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Stress remains a significant social problem for individuals in modern societies. This paper presents a machine learning approach for the automatic detection of stress of people in a social situation by combining two sensor systems that capture physiological and social responses. We compare the performance using different classifiers including support vector machine, AdaBoost, and $k$-nearest neighbor. Our experimental results show that by combining the measurements from both sensor systems, we could accurately discriminate between stressful and neutral situations during a controlled Trier social stress test (TSST). Moreover, this paper assesses the discriminative ability of each sensor modality individually and considers their suitability for real-time stress detection. Finally, we present an study of the most discriminative features for stress detection.

There are a substantial number of empirical relations that began with the identification of a pattern in data; were shown to have a terse power-law description; were interpreted using existing theory; reached the level of "law" and given a name; only to be subsequently fade away when it proved impossible to connect the "law" with a larger body of theory and/or data. Various forms of allometry relations (ARs) have followed this path. The ARs in biology are nearly two hundred years old and those in ecology, geophysics, physiology and other areas of investigation are not that much younger. In general if X is a measure of the size of a complex host network and Y is a property of a complex subnetwork embedded within the host network a theoretical AR exists between the two when Y = aX^b. We emphasize that the reductionistic models of AR interpret X and Y as dynamic variables, albeit the ARs themselves are explicitly time independent even though in some cases the parameter values change over time. On the other hand, the phenomenological models of AR are based on the statistical analysis of data and interpret X and Y as averages to yield the empirical AR: 〈Y〉 = a〈X〉^b. Modern explanations of AR begin with the application of fractal geometry and fractal statistics to scaling phenomena. The detailed application of fractal geometry to the explanation of theoretical ARs in living networks is slightly more than a decade old and although well received it has not been universally accepted. An alternate perspective is given by the empirical AR that is derived using linear regression analysis of fluctuating data sets. We emphasize that the theoretical and empirical ARs are not the same and review theories "explaining" AR from both the reductionist and statistical fractal perspectives. The probability calculus is used to systematically incorporate both views into a single modeling strategy. We conclude that the empirical AR is entailed by the scaling behavior of the probability density, which is derived using the probability calculus.

We propose in this paper a novel architecture for human activity monitoring, following conceptual, technical and experimental claims. From a conceptual viewpoint, we propose to approach the interpretation of sensor data as embedded into a multidimensional frame involving functional and non-functional requirements. Functional requirements involve considering the monitored person's specificities as well as the task to be performed. Non-functional requirements qualify the system activity. This frame of interpretation is continuously refined, to cope with evolving situations or expectations from the Observer. From a technical viewpoint, we propose to develop a multi-Agent architecture as a means for dependable, flexible monitoring. This paradigm allows to handle multiple, heterogeneous entities in a unified way. The Agents process incoming data with a dynamic population of hypotheses on several abstraction levels. This reasoning is abductive and fuzzy in nature. From the experimental viewpoint, we propose a dedicated evaluation approach to estimate the interpretative process unfolding. Functional and non-functional properties are presented to discuss the system's effectiveness, informativeness, sensitivity, efficiency and robustness, some of which are supported by qualitative, analytical discussions, others by quantitative measures.

Geoffrey Ball and his Innovation: VIBRANT SOUNDBRIDGE Hearing Implant.

Interviews with Nobel Laureates in Physiology or Medicine.

Talk about Over-the-Counter (OTC) Medicines and Self-Care.

For many aquatic animals, the electrosense is an important sensory system used to detect prey or conspecifics at short to medium range and for long-range orientation. Passive electroreceptive animals sense the minute electric fields of animate and inanimate sources and it has been thought that they are most sensitive to sources that modulate the field around a few Hertz. Our data on the properties of the electrosensory system in the paddlefish reveal that the firing rate of electrosensory brain stem neurons represents the first derivative of the stimulus, i.e. the rate of change in intensity of an electric field. Furthermore, the responses to several non-periodic stimuli suggest that the electrosensory system monitors changes in field intensity caused by the relative movement between source and receiver and converts spatial field structure into its time derivative form. This new interpretation solves a number of contradictions between behavioural observations and electrophysiological studies on the electrosensory system of vertebrates.

Severe Acute Respiratory Syndrome (SARS) is a highly infectious disease caused by a coronavirus. Screening to detect potential SARS infected subject with elevated body temperature plays an important role in preventing the spread of SARS. The use of infrared (IR) thermal imaging cameras has thus been proposed as a non-invasive, speedy, cost-effective and fairly accurate means for mass blind screening of potential SARS infected persons. Infrared thermography provides a digital image showing temperature patterns. This has been previously utilized in the detection of inflammation and nerve dysfunctions. It is believed that IR cameras may potentially be used to detect subjects with fever, the cardinal symptom of SARS and avian influenza. The accuracy of the infrared system can, however, be affected by human, environmental, and equipment variables. It is also limited by the fact that the thermal imager measures the skin temperature and not the body core temperature. Thus, the use of IR thermal systems at various checkpoints for mass screening of febrile persons is scientifically unjustified such as what is the false negative rate and most importantly not to create false sense of security.

This paper aims to study the effectiveness of infrared systems for its application in mass blind screening to detect subjects with elevated body temperature. For this application, it is critical for thermal imagers to be able to identify febrile from normal subjects accurately. Minimizing the number of false positive and false negative cases improves the efficiency of the screening stations. False negative results should be avoided at all costs, as letting a SARS infected person through the screening process may result in potentially catastrophic results. Hitherto, there is lack of empirical data in correlating facial skin with body temperature. The current work evaluates the correlations (and classification) between the facial skin temperatures to the aural temperature using the artificial neural network approach to confirm the suitability of the thermal imagers for human temperature screening. We show that the Train Back Propagation and Kohonen self-organizing map (SOM) can form an opinion about the type of network that is better to complement thermogram technology in fever diagnosis to drive a better parameters for reducing the size of the neural network classifier while maintaining good classification accuracy.

The present approach of aging and time irreversibility is a consequence of the theory of functional organization that I have developed and presented over recent years (see e.g., Ref. 11). It is based on the effect of physically small and numerous perturbations known as fluctuations, of structural units on the dynamics of the biological system during its adult life. Being a highly regulated biological system, a simple realistic hypothesis, the time-optimum regulation between the levels of organization, leads to the existence of an internal age for the biological system, and time-irreversibility associated with aging. Thus, although specific genes are controlling aging, time-irreversibility of the system may be shown to be due to the degradation of physiological functions. In other words, I suggest that for a biological system, the nature of time is specific and is an expression of the highly regulated integration. An internal physiological age reflects the irreversible course of a living organism towards death because of the irreversible course of physiological functions towards dysfunction, due to the irreversible changes in the regulatory processes. Following the works of Prigogine and his colleagues in physics, and more generally in the field of non-integrable dynamical systems (theorem of Poincaré–Misra), I have stated this problem in terms of the relationship between the macroscopic irreversibility of the functional organization and the basic mechanisms of regulation at the lowest "microscopic" level, i.e., the molecular, lowest level of organization. The neuron-neuron elementary functional interaction is proposed as an illustration of the method to define aging in the nervous system.

The review deals with the morphology, physiology, topography, and central projections of direction-selective cells of the accessory optic system in vertebrates.

This is a model of consciousness. The hard problem of consciousness, what it feels like, is answered. The work builds on medical research analyzing the source and mechanisms associated with our feelings. It goes further by describing a generic model with wide applicability. The model is fully consistent with medical pathways in humans, but easily extends to animals and artificial intelligence (AI). The essence of the model is the interplay between associative memory and physiology. The model is a clear and concrete counterexample to the famous philosophical objections to a scientific explanation.