Authors:
Peter Christ
1
;
Felix Werner
2
;
Ulrich Rückert
2
and
Jörg Mielebacher
3
Affiliations:
1
Universität Bielefeld, Germany
;
2
Bielefeld University, Germany
;
3
Mielebacher Informatiksysteme, Germany
Keyword(s):
Human Identification, Accelerometer, Electrocardiograph (ECG), Wireless Body Sensor (WBS), Pattern Recognition.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Biometrics
;
Biometrics and Pattern Recognition
;
Computational Intelligence
;
Data Manipulation
;
Detection and Identification
;
Devices
;
Health Engineering and Technology Applications
;
Health Information Systems
;
Human-Computer Interaction
;
Methodologies and Methods
;
Multimedia
;
Multimedia Signal Processing
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Telecommunications
;
Theory and Methods
;
Wearable Sensors and Systems
Abstract:
In this paper we propose a biometric method for identifying humans during walking and jogging. We use acceleration and electrocardiographic measurements recorded with a wireless body sensor attached to a chest strap. Our method does not require a particular acquisition setup. Information on the gait style and on the physiology is combined to identify a human despite severe motion related artefacts in the electrocardiograph and variations in the gait patterns. We propose to identify humans using features extracted in time and frequency domain and a standard classifier. With the collected data of 22 subjects on a treadmill at velocities from 3 to 9 km/h we obtained an accuracy of 98.1 %. The sensitivity of the identification ranged between 94.6 to 99.5% for the different subjects and the specificity was higher than 99.7 %.