An exploratory, intra- and interindividual comparison of the deep neural network automatically measured calf surface radiation temperature during cardiopulmonary running and cycling exercise testing: A preliminary study.

Non-invasive and contactless infrared thermography (IRT) measurements have been claimed to indicate acute neural, cardiovascular, and thermoregulatory adaptations during exercise. Due to challenging comparability, reproducibility, and objectivity, investigations considering different exercise types and intensities, and automatic ROI analysis are currently needed. Thus, we aimed to examine surface radiation temperature (Tsr) variations during different exercise types and intensities in the same individuals, ROI, and environmental conditions. Ten healthy, active males performed a cardiopulmonary exercise test on a treadmill in the first week and on a cycling ergometer the following week. Respiration, heart rate, lactate, rated perceived exertion, the mean, minimum, and maximum Tsr of the right calf (CTsr (°C)), and the surface radiation temperature pattern (CPsr) were explored. We executed two-way rmANOVA and Spearman's rho correlation analyses. Across all IRT parameters, mean CTsr showed the highest association to cardiopulmonary parameters (E.g., oxygen consumption: rs = -0.612 (running); -0.663 (cycling); p < .001). A global significant difference of CTsr was identified between all relevant exercise test increments for both exercise-types (p < .001; η2p = .842) and between both exercise-types (p = .045; η2p = .205). Differences in CTsr between running and cycling significantly appeared after a 3-min recovery period, whereas lactate, heart rate, and oxygen consumption were not different. High correlations between the CTsr values extracted manually and the CTsr values processed automatically by a deep neural network were identified. The applied objective time series analysis enables crucial insights into intra- and interindividual differences between both tests. CTsr variations indicate different physiological demands between incremental running and cycling exercise testing. Further studies applying automatic ROI analyses are needed to enable the extensive analysis of inter- and intraindividual factors influencing the CTsr variation during exercise to allow determine the criterion and predictive validity of IRT parameters in exercise physiology.

Acute physiological responses to a pyramidal exercise protocol and the associations with skin temperature variation in different body areas.

This study aimed to examine the skin temperature (Tsk) variations in five regions of interest (ROI) to assess whether possible disparities between the ROI's Tsk could be associated with specific acute physiological responses during cycling. Seventeen participants performed a pyramidal load protocol on a cycling ergometer. We synchronously measured Tsk in five ROI with three infrared cameras. We assessed internal load, sweat rate, and core temperature. Reported perceived exertion and calves' Tsk showed the highest correlation (r = -0.588; p < 0.01). Mixed regression models revealed that the heart rate and reported perceived exertion were inversely related to calves' Tsk. The exercise duration was directly associated with the nose tip and calf Tsk but inversely related to the forehead and forearm Tsk. The sweat rate was directly related to forehead and forearm Tsk. The association of Tsk with thermoregulatory or exercise load parameters depends on the ROI. The parallel observation of the face and calf Tsk could indicate simultaneously the observation of acute thermoregulatory needs and individual internal load. The separate Tsk analyses of individual ROI appear more suitable to examine specific physiological response than a mean Tsk of several ROI during cycling.

iBEAM: substrate-integrated hollow waveguides for efficient laser beam combining.

Laser light sources are routinely applied building blocks in optical sensor technologies. While lasers are emitting at a precisely defined wavelength within narrow emission bands, chem/bio-sensing applications frequently demand multi-wavelength illumination for addressing a series of species. Instead of using broadband radiation sources, it is a viable strategy to efficiently combine the beams emitted from different lasers to maintain the spectral brightness and yet cover extended wavelength regimes. In this study, substrate-integrated hollow waveguides (iHWGs) are reported as a versatile and efficient alternative compared to conventional beam combining concepts, especially for applications in the mid-infrared spectral regime leading to a highly efficient multi-port beam combiner-the iBEAM.

Sensing hydrocarbons with interband cascade lasers and substrate-integrated hollow waveguides.

Tunable diode laser absorption spectroscopy (TDLAS) is an excellent analytical technique for gas sensing applications. In situ sensing of relevant hydrocarbon gases is of substantial interest for a variety of in-field scenarios including environmental monitoring and process analysis, ideally providing accurate, molecule specific, and rapid information with minimal sampling requirements. Substrate-integrated hollow waveguides (iHWGs) have demonstrated superior properties for gas sensing applications owing to minimal sample volumes required while simultaneously serving as efficient photon conduits. Interband cascade lasers (ICLs) are recently emerging as mid-infrared light sources operating at room temperature, with low power consumption, and providing excellent potential for integration. Thereby, portable and handheld mid-infrared sensing devices are facilitated. Methane (CH4) is among the most frequently occurring, and thus, highly relevant hydrocarbons requiring in situ emission monitoring by taking advantage of its distinct molecular absorption around 3 µm. Here, an efficient combination of iHWGs with ICLs is presented providing a methane sensor calibrated in the range of 100 to 2000 ppmv with a limit of detection at 38 ppmv at the current stage of development. Furthermore, a measurement precision of 0.62 ppbv during only 1 s of averaging time has been demonstrated, thereby rendering this sensor concept useful for in-line and on-site emission monitoring and process control applications.

Towards Exercise Radiomics: Deep Neural Network-Based Automatic Analysis of Thermal Images Captured During Exercise.

Infrared thermography is increasingly applied in sports science due to promising observations regarding changes in skin's surface radiation temperature ( Tsr) before, during, and after exercise. The common manual thermogram analysis limits an objective and reproducible measurement of Tsr. Previous analysis approaches depend on expert knowledge and have not been applied during movement. We aimed to develop a deep neural network (DNN) capable of automatically and objectively segmenting body parts, recognizing blood vessel-associated Tsr distributions, and continuously measuring Tsr during exercise. We conducted 38 cardiopulmonary exercise tests on a treadmill. We developed two DNNs: body part network and vessel network, to perform semantic segmentation of 1 107 855 thermal images. Both DNNs were trained with 263 training and 75 validation images. Additionally, we compare the results of a common manual thermogram analysis with these of the DNNs. Performance analysis identified a mean IoU of 0.8 for body part network and 0.6 for vessel network. There is a high agreement between manual and automatic analysis (r = 0.999; p 0.001; T-test: p = 0.116), with a mean difference of 0.01 °C (0.08). Non-parametric Bland Altman's analysis showed that the 95% agreement ranges between - 0.086 °C and 0.228 °C. The developed DNNs enable automatic, objective, and continuous measurement of Tsr and recognition of blood vessel-associated Tsr distributions in resting and moving legs. Hence, the DNNs surpass previous algorithms by eliminating manual region of interest selection and form the currently needed foundation to extensively investigate Tsr distributions related to non-invasive diagnostics of (patho-)physiological traits in means of exercise radiomics.

Infrared Thermography in Exercise Physiology: The Dawning of Exercise Radiomics.

Infrared thermography (IRT) is a non-invasive tool to measure the body surface radiation temperature (Tsr). IRT is an upcoming technology as a result of recent advancements in camera lenses, detector technique and data processing capabilities. The purpose of this review is to determine the potential and applicability of IRT in the context of dynamic measurements in exercise physiology. We searched PubMed and Google Scholar to identify appropriate articles, and conducted six case experiments with a high-resolution IRT camera (640 × 480 pixels) for complementary illustration. Ten articles for endurance exercise, 12 articles for incremental exercise testing and 11 articles for resistance exercise were identified. Specific Tsr changes were detected for different exercise types. Close to physical exertion or during prolonged exercise six recent studies described "tree-shaped" or "hyper-thermal" surface radiation pattern (Psr) without further specification. For the first time, we describe the Tsr and Psr dynamics and how these may relate to physiological adaptations during exercise and illustrate the differential responsiveness of Psr to resistance or endurance exercise. We discuss how bias related to individual factors, such as skin blood flow, or related to environmental factors could be resolved by innovative technological approaches. We specify why IRT seems to be increasingly capable of differentiating physiological traits relevant for exercise physiologists from various forms of environmental, technical and individual bias. For refined analysis, it will be necessary to develop and implement standardized and accurate pattern recognition technology capable of differentiating exercise modalities to support the evaluation of thermographic data by means of radiomics.

Advanced Photonic Sensors Based on Interband Cascade Lasers for Real-Time Mouse Breath Analysis.

A multiparameter gas sensor based on distributed feedback interband cascade lasers emitting at 4.35 µm and ultrafast electro-spun luminescence oxygen sensors has been developed for the quantification and continuous monitoring of 13CO2/12CO2 isotopic ratio changes and oxygen in exhaled mouse breath samples. Mid-infrared absorption spectra for quantitatively monitoring the enrichment of 13CO2 levels were recorded in a miniaturized dual-channel substrate-integrated hollow waveguide using balanced ratiometric detection, whereas luminescence quenching was used for synchronously detecting exhaled oxygen levels. Allan variance analysis verified a CO2 measurement precision of 1.6‰ during a 480 s integration time. Routine online monitoring of exhaled mouse breath was performed in 14 mechanically ventilated and instrumented mice and demonstrated the feasibility of online isotope-selective exhaled breath analysis within microliters of probed gas samples using the reported combined sensor platform.