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CapSeat - Capacitive Proximity Sensing for Automotive Activity Recognition

Braun, Andreas and Frank, Sebastian and Majewski, Martin and Wang, Xiaofeng (2015):
CapSeat - Capacitive Proximity Sensing for Automotive Activity Recognition.
ACM Press, New York, In: The 7th International Conference on Automotive User Interfaces and Interactive Vehicular Applications. Proceedings, DOI: 10.1145/2799250.2799263,
[Conference or Workshop Item]

Abstract

Inattentiveness is one of the major causes of traffic accidents. Advanced car safety systems try to mitigate this by detecting potential signs of distraction or tiredness, and providing alerts to the driver. In this paper we present CapSeat - a car seat equipped with integrated capacitive proximity sensors that are used to measure a wide range of physiological parameters about the driver. This can support safety systems by detecting inattentiveness and increase passive safety by facilitating suitable seat adjustments and posture detection. We present a sensor electrode layout suitable for detecting the necessary parameters and processing methods that acquire multiple physiological parameters from sensor data, using a variety of different algorithms. A prototype of the system is presented that was evaluated for all detectable parameters in a proof-of-concept study. We achieved a classification precision between 95 and 100.

Item Type: Conference or Workshop Item
Erschienen: 2015
Creators: Braun, Andreas and Frank, Sebastian and Majewski, Martin and Wang, Xiaofeng
Title: CapSeat - Capacitive Proximity Sensing for Automotive Activity Recognition
Language: English
Abstract:

Inattentiveness is one of the major causes of traffic accidents. Advanced car safety systems try to mitigate this by detecting potential signs of distraction or tiredness, and providing alerts to the driver. In this paper we present CapSeat - a car seat equipped with integrated capacitive proximity sensors that are used to measure a wide range of physiological parameters about the driver. This can support safety systems by detecting inattentiveness and increase passive safety by facilitating suitable seat adjustments and posture detection. We present a sensor electrode layout suitable for detecting the necessary parameters and processing methods that acquire multiple physiological parameters from sensor data, using a variety of different algorithms. A prototype of the system is presented that was evaluated for all detectable parameters in a proof-of-concept study. We achieved a classification precision between 95 and 100.

Publisher: ACM Press, New York
Uncontrolled Keywords: Business Field: Digital society, Research Area: Human computer interaction (HCI), Activity recognition, Capacitive sensors, Capacitive proximity sensing, Safety concepts, Classifications
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
Event Title: The 7th International Conference on Automotive User Interfaces and Interactive Vehicular Applications. Proceedings
Date Deposited: 08 May 2019 08:00
DOI: 10.1145/2799250.2799263
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