Exploring Cognitive States: Methods for Detecting Physiological Temporal Fingerprints

Cognitive state detection and its relationship to observable physiologically telemetry has been utilized for many human-machine and human-cybernetic applications. This paper aims at understanding and addressing if there are unique psychophysiological patterns over time, a physiological temporal fingerprint, that is associated with specific cognitive states. This preliminary work involves commercial airline pilots completing experimental benchmark task inductions of three cognitive states: 1) Channelized Attention (CA); 2) High Workload (HW); and 3) Low Workload (LW). We approach this objective by modeling these "fingerprints" through the use of Hidden Markov Models and Entropy analysis to evaluate if the transitions over time are complex or rhythmic/predictable by nature. Our results indicate that cognitive states do have unique complexity of physiological sequences that are statistically different from other cognitive states. More specifically, CA has a significantly higher temporal psychophysiological complexity than HW and LW in EEG and ECG telemetry signals. With regards to respiration telemetry, CA has a lower temporal psychophysiological complexity than HW and LW. Through our preliminary work, addressing this unique underpinning can inform whether these underlying dynamics can be utilized to understand how humans transition between cognitive states and for improved detection of cognitive states

Measuring cognitive load and cognition: metrics for technology-enhanced learning

This critical and reflective literature review examines international research published over the last decade to summarise the different kinds of measures that have been used to explore cognitive load and critiques the strengths and limitations of those focussed on the development of direct empirical approaches. Over the last 40 years, cognitive load theory has become established as one of the most successful and influential theoretical explanations of cognitive processing during learning. Despite this success, attempts to obtain direct objective measures of the theory's central theoretical construct – cognitive load – have proved elusive. This obstacle represents the most significant outstanding challenge for successfully embedding the theoretical and experimental work on cognitive load in empirical data from authentic learning situations. Progress to date on the theoretical and practical approaches to cognitive load are discussed along with the influences of individual differences on cognitive load in order to assess the prospects for the development and application of direct empirical measures of cognitive load especially in technology-rich contexts

Healthcare Robotics

Robots have the potential to be a game changer in healthcare: improving health and well-being, filling care gaps, supporting care givers, and aiding health care workers. However, before robots are able to be widely deployed, it is crucial that both the research and industrial communities work together to establish a strong evidence-base for healthcare robotics, and surmount likely adoption barriers. This article presents a broad contextualization of robots in healthcare by identifying key stakeholders, care settings, and tasks; reviewing recent advances in healthcare robotics; and outlining major challenges and opportunities to their adoption.Comment: 8 pages, Communications of the ACM, 201

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 324)

This bibliography lists 200 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during May, 1989. Subject coverage includes: aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance

Noninvasive Physiological Measures And Workload Transitions:an Investigation Of Thresholds Using Multiple Synchronized Sensors

The purpose of this study is to determine under what conditions multiple minimally intrusive physiological sensors can be used together and validly applied for use in areas which rely on adaptive systems including adaptive automation and augmented cognition. Specifically, this dissertation investigated the physiological transitions of operator state caused by changes in the level of taskload. Three questions were evaluated including (1) Do differences exist between physiological indicators when examined between levels of difficulty? (2) Are differences of physiological indicators (which may exist) between difficulty levels affected by spatial ability? (3) Which physiological indicators (if any) account for variation in performance on a spatial task with varying difficulty levels? The Modular Cognitive State Gauge model was presented and used to determine which basic physiological sensors (EEG, ECG, EDR and eye-tracking) could validly assess changes in the utilization of two-dimensional spatial resources required to perform a spatial ability dependent task. Thirty-six volunteers (20 female, 16 male) wore minimally invasive physiological sensing devices while executing a challenging computer based puzzle task. Specifically, participants were tested with two measures of spatial ability, received training, a practice session, an experimental trial and completed a subjective workload survey. The results of this experiment confirmed that participants with low spatial ability reported higher subjective workload and performed poorer when compared to those with high spatial ability. Additionally, there were significant changes for a majority of the physiological indicators between two difficulty levels and most importantly three measures (EEG, ECG and eye-tracking) were shown to account for variability in performance on the spatial task

Novel Multimodal Feedback Techniques for In-Car Mid-Air Gesture Interaction

This paper presents an investigation into the effects of different feedback modalities on mid-air gesture interaction for infotainment systems in cars. Car crashes and near-crash events are most commonly caused by driver distraction. Mid-air interaction is a way of reducing driver distraction by reducing visual demand from infotainment. Despite a range of available modalities, feedback in mid-air gesture systems is generally provided through visual displays. We conducted a simulated driving study to investigate how different types of multimodal feedback can support in-air gestures. The effects of different feedback modalities on eye gaze behaviour, and the driving and gesturing tasks are considered. We found that feedback modality influenced gesturing behaviour. However, drivers corrected falsely executed gestures more often in non-visual conditions. Our findings show that non-visual feedback can reduce visual distraction significantl

Activity Theory Analysis of Heart Failure Self-Care

The management of chronic health conditions such as heart failure is a complex process emerging from the activity of a network of individuals and artifacts. This article presents an Activity Theory-based secondary analysis of data from a geriatric heart failure management study. Twenty-one patients' interviews and clinic visit observations were analyzed to uncover eight configurations of roles and activities involving patients, clinicians, and others in the sociotechnical network. For each configuration or activity pattern, we identify points of tension and propose guidelines for developing interventions for future computer-supported healthcare systems

Rethinking Eye-blink: Assessing Task Difficulty through Physiological Representation of Spontaneous Blinking

Continuous assessment of task difficulty and mental workload is essential in improving the usability and accessibility of interactive systems. Eye tracking data has often been investigated to achieve this ability, with reports on the limited role of standard blink metrics. Here, we propose a new approach to the analysis of eye-blink responses for automated estimation of task difficulty. The core module is a time-frequency representation of eye-blink, which aims to capture the richness of information reflected on blinking. In our first study, we show that this method significantly improves the sensitivity to task difficulty. We then demonstrate how to form a framework where the represented patterns are analyzed with multi-dimensional Long Short-Term Memory recurrent neural networks for their non-linear mapping onto difficulty-related parameters. This framework outperformed other methods that used hand-engineered features. This approach works with any built-in camera, without requiring specialized devices. We conclude by discussing how Rethinking Eye-blink can benefit real-world applications