Validation of wireless sensors for psychophysiological studies

James One (MindProber Labs) is a wireless psychophysiological device comprising two sensors: one measuring electrodermal activity (EDA), the other photoplethysmography (PPG). This paper reports the validation of James One's EDA sensor by comparing its signal against a research grade polygraph. Twenty participants were instructed to perform breathing exercises to elicit the modulation of EDA and heart rate, while the physiological signal was captured simultaneously on James One and a Biopac MP36. The resulting EDA and PPG records collected from both systems were comprehensively compared. Results suggest that James One captures EDA signal with a quality comparable to a research grade equipment, this constituting a reliable means of capturing data while minimizing setup time and intrusiveness.P.S.M. was supported by an FCT fellowship grant (PhD-iHES program) with the reference PDE/BDE/113601/2015

Optimization of the position of single-lead wireless sensor with low electrodes separation distance for ECG-derived respiration

A classical method for estimation of respiratory information from electrocardiogram (ECG), called ECG - derived respiration (EDR), is using flexible electrodes located at standard electrocardiography positions. This work introduces an alternative approach suitable for miniaturized sensors with low inter-electrode separation and electrodes fixed to the sensor encapsulation. Application of amplitude EDR algorithm on single-lead wireless sensor system with optimized electrode positions shows results comparable with standard robust systems. The modified method can be applied in daily physiological monitoring, in sleep studies or implemented in smart clothes when standard respiration techniques are not suitable

Continuous electronic data capture of physiology, behavior and experience in real life: towards ecological momentary assessment of emotion

Emotions powerfully influence our physiology, behavior, and experience. A comprehensive assessment of affective states in health and disease would include responses from each of these domains in real life. Since no single physiologic parameter can index emotional states unambiguously, a broad assessment of physiologic responses is desirable. We present a recently developed system, the LifeShirt, which allows reliable ambulatory monitoring of a wide variety of cardiovascular, respiratory, metabolic, motor-behavioral, and experiential responses. The system consists of a garment with embedded inductive plethysmography and other sensors for physiologic data recording and a handheld computer for input of experiential data via touch screen. Parameters are extracted offline using sophisticated analysis and display software. The device is currently used in clinical studies and to monitor effects of physical and emotional stress in naturalistic settings. Further development of signal processing and pattern recognition algorithms will enhance computerized identification of type and extent of physical and emotional activatio

Sensing a live audience.

Psychophysiological measurement has the potential to play an important role in audience research. Currently, such research is still in its infancy and it usually involves collecting data in the laboratory, where during each experimental session one individual watches a video recording of a performance. We extend the experimental paradigm by simultaneously measuring Galvanic Skin Response (GSR) of a group of participants during a live performance. GSR data were synchronized with video footage of performers and audience. In conjunction with questionnaire data, this enabled us to identify a strongly correlated main group of participants, describe the nature of their theatre experience and map out a minute-by-minute unfolding of the performance in terms of psycho-physiological engagement. The benefits of our approach are twofold. It provides us a robust and accurate mechanism for assessing a performance. Moreover, our infrastructure can enable, in the future, real-time feedback from remote audiences for online performances. We are currently scaling up the system allowing for simultaneous GSR measurement of larger audiences

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

Multimedia sensors embedded in smartphones for ambient assisted living and e-health

The final publication is available at link.springer.com[EN] Nowadays, it is widely extended the use of smartphones to make human life more comfortable. Moreover, there is a special interest on Ambient Assisted Living (AAL) and e-Health applications. The sensor technology is growing and amount of embedded sensors in the smartphones can be very useful for AAL and e-Health. While some sensors like the accelerometer, gyroscope or light sensor are very used in applications such as motion detection or light meter, there are other ones, like the microphone and camera which can be used as multimedia sensors. This paper reviews the published papers focused on showing proposals, designs and deployments of that make use of multimedia sensors for AAL and e-health. We have classified them as a function of their main use. They are the sound gathered by the microphone and image recorded by the camera. 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Psychophysiological Sensing and State Classification for Attention Management in Commercial Aviation

Attention-related human performance limiting states (AHPLS) can cause pilots to lose airplane state awareness (ASA), and their detection is important to improving commercial aviation safety. The Commercial Aviation Safety Team found that the majority of recent international commercial aviation accidents attributable to loss of control inflight involved flight crew loss of airplane state awareness, and that distraction of various forms was involved in all of them. Research on AHPLS, including channelized attention, diverted attention, startle / surprise, and confirmation bias, has been recommended in a Safety Enhancement (SE) entitled "Training for Attention Management." To accomplish the detection of such cognitive and psychophysiological states, a broad suite of sensors has been implemented to simultaneously measure their physiological markers during high fidelity flight simulation human subject studies. Pilot participants were asked to perform benchmark tasks and experimental flight scenarios designed to induce AHPLS. Pattern classification was employed to distinguish the AHPLS induced by the benchmark tasks. Unimodal classification using pre-processed electroencephalography (EEG) signals as input features to extreme gradient boosting, random forest and deep neural network multiclass classifiers was implemented. Multi-modal classification using galvanic skin response (GSR) in addition to the same EEG signals and using the same types of classifiers produced increased accuracy with respect to the unimodal case (90 percent vs. 86 percent), although only via the deep neural network classifier. These initial results are a first step toward the goal of demonstrating simultaneous real time classification of multiple states using multiple sensing modalities in high-fidelity flight simulators. This detection is intended to support and inform training methods under development to mitigate the loss of ASA and thus reduce accidents and incidents

Towards a NeuroIS Research Methodology: Intensifying the Discussion on Methods, Tools, and Measurement

The genesis of the Neuro-Information Systems (NeuroIS) field took place in 2007. Since then, a considerable number of IS scholars and academics from related disciplines have started to use theories, methods, and tools from neuroscience and psychophysiology to better understand human cognition, emotion, and behavior in IS contexts, and to develop neuro-adaptive information systems (i.e., systems that recognize the physiological state of the user and that adapt, based on that information, in real-time). However, because the NeuroIS field is still in a nascent stage, IS scholars need to become familiar with the methods, tools, and measurements that are used in neuroscience and psychophysiology. Against the background of the increased importance of methodological discussions in the NeuroIS field, the Journal of the Association for Information Systems published a special issue call for papers entitled “Methods, tools, and measurement in NeuroIS research” in 2012. We, the special issue’s guest editors, accepted three papers after a stringent review process, which appear in this special issue. In addition to these three papers, we hope to intensify the discussion on NeuroIS research methodology, and to this end we present the current paper. Importantly, our observations during the review process (particularly with respect to methodology) and our own reading of the literature and the scientific discourse during conferences served as input for this paper. Specifically, we argue that six factors, among others that will become evident in future discussions, are critical for a rigorous NeuroIS research methodology; namely, reliability, validity, sensitivity, diagnosticity, objectivity, and intrusiveness of a measurement instrument. NeuroIS researchers—independent from whether their role is editor, reviewer, or author—should carefully give thought to these factors. We hope that the discussion in this paper instigates future contributions to a growing understanding towards a NeuroIS research methodology

A Framework for Psychophysiological Classification within a Cultural Heritage Context Using Interest

This article presents a psychophysiological construct of interest as a knowledge emotion and illustrates the importance of interest detection in a cultural heritage context. The objective of this work is to measure and classify psychophysiological reactivity in response to cultural heritage material presented as visual and audio. We present a data processing and classification framework for the classification of interest. Two studies are reported, adopting a subject-dependent approach to classify psychophysiological signals using mobile physiological sensors and the support vector machine learning algorithm. The results show that it is possible to reliably infer a state of interest from cultural heritage material using psychophysiological feature data and a machine learning approach, informing future work for the development of a real-time physiological computing system for use within an adaptive cultural heritage experience designed to adapt the provision of information to sustain the interest of the visitor