaluation of the effects of cold and hot environmental temperatures on the spatial distribution of facial skin temperature

In the Covid-19 disaster, fever detection using infrared thermography became widespread. A person with fever is detected based on the facial skin temperature measured in a non-invasive and free-of-restraint method. Recent studies have pointed out that the facial whole skin temperature, when measured immediately after entering a moderately moderate environment from a cold environment, is not practical for detecting persons with fever because it is greatly affected by the environmental temperature. On the other hand, the effect of cold and hot temperatures on the details of the entire face has not been evaluated. In this study, we compared the cold and hot environments and the acclimation to moderate temperatures to The effects of cold and hot environments on the whole face skin temperature distribution was evaluated in detail.The results showed that the periorbital area and side of the nose were least affected in the cold environment, and the side of the the nose was least affected in the hot environment. And, These parts are suggested to be suitable for core temperature estimation considering the environmental temperature. © 2022 IEEE.

MsWH: A Multi-Sensory Hardware Platform for Capturing and Analyzing Physiological Emotional Signals.

This paper presents a new physiological signal acquisition multi-sensory platform for emotion detection: Multi-sensor Wearable Headband (MsWH). The system is capable of recording and analyzing five different physiological signals: skin temperature, blood oxygen saturation, heart rate (and its variation), movement/position of the user (more specifically of his/her head) and electrodermal activity/bioimpedance. The measurement system is complemented by a porthole camera positioned in such a way that the viewing area remains constant. Thus, the user's face will remain centered regardless of its position and movement, increasing the accuracy of facial expression recognition algorithms. This work specifies the technical characteristics of the developed device, paying special attention to both the hardware used (sensors, conditioning, microprocessors, connections) and the software, which is optimized for accurate and massive data acquisition. Although the information can be partially processed inside the device itself, the system is capable of sending information via Wi-Fi, with a very high data transfer rate, in case external processing is required. The most important features of the developed platform have been compared with those of a proven wearable device, namely the Empatica E4 wristband, in those measurements in which this is possible.

Mask Dermatitis - Perhaps a New Dermatologic Masquerade Syndrome?

Background: The COVID-19 pandemic has fostered many new patterns of behavior. One of the more popular strategies has been facialmask wearing. Outbreaks of skin reactions secondary to mask wearing have been commonplace findings in outpatient primary care offices and Conclusions: Facial mask wearing has been extensively utilized throughout the COVID-19 pandemic. The classic mask is made of polypropylene, a non-woven fabric which is an effective barrier for transmission of viruses and other microorganisms (1). Materials such as cotton and polyester are also used. Some masks even contain copper and trace metals , so it is not unusual that ubiquitous mask wearing would increase the incidence of facial dermatoses (2).What we have observed is that these new facial eruptions have mimicked other known facial skin entities prompting referrals to specialty offices, misdiagnoses, patient misconceptions, and the potential initiation of erroneous treatments. These facial skin presentations, which we refer to as “mask dermatitis”, have brought about a new “masquarade syndrome”. Recent literature has described similar facial dermatoses which have been termed “maskne” (3). It is surmised that alterations in skin temperature, pH, and humidity apparently affect sebum secretion in the face leading to inflammatory changes (4). Although the analogy to acne is certainly plausible, we feel that our cases may be more consistent with contact dermatitis. We found that simply discontinuing mask wearing often resolves the facial outbreaks and prevents the need for unnecessary topical therapy, oral medications and needless surgical biopsies. In the wake of the recent COVID-19 pandemic, we feel strongly that “mask dermatitis” should be considered as part of the differential diagnosis in all patients presenting with facial skin eruptions.

A Telecare System for Use in Traditional Persian Medicine

Background: In Persian Medicine (PM), measuring the wrist temperature/humidity and pulse is one of the main methods for determining a person's health status and temperament. An important problem is the dependence of the diagnosis on the physician's interpretation of the above-mentioned criteria. Perhaps this is one reason why this method has yet to be combined with modern medical methods. Also, sometimes there is a need to use PM to diagnose patients remotely, especially during a pandemic. This brings up the question of how to implement PM into a telecare system. This study addresses these concerns and outlines a system for measuring pulse signals and temperament detection based on PM. Methods: A system was designed and clinically implemented based on PM that uses data from recorded thermal distribution, a temperament questionnaire, and a customized device that logs the pulse waves on the wrist. This system was used for patient care via telecare. Results: The temperaments of 34 participants were assessed by a PM specialist using the standardized Mojahedi Mizaj Questionnaire (MMQ). Thermal images of the wrist in the supine position (named Malmas in PM), the back of the hand, and the entire face were also recorded under the supervision of the physician. Also, the wrist pulse waves were evaluated by a customized pulse measurement device. Finally, the collected data could be sent to a physician via a telecare system for further interpretation and prescription of medications. Conclusion: This preliminary study focused on the implementation of a combinational hardware-software system for patient assessment based on PM. It appears that the design and construction of a customized device that can measure the pulse waves, and some other criteria, according to PM, is possible and can decrease the dependency of the diagnostic to PM specialists. Thus, it can be incorporated into a telemedicine system. © 2021 Nafisi and Ghods.

Predicting Deep Body Temperature (Tb) from Forehead Skin Temperature: Tb or Not Tb?

There is a need to rapidly screen individuals for heat strain and fever using skin temperature (Tsk) as an index of deep body temperature (Tb). This study's aim was to assess whether Tsk could serve as an accurate and valid index of Tb during a simulated heatwave. Seven participants maintained a continuous schedule over 9-days, in 3-day parts; pre-/post-HW (25.4 °C), simulated-HW (35.4 °C). Contact thermistors measured Tsk (Tforehead, Tfinger); radio pills measured gastrointestinal temperature (Tgi). Proximal-distal temperature gradients (ΔTforehead-finger) were also measured. Measurements were grouped into ambient conditions: 22, 25, and 35 °C. Tgi and Tforehead only displayed a significant relationship in 22 °C (r: 0.591; p < 0.001) and 25 °C (r: 0.408; p < 0.001) conditions. A linear regression of all conditions identified Tforehead and ΔTforehead-finger as significant predictors of Tgi (r2: 0.588; F: 125.771; p < 0.001), producing a root mean square error of 0.26 °C. Additional residual analysis identified Tforehead to be responsible for a plateau in Tgi prediction above 37 °C. Contact Tforehead was shown to be a statistically suitable indicator of Tgi in non-HW conditions; however, an error of ~1 °C makes this physiologically redundant. The measurement of multiple sites may improve Tb prediction, though it is still physiologically unsuitable, especially at higher ambient temperatures.

Metrics from Wearable Devices as Candidate Predictors of Antibody Response Following Vaccination against COVID-19: Data from the Second TemPredict Study.

There is significant variability in neutralizing antibody responses (which correlate with immune protection) after COVID-19 vaccination, but only limited information is available about predictors of these responses. We investigated whether device-generated summaries of physiological metrics collected by a wearable device correlated with post-vaccination levels of antibodies to the SARS-CoV-2 receptor-binding domain (RBD), the target of neutralizing antibodies generated by existing COVID-19 vaccines. One thousand, one hundred and seventy-nine participants wore an off-the-shelf wearable device (Oura Ring), reported dates of COVID-19 vaccinations, and completed testing for antibodies to the SARS-CoV-2 RBD during the U.S. COVID-19 vaccination rollout. We found that on the night immediately following the second mRNA injection (Moderna-NIAID and Pfizer-BioNTech) increases in dermal temperature deviation and resting heart rate, and decreases in heart rate variability (a measure of sympathetic nervous system activation) and deep sleep were each statistically significantly correlated with greater RBD antibody responses. These associations were stronger in models using metrics adjusted for the pre-vaccination baseline period. Greater temperature deviation emerged as the strongest independent predictor of greater RBD antibody responses in multivariable models. In contrast to data on certain other vaccines, we did not find clear associations between increased sleep surrounding vaccination and antibody responses.

Exoskeletons and Exosuits Could Benefit from Mode-Switching Body Interfaces That Loosen/Tighten to Improve Thermal Comfort.

Exoskeletons and exosuits (exos) are wearable devices that physically assist movement. User comfort is critically important for societal adoption of exos. Thermal comfort (a person's satisfaction with their thermal environment) represents a key design challenge. Exos must physically attach/interface to the body to apply forces, and these interfaces inevitably trap some heat. It is envisioned that thermal comfort could be improved by designing mode-switching exo interfaces that temporarily loosen around a body segment when assistive forces are not being applied. To inform exo design, a case series study (N = 4) based on single-subject design principles was performed. Our objective was to assess individual responses to skin temperature and thermal comfort during physical activity with a Loose leg-sleeve interface compared with a Form-Fitting one, and immediately after a Form-Fitting sleeve switched to Loose. Skin under the Loose sleeve was 2-3 °C (4-6 °F) cooler after 25 min of physical activity, and two of four participants reported the Loose sleeve improved their thermal comfort. After completion of the physical activity, the Form-Fitting sleeve was loosened, causing a 2-4 °C (3-8 °F) drop in skin temperature underneath for all participants, and two participants to report slightly improved thermal comfort. These findings confirmed that an exo that can quickly loosen its interface when assistance is not required-and re-tighten when it is- has the potential to enhance thermal comfort for some individuals and environments. More broadly, this study demonstrates that mode-switching mechanisms in exos can do more than adjust physical assistance: they can also exploit thermodynamics and facilitate thermoregulation in a way that enhances comfort for exo users.

A Wearable Skin Temperature Monitoring System for Early Detection of Infections

This paper describes a wearable, open-source wrist temperature monitoring system that enables the reliable identification of slowly-varying skin temperature patterns that may be indicative of infections. The hardware platform uses a Bluetooth Low Energy (BLE) wireless interface and includes three skin temperature sensors, a thermally-isolated ambient temperature sensor, an inertial measurement unit (IMU), and a Galvanic skin response (GSR) sensor. A template-matching algorithm is used to detect weak but long-lived anomalous temperature patterns that deviate from the normal circadian rhythm are thus may be driven by infections. Experimental and simulation results confirm that small temperature anomalies (peak value <0.4°C) extending over 2-3 weeks can be detected with a total error rate <10%. IEEE

Dynamic Variations in Infrared Skin Temperature of Weaned Pigs Experimentally Inoculated with the African Swine Fever Virus: A Pilot Study.

African swine fever (ASF) is a devastating viral disease in pigs and is therefore economically important for the swine industry. ASF is characterized by a short incubation period and immediate death, making the early identification of ASF-infected pigs essential. This pilot-scale study evaluates whether the infrared thermography (IRT) technique can be used as a diagnostic tool to detect changes in skin temperature (Tsk) during the early stages of disease development in experimentally ASF-infected pigs. Clinical symptoms and rectal temperatures (Tcore) were recorded daily, and IRT readings during the experimental ASF infection were analyzed. All infected pigs died at 5-8 days post inoculation (dpi), and the incubation period was approximately 4 dpi. The average Tcore increased from 0 dpi (38.9 ± 0.3 °C) to 7 dpi (41.0 ± 0.5 °C) and decreased by 8 dpi (39.8 ± 0 °C). The maximum Tsk of ASF-infected pigs increased from 2 (35.0 °C) to 3 dpi (38.5 °C). The mean maximum Tsk observed from three regions on the skin (ear, inguinal, and neck) significantly increased from 2 to 3 dpi. This study presents a non-contact method for the early detection of ASF in infected pigs using thermal imaging at 3 days after ASF infection.

[Physiological cost of wearing protective masks - a narrative review of the literature]. / Koszt fizjologiczny noszenia masek ochronnych ­ narracyjny przeglad literatury.

Due to the controversy related to the necessity to wear protective masks and the negative perceptions of users, an attempt was made to analyze the available scientific research on the physiological consequences of wearing various types of masks. The literature review includes publications available in the PubMed bibliographic database, describing symptoms and complaints of mask users (e.g., the feeling of discomfort, fatigue, headaches), different physiological reactions depending on the type of mask (surgical masks, masks with an exhalation valve, with air flow, etc.) as well as the influence of air composition, temperature and humidity in the space under the mask. The impact of using the mask on the ability to exercise (maximal effort, moderate effort) and mental work was outlined. The consequences of wearing masks by people in a different physiological state (pregnancy) were discussed. Proposals for the organization of work were also presented in order to minimize the negative consequences for people wearing masks. The analysis of the presented studies shows that, regardless of the type of masks worn, they can intensify the body's reactions to a varying degree, increasing the physiological cost of work and worsening the ability to make both physical and mental effort. In addition, the mask can contribute, among others, to more frequent headaches, symptoms of fatigue or the subjective feeling of discomfort. However, despite these adverse effects, the use of masks is important to protect people against harmful factors in the work and communal environments, and during the current SARS-CoV-2 pandemic, it has become a necessity. The discomfort of wearing a mask can be reduced by taking appropriate breaks. It should be emphasized that the rhythm of work and breaks in wearing the mask should take into account the individual limitations of the employee. Med Pr. 2021;72(5):569-89.

An attempt to construct the individual model of daily facial skin temperature using variational autoencoder.

Facial skin temperature (FST) has also gained prominence as an indicator for detecting anomalies such as fever due to the COVID-19. When FST is used for engineering applications, it is enough to be able to recognize normal. We are also focusing on research to detect some anomaly in FST. In a previous study, it was confirmed that abnormal and normal conditions could be separated based on FST by using a variational autoencoder (VAE), a deep generative model. However, the simulations so far have been a far cry from reality. In this study, normal FST with a diurnal variation component was defined as a normal state, and a model of normal FST in daily life was individually reconstructed using VAE. Using the constructed model, the anomaly detection performance was evaluated by applying the Hotelling theory. As a result, the area under the curve (AUC) value in ROC analysis was confirmed to be 0.89 to 1.00 in two subjects.

Analysis of hand environment factors contributing to the hand surface infection barrier imparted by lactic acid.

BACKGROUND: Organic acids on the surface of human hands contribute to the barrier against transient pathogens. This is the first study to explore the synergistic contribution of lactic acid and other hand environment-related features on the antibacterial properties of the hand surface. MATERIALS AND METHODS: We estimated the contribution of fingerprint depth, skin pH, stratum corneum water content, skin temperature, and sweat rate of the hands to the infection barrier using an observational survey of 105 subjects. The relationship between each factor and the antibacterial activity of the hands was analyzed using Pearson's correlation coefficient. We performed molecular dynamics simulations to study the interaction between lactic acid and bacterial membranes. RESULTS: The amount of lactic acid on the hands and skin temperature contributed positively to the antimicrobial activity (r = 0.437 and P = 3.18 × 10-6 , r = 0.500 and P = 5.66 × 10-8 , respectively), while the skin pH contributed negatively (r = -0.471, P = 3.99 × 10-7 ). The predicted value of the combined antimicrobial effect of these parameters was [antimicrobial activity] = 0.21 × [lactic acid] - 0.25 × [skin pH] + 0.26 × [skin temperature] + 0.98. The coefficient of determination (R2 ) was 0.50. CONCLUSION: The increase in the amount of non-ionic lactic acid due to lower pH and improvement in the fluidity of the cell membrane due to higher temperatures enable the efficient transport of lactic acid into cells and subsequent antimicrobial activity. The proposed mechanism could help to develop an effective hand infection barrier technology.

Infrared Thermal Imaging as a Novel Non-Invasive Point-of-Care Tool to Assess Filarial Lymphoedema.

Lymphatic filariasis causes disfiguring and disabling lymphoedema, which is commonly and frequently exacerbated by acute dermatolymphangioadenitis (ADLA). Affected people require long-term care and monitoring but health workers lack objective assessment tools. We examine the use of an infrared thermal imaging camera as a novel non-invasive point-of-care tool for filarial lower-limb lymphoedema in 153 affected adults from a highly endemic area of Bangladesh. Temperature differences by lymphoedema stage (mild, moderate, severe) and ADLA history were visualised and quantified using descriptive statistics and regression models. Temperatures were found to increase by severity and captured subclinical differences between no lymphoedema and mild lymphoedema, and differences between moderate and severe stages. Toes and ankle temperatures detected significant differences between all stages other than between mild and moderate stages. Significantly higher temperatures, best captured by heel and calf measures, were found in participants with a history of ADLA, compared to participants who never had ADLA, regardless of the lymphoedema stage. This novel tool has great potential to be used by health workers to detect subclinical cases, predict progression of disease and ADLA status, and monitor pathological tissue changes and stage severity following enhanced care packages or other interventions in people affected by lymphoedema.

Surgical masks do not increase the risk of heat stroke during mild exercise in hot and humid environment.

Surgical masks are widely used for the prevention of respiratory infections. However, the risk of heat stroke during intense work or exercise in hot and humid environment is a concern. This study aimed to examine whether wearing a surgical mask increases the risk of heat stroke during mild exercise in such environment. Twelve participants conducted treadmill exercise for 30 min at 6 km/h, with 5% slope, 35°C ambient temperature, and 65% relative humidity, while wearing or not a surgical mask (mask and control trials, respectively). Rectal temperature (Trec), ear canal temperature (Tear), and mean skin temperature (mean Tskin) were assessed. Skin temperature and humidity of the perioral area of the face (Tface and RHface) were also estimated. Thermal sensation and discomfort, sensation of humidity, fatigue, and thirst were rated using the visual analogue scale. Trec, Tear, mean Tskin, and Tface increased during the exercise, without any difference between the two trials. RHface during the exercise was greater in the mask trial. Hot sensation was greater in the mask trial, but no influence on fatigue and thirst was found. These results suggest that wearing a surgical mask does not increase the risk of heat stroke during mild exercise in moist heat.

IoT garment for remote elderly care network.

The elderly is a continuous growth sector thanks to the life expectancy increase in Western society. This sector is especially at risk from the appearance of respiratory diseases and, therefore, is the most affected sector in the COVID-19 epidemic. Many of these elderly require continuous care in residences or by specialized caregivers, but these personal contacts put this sector at risk. In this work, an IoT system for elderly remote monitoring is studied, designed, developed and tested. This system is composed by a smart garment that records information from various physiological sensors in order to detect falls, sudden changes in body temperature, heart problems and heat stroke; This information is sent to a cloud server through a gateway located in the patient's residence, allowing to real-time monitor remotely patient's activity using a customized App, as well as receiving alerts in dangerous situations. This system has been tested with professional caregivers, obtaining usability and functionality surveys; and, in addition, a detailed power-consumption study has been carried out. The results, compared with other similar systems, demonstrate that the proposed one is useful, usable, works in real time and has a decent power consumption that allows the patient to carry it during all day without charging the battery.