Nose Heat: Exploring Stress-induced Nasal Thermal Variability through Mobile Thermal Imaging

Cho, Youngjun; Bianchi-Berthouze, Nadia; Oliveira, Manuel; Holloway, Catherine; Julier, Simon

doi:10.1109/acii.2019.8925453

Cited by 24 publications

(29 citation statements)

References 24 publications

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“…Nose Temperature: The human internal temperature correlates with physical and psychological states [33].The reaction to stimuli increases or decreases blood ow which leads to variation in skin temperature. The variability in skin temperature is often measured with thermal imaging [34].…”

Section: Introductionmentioning

confidence: 99%

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An Adaptive Human Sensor Framework for Human-Robot Collaboration

Buerkle

Matharu

Al-Yacoub

et al. 2021

Preprint

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Manufacturing challenges are increasing the demands for more agile and dexterous means of production. At the same time, these systems aim to maintain or even increase productivity. The challenges risen from these developments can be tackled through Human-Robot Collaboration (HRC). HRC requires effective task distribution according to each parties’ distinctive strengths, which is envisioned to generate synergetic effects. To enable a seamless collaboration, the human and robot require a mutual awareness, which is challenging, due to the human and robot “speaking” different languages as in analogue and digital. Thus, this challenge can be addressed by equipping the robot with a model of the human. Despite a range of models being available, data-driven models of the human are still at an early stage. This paper proposes an adaptive human sensor framework, which incorporates objective, subjective, and physiological metrics, as well as associated Machine Learning. Thus, it is envisioned to adapt to the uniqueness and dynamic nature of human behavior. To test the framework, a validation experiment was performed, including 18 participants, which aims to predict Perceived Workload during two scenarios, namely a manual and an HRC assembly task. Perceived Workloads are described to have a substantial impact on a human operator’s task performance. Throughout the experiment physiological data from an electroencephalogram (EEG), an electrocardiogram (ECG), and respiration sensor was collected and interpreted. For subjective metrics, the standardized NASA Task Load Index was used. Objective metrics included task completion time and number of errors/assistance requests. Overall, the framework revealed a promising potential towards an adaptive behavior, which is ultimately envisioned to enable a more effective HRC.

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Section: Introductionmentioning

confidence: 99%

“…Among other facial regions, the nose tip is regarded as providing the most consistent indications of stress. Once stressful conditions apply, the nasal temperature decreases [33].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Human Sensor Framework for Human-Robot Collaboration

Buerkle

Matharu

Al-Yacoub

et al. 2021

Preprint

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“…To overcome these limitations, infrared thermography (IRT) has recently been used to measure the breathing characteristics of cattle [ 5 , 6 ]. In fact, IRT has been used not only to measure breathing but also in various fields such as stress assessment by eye temperature [ 7 , 8 , 9 ] or nose temperature [ 10 ]. However, the previous methods have utilized it in a limited way.…”

Section: Introductionmentioning

confidence: 99%

Breathing Pattern Analysis in Cattle Using Infrared Thermography and Computer Vision

Kim

Hidaka

2021

Animals

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Breathing patterns can be considered a vital sign providing health information. Infrared thermography is used to evaluate breathing patterns because it is non-invasive. Our study used not only sequence temperature data but also RGB images to gain breathing patterns in cattle. Mask R-CNN was used to detect the ROI (region of interest, nose) in the cattle RGB images. Mask segmentation from the ROI detection was applied to the corresponding temperature data. Finally, to visualize the breathing pattern, we calculated the temperature values in the ROI by averaging all temperature values in the ROI. The results in this study show 76% accuracy with Mask R-CNN in detecting cattle noses. With respect to the temperature calculation methods, the averaging method showed the most appropriate breathing pattern compared to other methods (maximum temperature in the ROI and integrating all temperature values in the ROI). Finally, we compared the breathing pattern from the averaging method and that from the thermal image observation and found them to be highly correlated (R2 = 0.91). This method is not labor-intensive, can handle big data, and is accurate. In addition, we expect that the characteristics of the method might enable the analysis of temperature data from various angles.

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“…the temperature of a particular regions of interest, such as the eyes, ears or tails. Moreover, it has already been shown that the equipment needed to collect such non-invasive data can be mobilized, increasing the feasibility to employ such equipment within a larger industrial setting ( 40 , 50 , 76 ). More and more studies demonstrate the applications of automated systems within a farm setting, highlighting the potential of this field, ranging from surveying pig enclosure usage to identifying heat stress ( 77 ) to infrared thermography to track cattle health ( 76 ).…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, it has already been shown that the equipment needed to collect such non-invasive data can be mobilized, increasing the feasibility to employ such equipment within a larger industrial setting ( 40 , 50 , 76 ). More and more studies demonstrate the applications of automated systems within a farm setting, highlighting the potential of this field, ranging from surveying pig enclosure usage to identifying heat stress ( 77 ) to infrared thermography to track cattle health ( 76 ). Human affective research is only just starting to understand the importance of multimodal approaches, and this knowledge should be used to develop models for non-human animals, too.…”

Section: Introductionmentioning

confidence: 99%

The Use of Artificial Intelligence in Assessing Affective States in Livestock

Neethirajan

2021

Front. Vet. Sci.

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In order to promote the welfare of farm animals, there is a need to be able to recognize, register and monitor their affective states. Numerous studies show that just like humans, non-human animals are able to feel pain, fear and joy amongst other emotions, too. While behaviorally testing individual animals to identify positive or negative states is a time and labor consuming task to complete, artificial intelligence and machine learning open up a whole new field of science to automatize emotion recognition in production animals. By using sensors and monitoring indirect measures of changes in affective states, self-learning computational mechanisms will allow an effective categorization of emotions and consequently can help farmers to respond accordingly. Not only will this possibility be an efficient method to improve animal welfare, but early detection of stress and fear can also improve productivity and reduce the need for veterinary assistance on the farm. Whereas affective computing in human research has received increasing attention, the knowledge gained on human emotions is yet to be applied to non-human animals. Therefore, a multidisciplinary approach should be taken to combine fields such as affective computing, bioengineering and applied ethology in order to address the current theoretical and practical obstacles that are yet to be overcome.

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Nose Heat: Exploring Stress-induced Nasal Thermal Variability through Mobile Thermal Imaging

Cited by 24 publications

References 24 publications

An Adaptive Human Sensor Framework for Human-Robot Collaboration

An Adaptive Human Sensor Framework for Human-Robot Collaboration

Breathing Pattern Analysis in Cattle Using Infrared Thermography and Computer Vision

The Use of Artificial Intelligence in Assessing Affective States in Livestock

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