New Frontiers for Applications of Thermal Infrared Imaging Devices: Computational Psychopshysiology in the Neurosciences

Compression and Encryption of Heterogeneous Signals for Internet of Medical Things

Psychophysiological computing can be utilized to analyze heterogeneous physiological signals with psychological behaviors in the Internet of Medical Things (IoMT). Since IoMT devices are generally limited by power, storage, and computing resources, it's very challenging to process the physiological signal securely and efficiently. In this work, we design a novel scheme named Heterogeneous Compression and Encryption Neural Network (HCEN), which aims to protect signal security and reduce the required resources in processing heterogeneous physiological signals. The proposed HCEN is designed as an integrated structure that introduces the adversarial properties of Generative Adversarial Networks (GAN) and the feature extraction functionality of Autoencoder (AE). Moreover, we conduct simulations to validate the performance of HCEN using the MIMIC-III waveform dataset. Electrocardiogram (ECG) and Photoplethysmography (PPG) signals are extracted in the simulation. The results reveal that the proposed HCEN can effectively encrypt floating-point signals. Meanwhile, the compression performance outperforms baseline compression methods.

Emerging effects of temperature on human cognition, affect, and behaviour

Human body core temperature is tightly regulated within approximately 37 °C. Global near surface temperature has increased by over 1.2 °C between 1850 and 2020. In light of the challenge this poses to human thermoregulation, the present perspective article sought to provide an overview on the effects of varying ambient and body temperature on cognitive, affective, and behavioural domains of functioning. To this end, an overview of observational and experimental studies in healthy individuals and individuals with mental disorders was provided. Within body core temperature at approximately 37 °C, relatively lower ambient and skin temperatures appear to evoke a need for social connection, whereas comparably higher temperatures appear to facilitate notions of other as closer and more sociable. Above-average ambient temperatures are associated with increased conflicts as well as incident psychotic and depressive symptoms, mental disorders, and suicide. With mild hypo- and hyperthermia, paradoxical effects are observed: whereas the acute states are generally characterised by impairments in cognitive performance, anxiety, and irritability, individuals with depression experience longer-term symptom improvements with treatments deliberately inducing these states for brief amounts of time. When taken together, it has thus become clear that temperature is inexorably associated with human cognition, affect, and (potentially) behaviour. Given the projected increase in global warming, further research into the affective and behavioural sequelae of heat and the mechanisms translating it into mental health outcomes is urgently warranted.

Thermosensation and emotion: Thermosensory accuracy in a dynamic thermal matching task is linked to depression and anxiety symptomatology

Interoception is related to the generation of bodily feelings and the awareness of ourselves as 'sentient beings', informing the organism about its bodily needs to guarantee survival. Previous studies have reported links among interoception, emotion processing, and mental health. For example, the alignment of interoceptive dimensions (i.e., accuracy, sensibility, awareness) can predict emotional symptoms, such as anxiety. Here, we aimed to investigate the relationship between the perception of a certain type of skin-mediated interoceptive signal, i.e., thermosensation, and self-reported depression, anxiety, and stress. One hundred seventy participants completed the Depression Anxiety Stress Scale (DASS-21) and a dynamic thermal matching task, a static temperature detection task, and a heartbeat counting task. Our results revealed that self-reported anxiety and depression were related to the perception of temperature on hairy and non-hairy skin, respectively: higher anxiety was related to better performance on the thermal matching task on the forearm, while higher depression was related to poorer performance on dynamic and static temperature tasks on the palm. Discrepancies between thermosensory accuracy and sensibility measures ('trait prediction error') were related to heightened anxiety, in line with previous studies. No significant correlations were found between DASS-21 scores and heartbeat counting accuracy. In conclusion, this study suggests that individual differences in thermosensory perception in different areas of the body are associated with self-reported anxiety and depression.

Thermal profile classification of the back of sportive and sedentary healthy individuals

BACKGROUND

Infrared thermography (IRT) is a non-harmful, risk-free imaging technique and it has application for healthy and pathological population.

OBJECTIVE

The aim of this study is to evaluate the thermographic profiles of the back of sport practitioners from different disciplines and compare it with those of sedentary healthy individuals.

METHOD

The back of 160 healthy subjects were evaluated, and participants were grouped considering their sport practice: team sport (TS), individual sport (IS), weight training (WT), inactive (I). Three regions of interest were identified to analyze the cervical, thoracic and lumbar temperatures of the back.

RESULTS

The Multivariate analysis of variance (MANOVA) resulted significant showing statistical differences for the cervical (p < 0.001), dorsal (p = 0.0011), and lumbar areas (p = 0.0366). The Tukey post-hoc test for pairwise comparison showed statistically significant differences between groups. For the cervical area significance was found between the IN and WT group (p = 0.002), the IN and IS group (p < 0.001), IN and TS group (p = 0.020). The dorsal area resulted significant between the IN and WT group (p = 0.007), the IN and IS group (p < 0.001), IN and TS group. The lumbar area showed significant differences only between the IN and WT group and the IN and IS group (p = 0.043).

CONCLUSION

This study demonstrated that inactive individuals manifest a statistically significant higher temperature in the cervical, dorsal and lumbar area of the back compared to sportive individuals.

Facial functional networks during resting state revealed by thermal infrared imaging

In recent decades, an increasing number of studies on psychophysiology and, in general, on clinical medicine has employed the technique of facial thermal infrared imaging (IRI), which allows to obtain information about the emotional and physical states of the subjects in a completely non-invasive and contactless fashion. Several regions of interest (ROIs) have been reported in literature as salient areas for the psychophysiological characterization of a subject (i.e. nose tip and glabella ROIs). There is however a lack of studies focusing on the functional correlation among these ROIs and about the physiological basis of the relation existing between thermal IRI and vital signals, such as the electrodermal activity, i.e. the galvanic skin response (GSR). The present study offers a new methodology able to assess the functional connection between salient seed ROIs of thermal IRI and all the pixel of the face. The same approach was also applied considering as seed signal the GSR and its phasic and tonic components. Seed correlation analysis on 63 healthy volunteers demonstrated the presence of a common pathway regulating the facial thermal functionality and the electrodermal activity. The procedure was also tested on a pathological case study, finding a completely different pattern compared to the healthy cases. The method represents a promising tool in neurology, physiology and applied neurosciences.

Development status of novel spectral imaging techniques and application to traditional Chinese medicine

Traditional Chinese medicine (TCM) is a treasure of the Chinese nation, providing effective solutions to current medical requisites. Various spectral techniques are undergoing continuous development and provide new and reliable means for evaluating the efficacy and quality of TCM. Because spectral techniques are noninvasive, convenient, and sensitive, they have been widely applied to in vitro and in vivo TCM evaluation systems. In this paper, previous achievements and current progress in the research on spectral technologies (including fluorescence spectroscopy, photoacoustic imaging, infrared thermal imaging, laser-induced breakdown spectroscopy, hyperspectral imaging, and surface enhanced Raman spectroscopy) are discussed. The advantages and disadvantages of each technology are also presented. Moreover, the future applications of spectral imaging to identify the origins, components, and pesticide residues of TCM in vitro are elucidated. Subsequently, the evaluation of the efficacy of TCM in vivo is presented. Identifying future applications of spectral imaging is anticipated to promote medical research as well as scientific and technological explorations.

Contactless Technologies, Sensors, and Systems for Cardiac and Respiratory Measurement during Sleep: A Systematic Review

Sleep is essential to physical and mental health. However, the traditional approach to sleep analysis-polysomnography (PSG)-is intrusive and expensive. Therefore, there is great interest in the development of non-contact, non-invasive, and non-intrusive sleep monitoring systems and technologies that can reliably and accurately measure cardiorespiratory parameters with minimal impact on the patient. This has led to the development of other relevant approaches, which are characterised, for example, by the fact that they allow greater freedom of movement and do not require direct contact with the body, i.e., they are non-contact. This systematic review discusses the relevant methods and technologies for non-contact monitoring of cardiorespiratory activity during sleep. Taking into account the current state of the art in non-intrusive technologies, we can identify the methods of non-intrusive monitoring of cardiac and respiratory activity, the technologies and types of sensors used, and the possible physiological parameters available for analysis. To do this, we conducted a literature review and summarised current research on the use of non-contact technologies for non-intrusive monitoring of cardiac and respiratory activity. The inclusion and exclusion criteria for the selection of publications were established prior to the start of the search. Publications were assessed using one main question and several specific questions. We obtained 3774 unique articles from four literature databases (Web of Science, IEEE Xplore, PubMed, and Scopus) and checked them for relevance, resulting in 54 articles that were analysed in a structured way using terminology. The result was 15 different types of sensors and devices (e.g., radar, temperature sensors, motion sensors, cameras) that can be installed in hospital wards and departments or in the environment. The ability to detect heart rate, respiratory rate, and sleep disorders such as apnoea was among the characteristics examined to investigate the overall effectiveness of the systems and technologies considered for cardiorespiratory monitoring. In addition, the advantages and disadvantages of the considered systems and technologies were identified by answering the identified research questions. The results obtained allow us to determine the current trends and the vector of development of medical technologies in sleep medicine for future researchers and research.

Intraoperative thermal infrared imaging in neurosurgery: machine learning approaches for advanced segmentation of tumors

Surgical resection is one of the most relevant practices in neurosurgery. Finding the correct surgical extent of the tumor is a key question and so far several techniques have been employed to assist the neurosurgeon in preserving the maximum amount of healthy tissue. Some of these methods are invasive for patients, not always allowing high precision in the detection of the tumor area. The aim of this study is to overcome these limitations, developing machine learning based models, relying on features obtained from a contactless and non-invasive technique, the thermal infrared (IR) imaging. The thermal IR videos of thirteen patients with heterogeneous tumors were recorded in the intraoperative context. Time (TD)- and frequency (FD)-domain features were extracted and fed different machine learning models. Models relying on FD features have proven to be the best solutions for the optimal detection of the tumor area (Average Accuracy = 90.45%; Average Sensitivity = 84.64%; Average Specificity = 93,74%). The obtained results highlight the possibility to accurately detect the tumor lesion boundary with a completely non-invasive, contactless, and portable technology, revealing thermal IR imaging as a very promising tool for the neurosurgeon.

Modulation of Heart Rate Variability following PAP Ion Magnetic Induction Intervention in Subjects with Chronic Musculoskeletal Pain: A Pilot Randomized Controlled Study

Heart rate variability (HRV) analysis has emerged as a simple and non-invasive technique to indirectly evaluate the autonomic nervous system (ANS), and it is considered a sensible and advanced index of health status. Pulsed electromagnetic fields (PEMFs) are widely used in clinical settings for improving the health status of individuals with chronic musculoskeletal pain. The aim of the present single-blind, randomized, placebo-controlled parallel pilot study was to investigate the acute effect of a single session of PEMFs stimulation by a PAP ion magnetic induction (PAPIMI) device on ANS activity, as measured by HRV, in patients with chronic musculoskeletal pain, and compare such effect with that induced by a sham (control) PAPIMI inductor. Thirty-two patients were randomized into two groups: PAPIMI intervention (PAP) (n = 17) and sham PAPIMI intervention (SHAM-PAP) (n = 15). HRV was assessed before and following the interventions. The PAP group showed a significant increase in all values of the time-domain parameters (SDNN, RMSSD, NN50, and pNN50) and the HF component of HRV, suggesting a parasympathetic effect. In contrast, the SHAM-PAP group showed no significant differences in all HRV indices following the intervention. Preliminary findings suggested that PAPIMI inductor could influence ANS activity and provided initial evidence of the potential physiological response induced by the PAPIMI device.

Face Thermal Map of the Mexican Population in the Basal State

There has been a wide use of thermal images of the human body in recent years, specifically images with thermal information of regions of interest (ROI) in the face; this information can be used for epidemiological, clinical, and/or psychological purposes. Due to this, it is important to have plenty of information on temperature in these ROIs in the basal state that allows their use as a reference in terms of their thermal analysis. In this work, a face thermal map of the Mexican population in the basal state (n = 196) is created, adding the comparison between different population groups, such as gender, age, and clinical status, obtaining results of great interest for future research. The t-test for independent samples was applied to the ROIs with normal distribution and Mann−Whitney u-test to the ones that did not present normal distribution. Statistically significant differences were found in some of the ROI comparisons like the corrugator, the supraorbitals, and the chin between the control and clinical groups, as well as in the differentiation by age (p < 0.05).

Theory and applications of InfraRed and thermal image analysis in ergonomics research

Designing products and services to fit human needs, wants and lifestyle require meaningful data. With Industry 4.0 and the internet of things, we have many ways to capture data using sensors and other means. InfraRed (IR) cameras are quite ubiquitous, especially for screening illness and wellness. They can provide a wealth of data on different objects and even people. However, their use has been limited due to processing complexities. With reducing cost and increasing accuracy of IR cameras, access to thermal data is becoming quite widespread, especially in medicine and people-related applications. These cameras have software to help process the data, with a focus on qualitative analyses and rather primitive quantitative analyses. In ergonomics, data from multiple users are essential to make reasonable predictions for a given population. In this study, using 4 simple experiments, several quantitative analysis techniques such as simple statistics, multivariate statistics, geometric modeling, and Fourier series modeling are applied to IR images and videos to extract essential user and population data. Results show that IR data can be useful to provide user and population data that are important for design. More research in modeling IR data and application software is needed for the increased application of IR information in ergonomics applications.

Non-Contact Negative Mood State Detection Using Reliability-Focused Multi-Modal Fusion Model

Negative mood states include tension, depression, anger, fatigue, and confusion, which represent the weak internal emotions of a human. Negative mood states exert adverse impact on individuals' ability to make rational decisions, which entails the practicable method of negative mood state detection. The most commonly used negative mood state detection methods are based on the psychological scale, which requires additional work and brings inconvenience to the subject in the application scenarios. To overcome this challenge, this paper proposes a novel non-contact negative mood state detection method according to the knowledge of affective computing. The POMS-net model is used to extract temporal-spatial features from visible and infrared thermal videos, and the negative mood state detection is realized using data reliability-focused multi-modal fusion. The proposed method is verified using the HDT-BR dataset collected in the aerospace medicine experiment "Earth-Star II" and the VIRI public dataset. The experimental results on the datasets verify that our method outperforms the comparison methods.

Experimental evaluation of the thermal performance of cool pavement materials in cold regions of China

The local thermal property evaluation of pavement materials has not received enough attention, making the implementation of cool material measures to alleviate the urban heat island difficult. This study aims to conduct a control experiment on cool pavement bricks selection from the local market. The surface temperature difference and change characteristics of 28 bricks with different physical properties (i.e. colour, thickness, size and internal structure) were recorded by an infrared thermal imager and thermometers. Comparative analysis shows that the maximum surface temperature of the light-yellow brick is 7.3 °C lower than that of the ash black one, and the maximum surface temperature of hollow bricks is 5.6 °C lower than that of solid bricks. Thick bricks have a slightly low mean and maximum surface temperatures, and large bricks also have a low maximum surface temperature, but only deep coloured bricks. The analysis of variance revealed that for the maximum surface temperature, the three factors (i.e. colour, thickness and internal structure) have an interactive effect; for the mean surface temperature, only the colour and internal structure factors have a significant independent effect. This paper provides a feasible bottom-up cooling scheme for urban underlying surface.

Capacitive and Resistive Electric Transfer Therapy: A Comparison of Operating Methods in Non-specific Chronic Low Back Pain

Capacitive and resistive electric transfer (TECAR) modes of therapy use radiant energy to generate endogenous heat and are used for musculoskeletal disorders for their analgesic, decontracting, and elasticizing properties. While the capacitive mode is supposed to interact with soft tissues, the resistive mode interacts more with hard tissues. This study aims to investigate whether the successive order of the two modes during their application could make a difference concerning the outcome. The study included 40 patients affected by chronic non-specific low back pain. Patients were assessed using algometry, before and immediately after the therapeutic intervention, and thermal imaging, before, immediately after, and then 30 and 60 min after the intervention. Each patient had two TECAR interventions on different days of a total of 20 min each, with a resistive followed by capacitive mode and conversely, capacitive followed by resistive mode. The capacitive mode alternated with the resistive mode by 10 min during either intervention. Results showed that the effects consisting of temporary increases in the superficial temperature of the lower back and pressure pain threshold were alike for both interventions. We conclude that TECAR therapy reduces low back pain regardless of the operative mode adopted, with only an insignificant advantage when starting the sequence from the resistive application.

Central and Peripheral Thermal Signatures of Brain-Derived Fatigue during Unilateral Resistance Exercise: A Preliminary Study

Simple Summary

Fatigue is considered a brain-derived emotion that could impact performance during the execution of physical exercises. Infrared thermography is a valuable technique able to measure the psychophysiological state associated with emotions in a contactless manner. The aim of the study is to test the capability of infrared thermography to evaluate the central and peripheral physiological effect of fatigue through facial skin and muscle temperature modulations collected during the execution of a unilateral resistance exercise of the lower limb. Both time- and frequency-domain analyses were performed on the temperature time course of the face and limbs. Particularly, significant correlations between features extracted from the thermal signals and the perceived exertion were found. These findings confirmed the ability of thermal imaging to detect both peripheral and central effects of fatigue in response to physical exercises. These results could foster the employment of infrared thermography to monitor the psychophysiological state of the athletes during training. The possibility to calibrate the training load in accordance with the psychophysiological conditions could improve the performance of the athletes during the training process and competitions.

Abstract

Infrared thermography (IRT) allows to evaluate the psychophysiological state associated with emotions from facial temperature modulations. As fatigue is a brain-derived emotion, it is possible to hypothesize that facial temperature could provide information regarding the fatigue related to exercise. The aim of this study was to investigate the capability of IRT to assess the central and peripheral physiological effect of fatigue by measuring facial skin and muscle temperature modulations in response to a unilateral knee extension exercise until exhaustion. Rate of perceived exertion (RPE) was recorded at the end of the exercise. Both time- (∆T_ROI: pre–post exercise temperature variation) and frequency-domain (∆PSD: pre–post exercise power spectral density variation of specific frequency bands) analyses were performed to extract features from regions of interest (ROIs) positioned on the exercised and nonexercised leg, nose tip, and corrugator. The ANOVA-RM revealed a significant difference between ∆T_ROI (F_(1.41,9.81) = 15.14; p = 0.0018), and between ∆PSD of myogenic (F_(1.34,9.39) = 15.20; p = 0.0021) and neurogenic bands (F_(1.75,12.26) = 9.96; p = 0.0034) of different ROIs. Moreover, significant correlations between thermal features and RPE were found. These findings suggest that IRT could assess both peripheral and central responses to physical exercise. Its applicability in monitoring the psychophysiological responses to exercise should be further explored

Interpersonal Emotion Dynamics in Couples With Somatic Symptom Disorder: Dyadic Coherence in Facial Temperature During Emotional Interactions

OBJECTIVE

Disturbances in emotional processes are commonly reported in patients with a somatic symptom disorder (SSD). Although emotions usually occur in social interactions, little is known about interpersonal emotion dynamics of SSD patients during their actual emotional encounters. This study examined physiological coherence (linkage) between SSD patients and their partners, and in healthy couples during their emotional interactions. Secondarily, we explored group-level relationships between participants' and their partners' subjective affect.

METHODS

Twenty-nine romantic couples (16 healthy and 13 SSD patient-couples) underwent a dyadic conversation task with neutral and anger-eliciting topics followed by a guided relaxation. Partners' cutaneous facial temperature was recorded simultaneously by functional infrared thermal imaging. Immediately after each condition, participants reported on their pain intensity, self-affect, and perceived partner-affect.

RESULTS

Emotional conditions and having a partner with an SSD significantly affected coherence amplitude on the forehead (F(2,54) = 4.95, p = .011) and nose tip temperature (F(2,54) = 3.75, p = .030). From baseline to anger condition, coherence amplitude significantly increased in the patient-couples, whereas it decreased in the healthy couples. Correlation changes between partners' subjective affect comparably accompanied the changes in physiological coherence in healthy and patient-couples.

CONCLUSIONS

Inability to reduce emotional interdependence in sympathetic activity and subjective affect during a mutual conflict observed in SSD patient-couples seems to capture emotion co-dysregulation. Interventions should frame patients' emotional experiences as embodied and social. Functional infrared thermal imaging confirms to be an ecological and reliable method for examining autonomic changes in interpersonal contexts.Registration Page: https://osf.io/8eyjr.

Development of computational models for microtesla-level magnetic brain scanning: a novel avenue for device development

BACKGROUND

Detection of locally increased blood concentration and perfusion is critical for assessment of functional cortical activity as well as diagnosis of conditions such as intracerebral hemorrhage (ICH). Current paradigms for assessment of regional blood concentration in the brain rely on computed tomography (CT), magnetic resonance imaging (MRI), and perfusion blood oxygen level dependent functional magnetic resonance imaging (BOLD-fMRI).

RESULTS

In this study, we developed computational models to test the feasibility of novel magnetic sensors capable of detecting hemodynamic changes within the brain on a microtesla-level. We show that low-field magnetic sensors can accurately detect changes in magnetic flux density and eddy current damping signals resulting from increases in local blood concentration. These models predicted that blood volume changes as small as 1.26 mL may be resolved by the sensors, implying potential use for diagnosis of ICH and assessment of regional blood flow as a proxy for cerebral metabolism and neuronal activity. We then translated findings from our computational model to demonstrate feasibility of accurate detection of modeled ICH in a simulated human cadaver setting.

CONCLUSIONS

Overall, microtesla-level magnetic scanning is feasible, safe, and has distinct advantages compared to current standards of care. Computational modeling may facilitate rapid prototype development and testing of novel medical devices with minimal risk to human participants prior to device construction and clinical trials.

Infrared-based visualization of exhalation flows while wearing protective face masks

Since the onset of the COVID-19 pandemic, a large number of flow visualization procedures have been proposed to assess the effect of personal protective equipment on respiratory flows. This study suggests infrared thermography as a beneficial visualization technique because it is completely noninvasive and safe and, thus, can be used on live individuals rather than mannequins or lung simulators. Here, we examine the effect of wearing either of three popular face coverings (a surgical mask, a cloth mask, or an N95 respirator with an exhalation valve) on thermal signatures of exhaled airflows near a human face while coughing, talking, or breathing. The flow visualization using a mid-wave infrared camera captures the dynamics of thermal inhomogeneities induced by increased concentrations of carbon dioxide in the exhaled air. Thermal images demonstrate that both surgical and cloth face masks allow air leakage through the edges and the fabric itself, but they decrease the initial forward velocity of a cough jet by a factor of four. The N95 respirator, on the other hand, reduces the infrared emission of carbon dioxide near the person's face almost completely. This confirms that the N95-type mask may indeed lead to excessive inhalation of carbon dioxide as suggested by some recent studies.

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.

Infrared Thermography Images Acquisition for a Technical Perspective in Screening and Diagnostic Processes: Protocol Standardized Acquisition

In this technical report we describe the thermographic setting protocol suitable for the FLIR T650SC thermal imager (FLIR Systems, Inc., Wilsonville, OR), an instrument that detects electromagnetic radiation in the infrared field which is physiologically emitted from the human body. FLIR T650SC thermal imager processes infrared radiations graphically and analyzes them through a specific software. In biomedicine, infrared thermography is a promising technique amongst other conventional methods used for detecting skin temperature differences considered as a possible sign of disturbances in the human body. Currently, automatic screening of temperature from a safe distance is an instrument utilized in the front line of the SARS CoV2 emergency.

The processing method of the thermogram considers an initial setting of constant parameters that cannot be subsequently modified such as temperature range, focusing and image composition. After the acquisition variable values important in the processing and analysis of the thermogram, such as detection of environment temperature, reflected temperature, emissivity, relative humidity and contrast palette, are set in the software. The analysis is performed using the FLIR Tools software.

In the biomedical field standardized acquisition of thermograms facilitates the identification of trigger points and areas of hyper- and hypothermia distributed on the skin surface and muscle bundles. The protocol made it possible to create images with the same acquisition method for all patients.

The thermal imaging camera is a valid screening tool because its execution is rapid, it is non-invasive, well-tolerated, and at a low cost for patients.

Regions of interest selection and thermal imaging data analysis in sports and exercise science: a narrative review

OBJECTIVE

Infrared thermography (IRT) is a non-invasive, contactless and low-cost technology that allows recording of the radiating energy that is released from a body, providing an estimate of its superficial temperature. Thanks to the improvement of infrared thermal detectors, this technique is widely used in the biomedical field to monitor the skin temperature for different purposes (e.g. assessing circulatory diseases, psychophysiological state, affective computing). Particularly, in sports and exercise science, thermography is extensively used to assess sports performance, to investigate superficial vascular changes induced by physical exercise, and to monitor injuries. However, the methods of analysis employed to treat IRT data are not standardized, and hence introduce variability in the results.

APPROACH

This review focuses on the methods of analysis currently used for thermal imaging in sports and exercise science.

MAIN RESULTS

Firstly, the procedures employed for the selection of regions of interest (ROIs) from anatomical body districts are reviewed, paying attention also to the potentialities of morphing algorithms to increase the reproducibility of thermal results. Secondly, the statistical approaches utilized to characterize the temperature frequency and spatial distributions within ROIs are investigated, showing their strengths and weaknesses. Moreover, the importance of employing tracking methods to analyze the temporal thermal oscillations within ROIs is discussed. Thirdly, the capability of employing procedures of investigation based on machine learning frameworks on thermal imaging in sports science is examined.

SIGNIFICANCE

Finally, some proposals to improve the standardization and the reproducibility of IRT data analysis are provided, in order to facilitate the development of a common database of thermal images and to improve the effectiveness of IRT in sports science.

Fascinating MXene nanomaterials: emerging opportunities in the biomedical field

In recent years, there has been rapid progress in MXene research due to its distinctive two-dimensional structure and outstanding properties. Especially in biomedical applications, MXenes have attracted widespread favor with numerous studies on biosafety, bioimaging, therapy, and biosensing, although their development is still in the experimental stage. A comprehensive understanding of the current status of MXenes in biomedicine will promote their use in clinical applications. Here, we review advances in MXene research. First, we introduce the methods of synthesis, surface modification and functionalization of MXenes. Then, we summarize the biosafety and biocompatibility, paving the way for specific biomedical applications. On this basis, MXene nanostructures are described with respect to their use in antibacterial, bioimaging, cancer therapy, tissue regeneration and biosensor applications. Finally, we discuss MXene as a promising candidate material for further applications in biomedicine.

The Core of Medical Imaging: State of the Art and Perspectives on the Detectors

In this review, the roles of detectors in various medical imaging techniques were described. Ultrasound, optical (near-infrared spectroscopy and optical coherence tomography) and thermal imaging, magnetic resonance imaging, computed tomography, single-photon emission tomography, positron emission tomography were the imaging modalities considered. For each methodology, the state of the art of detectors mainly used in the systems was described, emphasizing new technologies applied.

Deep learning techniques for automated detection of autism spectrum disorder based on thermal imaging

Children with autism spectrum disorder have impairments in emotional processing which leads to the inability in recognizing facial expressions. Since emotion is a vital criterion for having fine socialisation, it is incredibly important for the autistic children to recognise emotions. In our study, we have chosen the facial skin temperature as a biomarker to measure emotions. To assess the facial skin temperature, the thermal imaging modality has been used in this study, since it has been recognised as a promising technique to evaluate emotional responses. The aim of this study was the following: (1) to compare the facial skin temperature of autistic and non-autistic children by using thermal imaging across various emotions; (2) to classify the thermal images obtained from the study using the customised convolutional neural network compared with the ResNet 50 network. Fifty autistic and fifty non-autistic participants were included for the study. Thermal imaging was used to obtain the temperature of specific facial regions such as the eyes, cheek, forehead and nose while we evoked emotions (Happiness, anger and sadness) in children using an audio-visual stimulus. Among the emotions considered, the emotion anger had the highest temperature difference between the autistic and non-autistic participants in the region's eyes (1.9%), cheek (2.38%) and nose (12.6%). The accuracy obtained by classifying the thermal images of the autistic and non-autistic children using Customised Neural Network and ResNet 50 Network was 96% and 90% respectively. This computer aided diagnostic tool can be a predictable and a steadfast method in the diagnosis of the autistic individuals.

Convolutional Neural Networks for Differential Diagnosis of Raynaud’s Phenomenon Based on Hands Thermal Patterns

Raynaud’s phenomenon (RP) is a microvessels’ disorder resulting in transient ischemia. It can be either primary or secondary to connective tissue diseases, such as systemic sclerosis. The differentiation between primary and secondary to systemic sclerosis is of paramount importance to set the proper therapeutic strategy. Thus far, thermal infrared imaging has been employed to accomplish this task by monitoring the finger temperature response to a controlled cold challenge. A completely automated methodology based on deep convolutional neural network is here introduced with the purpose of being able to differentiate systemic sclerosis from primary RP patients by relying uniquely on thermal images of the hands acquired at rest. The classification performance of such a method was compared to that of a three-dimensional convolutional neural network model implemented to classify thermal images of the hands recorded during rewarming from a cold challenge. No significant differences were found between the two procedures, thus ensuring the possibility to avoid the cold challenge. Moreover, the convolutional neural network models were compared with standard feature-based approaches and showed higher performances, thus overcoming the limitations related to the feature extraction (e.g., biases introduced by the operator). Such automated procedures can constitute promising tools for large scale screening of primary RP and secondary to systemic sclerosis in clinical practice.

Does Osteopathic Manipulative Treatment Induce Autonomic Changes in Healthy Participants? A Thermal Imaging Study

Osteopathic manipulative treatment (OMT) has been demonstrated to be an effective therapy in several clinical conditions and age groups. Despite the clinical effectiveness, lack of robust data in terms of neurobiological, specifically autonomic, mechanisms of action is observed. Preliminary studies showed a parasympathetic effect leading to a trophotropic effect of OMT. However, these data are limited to heart rate variability (HRV) analysis. In order to study further the role of OMT on the autonomic nervous system, a cross-over randomized controlled trial RCT has been designed to test the effect of osteopathic treatment compared to sham therapy on a range of autonomic parameters. Thermal images, HRV and skin conductance data were collected on a sample of healthy adults. The study design consisted of two sessions (OMT and SHAM), 1 treatment per week, lasting 35 min each, composed of 5 min of baseline, 25 min of treatment, and 5 min of post-touch. During the baseline and the post-treatment, participants received no touch. Thirty-seven participants (aged 27 ± 5 years old, male ratio 40%) completed the study. Multivariate analysis showed a significant parasympathetic effect of group as well as of epoch on thermographic data of the nose (estimate 0.38; 95% CI 0.12–0.63; p < 0.01), left (0.17; 0.06–0.27; <0.001) and right (0.16; 0.07–0.24; <0.001) perioral as well as on the forehead (0.07; 0.01–0.12; <0.01) regions but not for the chin (0.08; −0.02 to 0.18; 0.13). Consistent with a parasympathetic effect, analyses demonstrated a difference between OMT and sham groups on the nuHF (p < 0.001) and DFA-a1 (p < 0.01) as well as on skin conductance (<0.01). The present research supports the hypothesis that a single session of OMT as compared to sham induces autonomic consequences in healthy non-symptomatic adults. Clinicaltrial.gov identifier: NCT03888456, https://clinicaltrials.gov/ct2/show/NCT03888456.

Driver Stress State Evaluation by Means of Thermal Imaging: A Supervised Machine Learning Approach Based on ECG Signal

Traffic accidents determine a large number of injuries, sometimes fatal, every year. Among other factors affecting a driver’s performance, an important role is played by stress which can decrease decision-making capabilities and situational awareness. In this perspective, it would be beneficial to develop a non-invasive driver stress monitoring system able to recognize the driver’s altered state. In this study, a contactless procedure for drivers’ stress state assessment by means of thermal infrared imaging was investigated. Thermal imaging was acquired during an experiment on a driving simulator, and thermal features of stress were investigated with comparison to a gold-standard metric (i.e., the stress index, SI) extracted from contact electrocardiography (ECG). A data-driven multivariate machine learning approach based on a non-linear support vector regression (SVR) was employed to estimate the SI through thermal features extracted from facial regions of interest (i.e., nose tip, nostrils, glabella). The predicted SI showed a good correlation with the real SI (r = 0.61, p = ~0). A two-level classification of the stress state (STRESS, SI ≥ 150, versus NO STRESS, SI < 150) was then performed based on the predicted SI. The ROC analysis showed a good classification performance with an AUC of 0.80, a sensitivity of 77%, and a specificity of 78%.

Effect of Facial Skin Temperature on the Perception of Anxiety: A Pilot Study

The extent of anxiety and psychological stress can impact upon the optimal performance of simulation-based practices. The current study investigates the association between differences in skin temperature and perceived anxiety by under- (n = 21) and post-graduate (n = 19) nursing students undertaking a cardiopulmonary resuscitation (CPR) training. Thermal facial gradients from selected facial regions were correlated with the scores assessed by the State-Trait Anxiety Inventory (STAI) and the chest compression quality parameters measured using mannequin-integrated accelerometer sensors. A specific temperature profile was obtained depending on thermal facial variations before and after the simulation event. Statistically significant correlations were found between STAI scale scores and the temperature facial recordings in the forehead (r = 0.579; p < 0.000), periorbital (r = 0.394; p < 0.006), maxillary (r = 0.328; p < 0.019) and neck areas (r = 0.284; p < 0.038). Significant associations were also observed by correlating CPR performance parameters with the facial temperature values in the forehead (r = 0.447; p < 0.002), periorbital (r = 0.446; p < 0.002) and maxillary areas (r = 0.422; p < 0.003). These preliminary findings suggest that higher anxiety levels result in poorer clinical performance and can be correlated to temperature variations in certain facial regions.

Evaluación e Intervención Psicofisiológica Térmica de Sesión Única de Estrés Social en Estudiantes de Psicología

El estrés social agudo induce un incremento de la actividad simpática, mientras que la relajación promueve actividad parasimpática. Sin embargo, algunas personas no logran regular su balance autonómico (estrés- relajación), por lo que se propuso evaluar el efecto de una intervención psicofisiológica de sesión única. Participaron estudiantes de Psicología (n= 15) quienes completaron una evaluación psicométrica de medidas de distrés (ansiedad, depresión, estrés postraumático, alexitimia y percepción subjetiva de estrés). Posteriormente se aplicó el Trier Social Stress Test (TSST-Cog), un protocolo conductual para inducir estrés social agudo, seguido de una estrategia de relajación por imaginería guiada (IG). Mediante un diseño de medidas repetidas se registró su temperatura nasal y se comparó la diferencia térmica entre estrés y relajación. Posteriormente, los estudiantes que reportaron un nivel de distrés en las medidas psicométricas se aleatorizaron en dos subgrupos para aplicar un procedimiento de condicionamiento térmico en manos – relajación y un grupo control. Los resultados revelaron una asociación (p< 0.05) entre la temperatura nasal y las medidas de distrés; un decremento de la temperatura nasal post-TSST-Cog (acompañado de un aumento de percepción de estrés) y un incremento térmico (asociado a una disminución de estrés) post-IG existiendo una diferencia estadística entre ambos procedimientos (p< 0.01). Para los participantes con distrés, el procedimiento de condicionamiento térmico logró revertir el descenso de temperatura (p= 0.05), mientras que los estudiantes sin el procedimiento no hubo cambios post-IG (p= 0.15). Se concluye que el uso del protocolo TSST-Relajación puede ser utilizado como un procedimiento diagnóstico del funcionamiento emocional, así como la promoción del condicionamiento térmico en quienes no se beneficien de los procedimientos de relajación tradicionales.

Evaluación e Intervención Psicofisiológica Térmica de Sesión Única de Estrés Social en Estudiantes de Psicología

El estrés social agudo induce un incremento de la actividad simpática, mientras que la relajación promueve actividad parasimpática. Sin embargo, algunas personas no logran regular su balance autonómico (estrés- relajación), por lo que se propuso evaluar el efecto de una intervención psicofisiológica de sesión única. Participaron estudiantes de Psicología (n= 15) quienes completaron una evaluación psicométrica de medidas de distrés (ansiedad, depresión, estrés postraumático, alexitimia y percepción subjetiva de estrés). Posteriormente se aplicó el Trier Social Stress Test (TSST-Cog), un protocolo conductual para inducir estrés social agudo, seguido de una estrategia de relajación por imaginería guiada (IG). Mediante un diseño de medidas repetidas se registró su temperatura nasal y se comparó la diferencia térmica entre estrés y relajación. Posteriormente, los estudiantes que reportaron un nivel de distrés en las medidas psicométricas se aleatorizaron en dos subgrupos para aplicar un procedimiento de condicionamiento térmico en manos – relajación y un grupo control. Los resultados revelaron una asociación (p< 0.05) entre la temperatura nasal y las medidas de distrés; un decremento de la temperatura nasal post-TSST-Cog (acompañado de un aumento de percepción de estrés) y un incremento térmico (asociado a una disminución de estrés) post-IG existiendo una diferencia estadística entre ambos procedimientos (p< 0.01). Para los participantes con distrés, el procedimiento de condicionamiento térmico logró revertir el descenso de temperatura (p= 0.05), mientras que los estudiantes sin el procedimiento no hubo cambios post-IG (p= 0.15). Se concluye que el uso del protocolo TSST-Relajación puede ser utilizado como un procedimiento diagnóstico del funcionamiento emocional, así como la promoción del condicionamiento térmico en quienes no se beneficien de los procedimientos de relajación tradicionales.

Revista Digital Internacional de Psicología y Ciencia Social | Volumen 6 | Número 2 | Julio-Diciembre 2020 | Desafíos contemporáneos en educación y salud

El actual número se titula “Desafíos contemporáneos en educación y salud” y nos permite conocer acerca de los temas de interés para algunos investigadores y la manera en que se han abordado para su estudio. De manera coincidente este número ha sido trabajado en tiempos de pandemia, donde también nos hemos enfrentado a retos ante las nuevas condiciones para adaptarnos a nuevas formas de trabajo, mayoritariamente a distancia y por ahora a lo que parece ser “la nueva normalidad”, y con ello, en algunos meses, estaremos leyendo trabajos que surjan de estos momentos históricos mundiales, sus implicaciones, problemas, alcances y tal vez sus repercusiones.

Facilitating the Child–Robot Interaction by Endowing the Robot with the Capability of Understanding the Child Engagement: The Case of Mio Amico Robot

Social Robots (SRs) are substantially becoming part of modern society, given their frequent use in many areas of application including education, communication, assistance, and entertainment. The main challenge in human–robot interaction is in achieving human-like and affective interaction between the two groups. This study is aimed at endowing SRs with the capability of assessing the emotional state of the interlocutor, by analyzing his/her psychophysiological signals. The methodology is focused on remote evaluations of the subject’s peripheral neuro-vegetative activity by means of thermal infrared imaging. The approach was developed and tested for a particularly challenging use case: the interaction between children and a commercial educational robot, Mio Amico Robot, produced by LiscianiGiochi©. The emotional state classified from the thermal signal analysis was compared to the emotional state recognized by a facial action coding system. The proposed approach was reliable and accurate and favored a personalized and improved interaction of children with SRs.

Thermal Infrared Imaging to Evaluate Emotional Competences in Nursing Students: A First Approach through a Case Study

During university studies of nursing, it is important to develop emotional skills for their impact on academic performance and the quality of patient care. Thermography is a technology that could be applied during nursing training to evaluate emotional skills. The objective is to evaluate the effect of thermography as the tool for monitoring and improving emotional skills in student nurses through a case study. The student was subjected to different emotions. The stimuli applied were video and music. The process consisted of measuring the facial temperatures during each emotion and stimulus in three phases: acclimatization, stimulus, and response. Thermographic data acquisition was performed with an FLIR E6 camera. The analysis was complemented with the environmental data (temperature and humidity). With the video stimulus, the start and final forehead temperature from testing phases, showed a different behavior between the positive (joy: 34.5 °C-34.5 °C) and negative (anger: 36.1 °C-35.1 °C) emotions during the acclimatization phase, different from the increase experienced in the stimulus (joy: 34.7 °C-35.0 °C and anger: 35.0 °C-35.0 °C) and response phases (joy: 35.0 °C-35.0 °C and anger: 34.8 °C-35.0 °C). With the music stimulus, the emotions showed different patterns in each phase (joy: 34.2 °C-33.9 °C-33.4 °C and anger: 33.8 °C-33.4 °C-33.8 °C). Whenever the subject is exposed to a stimulus, there is a thermal bodily response. All of the facial areas follow a common thermal pattern in response to the stimulus, with the exception of the nose. Thermography is a technique suitable for the stimulation practices in emotional skills, given that it is non-invasive, it is quantifiable, and easy to access.

FLIR vs SEEK thermal cameras in biomedicine: comparative diagnosis through infrared thermography

BACKGROUND

In biomedicine, infrared thermography is the most promising technique among other conventional methods for revealing the differences in skin temperature, resulting from the irregular temperature dispersion, which is the significant signaling of diseases and disorders in human body. Given the process of detecting emitted thermal radiation of human body temperature by infrared imaging, we, in this study, present the current utility of thermal camera models namely FLIR and SEEK in biomedical applications as an extension of our previous article.

RESULTS

The most significant result is the differences between image qualities of the thermograms captured by thermal camera models. In other words, the image quality of the thermal images in FLIR One is higher than SEEK Compact PRO. However, the thermal images of FLIR One are noisier than SEEK Compact PRO since the thermal resolution of FLIR One is 160 × 120 while it is 320 × 240 in SEEK Compact PRO.

CONCLUSION

Detecting and revealing the inhomogeneous temperature distribution on the injured toe of the subject, we, in this paper, analyzed the imaging results of two different smartphone-based thermal camera models by making comparison among various thermograms. Utilizing the feasibility of the proposed method for faster and comparative diagnosis in biomedical problems is the main contribution of this study.

Vascular response to social cognitive performance measured by infrared thermography: A translational study from mouse to man

To assess complex social recognition in mice, we previously developed the SocioBox paradigm. Unexpectedly, 4 weeks after performing in the SocioBox, mice displayed robust social avoidance during Y-maze sociability testing. This unique "sociophobia" acquisition could be documented in independent cohorts. We therefore employed infrared thermography as a non-invasive method of stress-monitoring during SocioBox testing (presentation of five other mice) versus empty box. A higher Centralization Index (body/tail temperature) in the SocioBox correlated negatively with social recognition memory and, after 4 weeks, with social preference in the Y-maze. Assuming that social stimuli might be associated with characteristic thermo-responses, we exposed healthy men (N = 103) with a comparably high intelligence level to a standardized test session including two cognitive tests with or without social component (face versus pattern recognition). In some analogy to the Centralization Index (within-subject measure) used in mice, the Reference Index (ratio nose/malar cheek temperature) was introduced to determine the autonomic facial response/flushing during social recognition testing. Whereas cognitive performance and salivary cortisol were comparable across human subjects and tests, the Face Recognition Test was associated with a characteristic Reference Index profile. Infrared thermography may have potential for discriminating disturbed social behaviors.

A Modular System for Detection, Tracking and Analysis of Human Faces in Thermal Infrared Recordings

We present a system that utilizes a range of image processing algorithms to allow fully automated thermal face analysis under both laboratory and real-world conditions. We implement methods for face detection, facial landmark detection, face frontalization and analysis, combining all of these into a fully automated workflow. The system is fully modular and allows implementing own additional algorithms for improved performance or specialized tasks. Our suggested pipeline contains a histogtam of oriented gradients support vector machine (HOG-SVM) based face detector and different landmark detecion methods implemented using feature-based active appearance models, deep alignment networks and a deep shape regression network. Face frontalization is achieved by utilizing piecewise affine transformations. For the final analysis, we present an emotion recognition system that utilizes HOG features and a random forest classifier and a respiratory rate analysis module that computes average temperatures from an automatically detected region of interest. Results show that our combined system achieves a performance which is comparable to current stand-alone state-of-the-art methods for thermal face and landmark datection and a classification accuracy of 65.75% for four basic emotions.

Autonomic impairment in Alzheimer's disease is revealed by complexity analysis of functional thermal imaging signals during cognitive tasks

OBJECTIVE

Alzheimer's disease (AD) is characterized by progressive memory failures and visuospatial impairment. Moreover, AD can be accompanied by autonomic system alterations, which, among other impacts, affect thermoregulatory activity. We here investigate differences in autonomic activity between AD patients and healthy controls (HC), employing a complexity analysis of functional infrared imaging (fIRI) data acquired at rest and during the execution of clinical cognitive and mnemonic tests.

APPROACH

fIRI allows for contactless monitoring of autonomic activity and its thermoregulatory expression without interfering with the psychophysiological state of the subject, preserving free interaction with the doctor. The signal complexity analysis, based on the sample entropy, was compared to a standard frequency-based analysis of autonomic-related signals.

MAIN RESULTS

AD patients exhibited lower complexity of fIRI signals during the tests, which could be indicative of a stronger sympathetic activity with respect to HC. No significant effects were found at rest. No differences were found on employing frequency-based analysis.

SIGNIFICANCE

This study confirms that AD patients may exhibit peculiar autonomic responses associated with the execution of cognitive tasks that can be measured through fIRI. Moreover, these responses could be highlighted by a nonlinear metric of signal predictability such as the sample entropy establishing autonomic impairment of AD patients.

Modelling Impulse Response Function of Functional Infrared Imaging for General Linear Model Analysis of Autonomic Activity

Functional infrared imaging (fIRI) is a validated procedure to infer autonomic arousal. Currently, fIRI signals are analysed through descriptive metrics, such as average temperature changes in a region of interest (ROI). However, the employment of mathematical models could provide a powerful tool for the accurate identification of autonomic activity and investigation of the mechanisms underlying autonomic arousal. A linear temporal statistical model such as the general linear model (GLM) is particularly suited for its simplicity and direct interpretation. In order to apply the GLM, the thermal response linearity and time-invariance of fIRI have to be demonstrated, and the thermal impulse response (TIR) needs to be characterized. In this study, the linearity and time-invariance of the thermal response to sympathetic activating stimulation were demonstrated, and the TIR for employment of the GLM was characterized. The performance of the GLM-fIRI was evaluated by comparison with the GLM applied on synchronous measurements of the skin conductance response (SCR). In fact, the GLM-SCR is a validated procedure to estimate autonomic arousal. Assuming the GLM-SCR as the gold standard approach, a GLM-fIRI sensitivity and specificity of 86.4% and 75.9% were obtained. The GLM-fIRI may allow increased performances in the evaluation of autonomic activity and a broader range of application of fIRI in both research and clinical settings for the assessment of psychophysiological and psychopathological states.

Comparison and Efficacy of Synergistic Intelligent Tutoring Systems with Human Physiological Response

The analysis of physiological signals is ubiquitous in health and medical diagnosis as a primary tool for investigation and inquiry. Physiological signals are now being widely used for psychological and social fields. They have found promising application in the field of computer-based learning and tutoring. Intelligent Tutoring Systems (ITS) is a fast-paced growing field which deals with the design and implementation of customized computer-based instruction and feedback methods without human intervention. This paper introduces the key concepts and motivations behind the use of physiological signals. It presents a detailed discussion and experimental comparison of ITS. The synergism of ITS and physiological signals in automated tutoring systems adapted to the learner's emotions and mental states are presented and compared. The insights are developed, and details are presented. The accuracy and classification methods of existing systems are highlighted as key areas of improvement. High-precision measurement systems and neural networks for machine-learning classification are deemed prospective directions for future improvements to existing systems.

Automated Region Extraction from Thermal Images for Peripheral Vascular Disease Monitoring

This work develops a method for automatically extracting temperature data from prespecified anatomical regions of interest from thermal images of human hands, feet, and shins for the monitoring of peripheral arterial disease in diabetic patients. Binarisation, morphological operations, and geometric transformations are applied in cascade to automatically extract the required data from 44 predefined regions of interest. The implemented algorithms for region extraction were tested on data from 395 participants. A correct extraction in around 90% of the images was achieved. The process of automatically extracting 44 regions of interest was performed in a total computation time of approximately 1 minute, a substantial improvement over 10 minutes it took for a corresponding manual extraction of the regions by a trained individual. Interrater reliability tests showed that the automatically extracted ROIs are similar to those extracted by humans with minimal temperature difference. This set of algorithms provides a sufficiently accurate and reliable method for temperature extraction from thermal images at par with human raters with a tenfold reduction in time requirement. The automated process may replace the manual human extraction, leading to a faster process, making it feasible to carry out large-scale studies and to increase the regions of interest with minimal cost. The code for the developed algorithms, to extract the 44 ROIs from thermal images of hands, feet, and shins, has been made available online in the form of MATLAB functions and can be accessed from http://www.um.edu.mt/cbc/tipmid.

V-RBNN Based Small Drone Detection in Augmented Datasets for 3D LADAR System

A common countermeasure to detect threatening drones is the electro-optical infrared (EO/IR) system. However, its performance is drastically reduced in conditions of complex background, saturation and light reflection. 3D laser sensor LiDAR is used to overcome the problems of 2D sensors like EO/IR, but it is not enough to detect small drones at a very long distance because of low laser energy and resolution. To solve this problem, A 3D LADAR sensor is under development. In this work, we study the detection methodology adequate to the LADAR sensor which can detect small drones at up to 2 km. First, a data augmentation method is proposed to generate a virtual target considering the laser beam and scanning characteristics, and to augment it with the actual LADAR sensor data for various kinds of tests before full hardware system developed. Second, a detection algorithm is proposed to detect drones using voxel-based background subtraction and variable radially bounded nearest neighbor (V-RBNN) method. The results show that 0.2 m L2 distance and 60% expected average overlap (EAO) indexes are satisfied for the required specification to detect 0.3 m size of small drones.