Real 2018 Abstracts

Full Papers Short Papers
Full Papers
Paper Nr: 1
Title:

Comparison of a Custom Functional Near-infrared Spectroscopy Sensor, a Peripheral SpO2 Sensor, and a Standard Laboratory Sensor (Biopac) for RR-Interval Assessment
Authors:

Bethany K. Bracken, Polemnia G. Amazeen, Aaron D. Likens, Mustafa Demir and Cameron T. Gibbons
Abstract: Across many careers, individuals face alternating periods of high and low cognitive workload which can impair cognitive function and undermine job performance. We have designed and are developing an unobtrusive system to Monitor, Extract, and Decode Indicators of Cognitive Workload (MEDIC) in real-world environments. With our partners at Biosignals Plux, we designed and manufactured a functional near-infrared spectroscopy (fNIRS) device that measures brain blood oxygenation and cardiac information in a form-factor that can be mounted on the inside of a baseball cap or headband. Because MEDIC is designed to be used in realistic, sometimes high-motion environments, changes in blood oxygenation to the brain must be put in context of current levels of physical activity without intruding on the activity of the user. Therefore, we also developed a NIRS Armband device made up of a combination of Plux sensors including: SpO2 sensor to measure cardiac information, a galvanic skin response sensor, a 6-axis accelerometer, and a non-contact skin temperature sensor. Because these were custom sensors, we tested them against a standard laboratory sensor (a Biopac RSPEC-R) while participants completed an obstacle course of cognitive and physical tasks.
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Paper Nr: 2
Title:

Using Functional near Infrared Spectroscopy to Assess Cognitive Performance of UAV Sensor Operators during Route Scanning
Authors:

Jazsmine Armstrong, Kurtulus Izzetoglu and Dale Richards
Abstract: The composition of UAV (Unmanned Aerial Vehicle) crew will sometimes define roles specific to tasks associated with the Ground Control Station (GCS). The sensor operator task is specific to both the type of platform and GCS they are operating, but in many instances the role of this operator is critical in determining mission success. In order to assess mission effectiveness we applied human performance measures that focussed on neurological brain imaging techniques and other physiological biomarkers in conjunction with behavioral data acquired from the sensor operator task. In the execution of the experiment, this included such tasks as route scanning, target detection and positive identification, and the tracking of identified targets. Within the scope of this paper, we reported the preliminary results for the route scanning task. Over the duration of three trials brain activity measures from the prefrontal cortex region were acquired via functional near infrared spectroscopy (fNIRS) in this research study. As the trials progressed, there was a significant difference between low and high performers on the route scanning task as determined by specific biomarkers, namely oxygenated haemoglobin. These findings support previous studies and indicates the benefits of applying neurophysiological measures in order to gain further objective insight into human cognitive performance. The use of fNIRS in this context is also discussed in terms of providing a key benefit in dynamically evaluating human performance in parallel with personalized training for UAV operators.
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Paper Nr: 3
Title:

Physiologically Attentive User Interface for Robot Teleoperation - Real Time Emotional State Estimation and Interface Modification using Physiology, Facial Expressions and Eye Movements
Authors:

Gaganpreet Singh, Sergi Bermúdez i Badia, Rodrigo Ventura and José Luís Silva
Abstract: We developed a framework for Physiologically Attentive User Interfaces, to reduce the interaction gap between humans and machines in life critical robot teleoperations. Our system utilizes emotional state awareness capabilities of psychophysiology and classifies three emotional states (Resting, Stress, and Workload) by analysing physiological data along with facial expression and eye movement analysis. This emotional state estimation is then used to create a dynamic interface that updates in real time with respect to user’s emotional state. The results of a preliminary evaluation of the developed emotional state classifier for robot teleoperation are presented, along with its future possibilities are discussed.
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Paper Nr: 4
Title:

Measuring Immersion in Experiences with Biosensors - Preparation for International Joint Conference on Biomedical Engineering Systems and Technologies
Authors:

Paul J. Zak and Jorge A. Barraza
Abstract: When people are engaged in an immersive task or experience, they can become so absorbed in it that they lose track of time and place. Narrative transportation, has similar effects, producing meaningful psychological responses and influencing behavior. Those seeking to create immersive experiences typically rely on inaccurate self-report to measure immersion. We describe research from our group on the neuroscience of immersion and our development of a physiologic sensor, algorithm, and software suite that measures immersion. Our studies show that immersion predicts enjoyment, recall of information, and actions after an experience with 75%-95%% accuracy depending on the outcome measure. We discuss the trade-offs when developing sensor technologies designed for non-laboratory environments.
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Short Papers
Paper Nr: 5
Title:

Quantification of the Voicescape: A Person-centric Approach to Describing Real-life Behaviour Patterns - A Case Study Comparing Two Age Groups
Authors:

Ana Londral, Burcu Demiray and Marcus Cheetham
Abstract: The human voice is a fundamental part of the everyday auditory environment. A measure of all voice activity that a person produces or perceives in the environment, i.e., the person’s voicescape, might provide an informative, low cost, ecologically valid, and person-centric approach to characterizing patterns of socially-relevant behaviour in real life. In this paper, we use the measure ratio of voice activity (rva) and present results of data acquired from N=20 subjects of 2 different age groups as they engaged in their usual daily life activities over 4 consecutive days. The data show no differences in total voice activity but significant between-group differences in its daily distribution. We propose that measurement of the voicescape can, even without knowledge of specific voice sources, serve as a useful indicator of person- or group specific activity patterns for purposes of describing significant aspects of variation and within- and between-group differences in patterns of everyday behaviour and, potentially, for identifying change in patterns that have health-related implications. Future work will target automatic detection and identification of voice sources and the use of privacy-preserving processing methods
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