Toward Machine Emotional Intelligence: Analysis of Affective Physiological State
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Physiological data feedback for application in distance education
Proceedings of the 2001 workshop on Perceptive user interfaces
Emotions and heart rate while sitting on a chair
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Automatic prediction of frustration
International Journal of Human-Computer Studies
A review of smart homes-Present state and future challenges
Computer Methods and Programs in Biomedicine
Adaptive user preference modeling and its application to in-flight entertainment
Proceedings of the 3rd international conference on Digital Interactive Media in Entertainment and Arts
Recognizing Upper Body Postures using Textile Strain Sensors
ISWC '07 Proceedings of the 2007 11th IEEE International Symposium on Wearable Computers
Detecting stress during real-world driving tasks using physiological sensors
IEEE Transactions on Intelligent Transportation Systems
Pervasive technologies for assistive environments: special issue of PETRA 2008 conference
Personal and Ubiquitous Computing
The smart car seat: personalized monitoring of vital signs in automotive applications
Personal and Ubiquitous Computing
| Hi-index | 0.00 |
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 removal.