Key result
An optimized band-pass filter achieved 100% ECG-based identification accuracy when subjects were resting and 99.7% during exercise.
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An optimized band-pass filter can effectively compensate for exercise-induced ECG waveform distortions, enabling highly accurate ECG-based human identification.
Waveform distortions generated by exercise cause erroneous predictions in human identification based on the electrocardiogram (ECG). An approach for the ECG‐based identification to achieve accurate predictions while exercising is proposed. In the proposed scheme, passband of a band‐pass filter is optimised to compensate for the distortion. As a result, the proposed scheme archives 100% identification accuracy is revealed when the subject is resting and 99.7% during exercise. The investigation is an important building block in the development of robust ECG‐based identification systems that are consistently accurate despite significant ECG waveform variation due to exercise.
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Nobunaga et al. (2016) studied ECG-based identification of exercising human subjects. Optimised band-pass filter was evaluated on Identification accuracy. An optimized band-pass filter achieved 100% ECG-based identification accuracy when subjects were resting and 99.7% during exercise.
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