Identity Recognition in Sanitary Facilities Using Invisible Electrocardiography.

Area of Science:

• Biomedical Engineering
• Wearable Technology
• Biometrics

Background:

• Previous work demonstrated invisible electrocardiography (ECG) using thigh electrodes integrated into a toilet seat.
• Industrial viability requires optimized electrode materials compatible with large-scale production.

Purpose of the Study:

• To develop and evaluate an industrially viable ECG-based identity recognition system integrated into a toilet seat.
• To explore conductive silicone as a suitable electrode material for mass production.
• To assess the accuracy of ECG signal acquisition and biometric identification without body-worn devices.

Main Methods:

• A biometrics pipeline was developed, testing four classifiers on populations of 2-17 subjects in a simulated residential environment.
• Conductive silicone electrodes were investigated for industrial-scale toilet seat production.
• Performance was evaluated against a reference device using 17 individuals (ages 24-70), including healthy and pathological cases.

Main Results:

• The conductive silicone composite achieved 100% signal acquisition across all sessions.
• Mean heart rate deviation from a reference system was 2.82 ± 1.99 beats per minute (BPM).
• ECG waveform morphology showed a high Pearson correlation coefficient of 0.91 ± 0.06 in optimal cases.
• The Binary Convolutional Neural Network (BCNN) classifier achieved 100% accuracy for biometric detection in populations up to four.

Conclusions:

• Conductive silicone is a promising material for industrial-scale production of ECG-recording toilet seats.
• The developed system enables reliable, non-invasive identity recognition without requiring body-worn sensors.
• The BCNN classifier demonstrates high accuracy for user identification in this novel biometric system.