Emotion Evaluation and Response Slowing in a Non-Human Primate: New Directions for Cognitive Bias Measures of Animal Emotion?

The cognitive bias model of animal welfare assessment is informed by studies with humans demonstrating that the interaction between emotion and cognition can be detected using laboratory tasks. A limitation of cognitive bias tasks is the amount of training required by animals prior to testing. A potential solution is to use biologically relevant stimuli that trigger innate emotional responses. Here; we develop a new method to assess emotion in rhesus macaques; informed by paradigms used with humans: emotional Stroop; visual cueing and; in particular; response slowing. In humans; performance on a simple cognitive task can become impaired when emotional distractor content is displayed. Importantly; responses become slower in anxious individuals in the presence of mild threat; a pattern not seen in non-anxious individuals; who are able to effectively process and disengage from the distractor. Here; we present a proof-of-concept study; demonstrating that rhesus macaques show slowing of responses in a simple touch-screen task when emotional content is introduced; but only when they had recently experienced a presumably stressful veterinary inspection. Our results indicate the presence of a subtle “cognitive freeze” response; the measurement of which may provide a means of identifying negative shifts in emotion in animals

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 182, July 1978

This bibliography lists 165 reports, articles, and other documents introduced into the NASA scientific and technical information system in June 1978

Emotion evaluation and response slowing in a non-human primate: New directions for cognitive bias measures of animal welfare

The cognitive bias model of animal welfare assessment is informed by studies with humans demonstrating that the interaction between emotion and cognition can be detected using laboratory tasks. A limitation of cognitive bias tasks is the amount of training required by animals prior to testing. A potential solution is to use biologically relevant stimuli that trigger innate emotional responses. Here; we develop a new method to assess emotion in rhesus macaques; informed byparadigms used with humans: emotional Stroop; visual cueing and; in particular; response slowing. In humans; performance on a simple cognitive task can become impaired when emotional distractor content is displayed. Importantly; responses become slower in anxious individuals in the presence of mild threat; a pattern not seen in non-anxious individuals; who are able to effectively process and disengage from the distractor. Here; we present a proof-of-concept study; demonstrating that rhesus macaques show slowing of responses in a simple touch-screen task when emotional content is introduced; but only when they had recently experienced a presumably stressful veterinary inspection. Our results indicate the presence of a subtle “cognitive freeze” response; the measurement of which may provide a means of identifying negative shifts in emotion in animals

Automatic nystagmus detection and quantification in long-term continuous eye-movement data

Symptoms of dizziness or imbalance are frequently reported by people over 65. Dizziness is usually episodic and can have many causes, making diagnosis problematic. When it is due to inner-ear malfunctions, it is usually accompanied by abnormal eye-movements called nystagmus. The CAVA (Continuous Ambulatory Vestibular Assessment) device has been developed to provide continuous monitoring of eye-movements to gain insight into the physiological parameters present during a dizziness attack. In this paper, we describe novel algorithms for detecting short periods of artificially induced nystagmus from the long-term eye movement data collected by the CAVA device. In a blinded trial involving 17 healthy subjects, each participant induced nystagmus artificially on up to eight occasions by watching a short video on a VR headset. Our algorithms detected these short periods with an accuracy of 98.77%. Additionally, data relating to vestibular induced nystagmus was collected, analysed and then compared to a conventional technique for assessing nystagmus during caloric testing. The results show that a range of nystagmus can be identified and quantified using computational methods applied to long-term eye-movement data captured by the CAVA device

Towards unravelling the relationship between on-body, environmental and emotion data using sensor information fusion approach

Over the past few years, there has been a noticeable advancement in environmental models and information fusion systems taking advantage of the recent developments in sensor and mobile technologies. However, little attention has been paid so far to quantifying the relationship between environment changes and their impact on our bodies in real-life settings. In this paper, we identify a data driven approach based on direct and continuous sensor data to assess the impact of the surrounding environment and physiological changes and emotion. We aim at investigating the potential of fusing on-body physiological signals, environmental sensory data and on-line self-report emotion measures in order to achieve the following objectives: (1) model the short term impact of the ambient environment on human body, (2) predict emotions based on-body sensors and environmental data. To achieve this, we have conducted a real-world study ‘in the wild’ with on-body and mobile sensors. Data was collected from participants walking around Nottingham city centre, in order to develop analytical and predictive models. Multiple regression, after allowing for possible confounders, showed a noticeable correlation between noise exposure and heart rate. Similarly, UV and environmental noise have been shown to have a noticeable effect on changes in ElectroDermal Activity (EDA). Air pressure demonstrated the greatest contribution towards the detected changes in body temperature and motion. Also, significant correlation was found between air pressure and heart rate. Finally, decision fusion of the classification results from different modalities is performed. To the best of our knowledge this work presents the first attempt at fusing and modelling data from environmental and physiological sources collected from sensors in a real-world setting

Smart system for children's chronic illness monitoring

[EN] Sick children need a continuous monitoring, but this involves high costs for the government and for the parents. The use of information and communication technologies (ICT) jointly with artificial intelligence and smart devices can reduce these costs, help the children and assist their parents. This paper presents a smart architecture for children's chronic illness monitoring that will let the caregivers (parents, teachers and doctors) to remotely monitor the health of the children based on the sensors embedded in the smartphones and smart wearable devices. The proposed architecture includes a smart algorithm developed to intelligently detect if a parameter has exceeded a threshold, thus it may imply an emergency or not. To check the correct operation of this system, we have developed a small wearable device that is able to measure the heart rate and the body temperature. We have designed a secure mechanism to stablish a Bluetooth connection with the smartphone. In addition, the system is able to perform the data fusion in both the information packetizing process, which contributes to improve the protocol performance, and in the measured values combination, where it is used a stochastic approach. As a result, our system can fusion data from different sensors in real-time and detect automatically strange situations for sending a warning to the caregivers. Finally, the consumed bandwidth and battery autonomy of the developed device have been measured.This work has been partially supported by the "Ministerio de EducaciOn, Cultura y Deporte", through the "Ayudas para contratos predoctorales de Formacion del Profesorado Universitario FPU (Convocatoria 2014)". Grant number FPU14/02953.Sendra, S.; Parra-Boronat, L.; Lloret, J.; Tomás Gironés, J. (2018). Smart system for children's chronic illness monitoring. Information Fusion. 40:76-86. https://doi.org/10.1016/j.inffus.2017.06.002S76864

Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 291)

This bibliography lists 131 reports, articles and other documents introduced into the NASA scientific and technical information system in November 1986

Sensor Technologies to Manage the Physiological Traits of Chronic Pain: A Review

Non-oncologic chronic pain is a common high-morbidity impairment worldwide and acknowledged as a condition with significant incidence on quality of life. Pain intensity is largely perceived as a subjective experience, what makes challenging its objective measurement. However, the physiological traces of pain make possible its correlation with vital signs, such as heart rate variability, skin conductance, electromyogram, etc., or health performance metrics derived from daily activity monitoring or facial expressions, which can be acquired with diverse sensor technologies and multisensory approaches. As the assessment and management of pain are essential issues for a wide range of clinical disorders and treatments, this paper reviews different sensor-based approaches applied to the objective evaluation of non-oncological chronic pain. The space of available technologies and resources aimed at pain assessment represent a diversified set of alternatives that can be exploited to address the multidimensional nature of pain.Ministerio de Economía y Competitividad (Instituto de Salud Carlos III) PI15/00306Junta de Andalucía PIN-0394-2017Unión Europea "FRAIL