Identification of Athletes During Walking and Jogging Based on Gait and Electrocardiographic Patterns
We propose a biometric method for identifying athletes based on information extracted from the gait style and the electrocardiographic (ECG) waveform. The required signals are recorded within a non-clinical acquisition setup using a wireless body sensor attached to a chest strap with integrated textile electrodes. Our method combines both sources of information to allow identification despite severe intra-subjects variations in the gait patterns (walking and jogging) and motion related artefacts in the ECG patterns. For identification we use features extracted in time and frequency domain and a standard classifier. Within a treadmill experiment with 22 subjects we obtained an accuracy of 98.1 % for velocities from 3 to 9 km/h. On a second data set consisting of 9 subjects and two sessions of recording, our method achieved 93.8 % despite variations in the patterns due to reapplying the body sensor and an increased velocity (up to 11 km/h).
KeywordsHuman identification Accelerometer Electrocardiograph (ECG) Wireless body sensor (WBS) Pattern recognition
This research was supported by the DFG CoE 277: Cognitive Interaction Technology (CITEC).
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