ECG-based Identity Validation during Bathing in Different Water Temperature

This paper proposes a novel identity validation method using ECG signal measured during bathing at 5 different bathtub water temperature ranges, that are 37 ±0.5 °C, 38±0.5 °C, 39±0.5 °C, 40±0.5 °C and 41±0.5 °C, respectively. The experiment includes 5 male and 5 female subjects, each subject collects 2 ECG recordings at each bathtub water temperature range, one day one recording, 10 ECG recordings are collected from each subject, each ECG recording is 18 minutes long, the sampling rate is 200 Hz. During the data processing stage, we perform spectrum analysis, baseline wandering removal, 50 Hz electromagnetic interference removal, signal smoothing, R peaks detection, and QRS complex segmentation. During the classification stage, we perform identity validation using long short-term memory (LSTM) classification network. 5 classification models are trained based on different bathtub water temperature ranges and the cross-validation method is used. Preliminary validation results show that different bathtub water temperature has an important impact on the identity validation. In order to precisely and quickly perform identity validation at different bathtub water temperature ranges, the final classification model is trained based on the samples from 5 different bathtub water temperature ranges. The highest and average identity validation accuracies are 98.43% and 97.68%, respectively.