Unobtrusive physiological monitoring in an airplane seat

Air travel has become the preferred mode of long-distance transportation for most of the world's travelers. People of every age group and health status are traveling by airplane and thus the airplane has become part of our environment, in which people with health-related limitations need assistive support. Since the main interaction point between a passenger and the airplane is the seat, this work presents a smart airplane seat for measuring health-related signals of a passenger. We describe the design, implementation and testing of a multimodal sensor system integrated into the seat. The presented system is able to measure physiological signals, such as electrocardiogram, electrodermal activity, skin temperature, and respiration. We show how the design of the smart seat system is influenced by the trade-off between comfort and signal quality, i.e. incorporating unobtrusive sensors and dealing with erroneous signals. Artifact detection through sensor fusion is presented and the working principle is shown with a feasibility study, in which normal passenger activities were performed. Based on the presented method, we are able to identify signal regions in which the accuracies for detecting the heart- and respiration-rate are 88 and 82%, respectively, compared to 40 and 76% without any artifact remova

The DRIVE-SAFE project: signal processing and advanced information technologies for improving driving prudence and accidents

In this paper, we will talk about the Drivesafe project whose aim is creating conditions for prudent driving on highways and roadways with the purposes of reducing accidents caused by driver behavior. To achieve these primary goals, critical data is being collected from multimodal sensors (such as cameras, microphones, and other sensors) to build a unique databank on driver behavior. We are developing system and technologies for analyzing the data and automatically determining potentially dangerous situations (such as driver fatigue, distraction, etc.). Based on the findings from these studies, we will propose systems for warning the drivers and taking other precautionary measures to avoid accidents once a dangerous situation is detected. In order to address these issues a national consortium has been formed including Automotive Research Center (OTAM), Koç University, Istanbul Technical University, Sabancı University, Ford A.S., Renault A.S., and Fiat A. Ş

Detecting driver fatigue using heart rate variability: A systematic review

Driver fatigue detection systems have potential to improve road safety by preventing crashes and saving lives. Conventional driver monitoring systems based on driving performance and facial features may be challenged by the application of automated driving systems. This limitation could potentially be overcome by monitoring systems based on physiological measurements. Heart rate variability (HRV) is a physiological marker of interest for detecting driver fatigue that can be measured during real life driving. This systematic review investigates the relationship between HRV measures and driver fatigue, as well as the performance of HRV based fatigue detection systems. With the applied eligibility criteria, 18 articles were identified in this review. Inconsistent results can be found within the studies that investigated differences of HRV measures between alert and fatigued drivers. For studies that developed HRV based fatigue detection systems, the detection performance showed a large variation, where the detection accuracy ranged from 44% to 100%. The inconsistency and variation of the results can be caused by differences in several key aspects in the study designs. Progress in this field is needed to determine the relationship between HRV and different fatigue causal factors and its connection to driver performance. To be deployed, HRV-based fatigue detection systems need to be thoroughly tested in real life conditions with good coverage of relevant driving scenarios and a sufficient number of participants

Smart Cabin Design Concept for Regional Aircraft: Technologies, Applications & Architecture

The use of smart devices is driving the growth of modern systems markets, resulting in more technological cities, cars, houses, and airplanes. Therefore, it is important to understand which smart technologies can support or even fulfill stakeholder needs and consequently enhance airplane operation and increase product competitiveness for airlines. This paper is the second of two-part series and has as its main objective the study of smart technologies that can be implemented on cabins of regional airplanes, from 60 to 120 seats, to comply with the needs and requirements pre-established on the first part of this series of articles. The integration of those technologies results in a “Smart Cabin” architecture which its purpose is to enhances the passenger experience by granting a new level of cabin comfort, customization, and connectivity that allows the reduction of airplane time on ground because of the real-time monitoring of airplane cabin components that enables the prediction of maintenance procedures; creates new profits and revenues opportunities for services, provides a more sustainable airplane operation and derived services, and creates new business opportunities for all companies that integrate regional aviation ecosystem

Ocular attention-sensing interface system

The purpose of the research was to develop an innovative human-computer interface based on eye movement and voice control. By eliminating a manual interface (keyboard, joystick, etc.), OASIS provides a control mechanism that is natural, efficient, accurate, and low in workload

The effects of trait labels on teacher evaluations of target behaviors, ratings, and academic work

The purpose of the present investigation was to examine the effects of trait labels on teachers' objective behavioral observations, teachers' subjective ratings, and teachers' grading of academic work. On the basis of previous studies examining observer bias, it was predicted that teacher observations would not be biased and that teacher subjective ratings would be biased. In addition, it was predicted that the subjective scoring of academic material would be biased. Five groups of teachers were trained on a three-category behavioral code by means of a video tape. Teachers then observed a 12-minute video tape of the same normal child in a classroom setting. The specific trait labels describing the child that were given to different teachers were: Emotionally Disturbed, Learning Disabled, Educable Mentally Retarded, Normal, and No Label. Teachers then coded behaviors from the video tape, rated the target child on a rating scale, and scored academic material purportedly completed by the target child

Cardiac Function, Pupillometry, Subjective Workload and Performance of Commercial Pilots During Jet Airplane Handling Manoeuvres

Projected demand for commercial pilots is expected to exceed current training programs. As such, next-generation training programs are focused on developing competencies through practice sessions targeting specific manoeuvres. These sessions are most effective when conducted near a trainee’s maximum workload. Thus, measuring and understanding the relationship between workload and performance in the cockpit is of prime importance. Currently, workload measurement in the cockpit relies on subjective measures. More recently, heart rate, heart rate variability and pupillometry have shown promise as objective, continuous indicators of workload. We investigated relationships between these psychophysiological measures, NASA-TLX subjective workload ratings and aircraft handling performance in commercial pilots flying a jet airplane simulator. We found that (1) heart rate and heart rate variability patterns were stable between participants, showing clear differences between manoeuvre types, but not manoeuvre difficulty, (2) pupil diameter is not an effective measure of workload in the simulator cockpit, likely due to the differences in brightness, and (3) psychophysiological measures of workload can supplement subjective workload reports to better understand performance

Blind Source Separation for the Processing of Contact-Less Biosignals

(Spatio-temporale) Blind Source Separation (BSS) eignet sich für die Verarbeitung von Multikanal-Messungen im Bereich der kontaktlosen Biosignalerfassung. Ziel der BSS ist dabei die Trennung von (z.B. kardialen) Nutzsignalen und Störsignalen typisch für die kontaktlosen Messtechniken. Das Potential der BSS kann praktisch nur ausgeschöpft werden, wenn (1) ein geeignetes BSS-Modell verwendet wird, welches der Komplexität der Multikanal-Messung gerecht wird und (2) die unbestimmte Permutation unter den BSS-Ausgangssignalen gelöst wird, d.h. das Nutzsignal praktisch automatisiert identifiziert werden kann. Die vorliegende Arbeit entwirft ein Framework, mit dessen Hilfe die Effizienz von BSS-Algorithmen im Kontext des kamera-basierten Photoplethysmogramms bewertet werden kann. Empfehlungen zur Auswahl bestimmter Algorithmen im Zusammenhang mit spezifischen Signal-Charakteristiken werden abgeleitet. Außerdem werden im Rahmen der Arbeit Konzepte für die automatisierte Kanalauswahl nach BSS im Bereich der kontaktlosen Messung des Elektrokardiogramms entwickelt und bewertet. Neuartige Algorithmen basierend auf Sparse Coding erwiesen sich dabei als besonders effizient im Vergleich zu Standard-Methoden.(Spatio-temporal) Blind Source Separation (BSS) provides a large potential to process distorted multichannel biosignal measurements in the context of novel contact-less recording techniques for separating distortions from the cardiac signal of interest. This potential can only be practically utilized (1) if a BSS model is applied that matches the complexity of the measurement, i.e. the signal mixture and (2) if permutation indeterminacy is solved among the BSS output components, i.e the component of interest can be practically selected. The present work, first, designs a framework to assess the efficacy of BSS algorithms in the context of the camera-based photoplethysmogram (cbPPG) and characterizes multiple BSS algorithms, accordingly. Algorithm selection recommendations for certain mixture characteristics are derived. Second, the present work develops and evaluates concepts to solve permutation indeterminacy for BSS outputs of contact-less electrocardiogram (ECG) recordings. The novel approach based on sparse coding is shown to outperform the existing concepts of higher order moments and frequency-domain features

Pulse patency and oxygenation sensing system development to detect g-induced loss of consciousness

A fighter pilots greatest strength is the weakness of his or her opponent. Commonly, this strength comes down to the maneuverability of the aircraft, particularly the ability to out-climb. Since the 1980\u27s, the thrust produced by these engines have the ability to drain the pilots head of blood causing a state of unconsciousness due to the overwhelming forces of gravity for upwards of 30 seconds; often times having fatal outcomes. This thesis explores the feasibility of detecting of blood flow by means of arterial wall expansion (pulse patency) and blood oxygenation using a microprocessor to continually monitor the signals from this two part sensor where by insight into the development of a g-induced loss of consciousness sensing system can be developed. Results indicate greater than 90% accuracy pulse patency detection using an accelerometer. Simulation and physical models were used as well as human testing to develop a blood oxygenation and pulse patency sensor, or BOPS