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Reproducible Benchmark of Wavelet-Enhanced Intrabody Communication Biometric Identification
Intrabody communication (IBC) channels offer physiological diversity that can be leveraged for passive biometric identification in wearable devices. Recent reports of over 99 per cent identification accuracy have frequently resulted from data leakage, where samples from the same subject are seen in both training and evaluation, yielding inflated and unreliable metrics. In this work, we establish a public, leakage-free benchmark for IBC biometrics built on a 30-subject open dataset, using strict subject-wise 80/20 splits repeated five times to ensure reproducibility. We systematically compare frequency-domain and time-frequency representations, including resampled spectra, discrete wavelet transform (DWT) statistics, and their fusion. We evaluate a diverse suite of models, spanning k-nearest neighbors, Random Forest, a one-dimensional spectral convolutional neural network (SpectralCNN), a multilayer perceptron (MLP) for feature fusion, and an SVD-based linear decoder. Our strongest configuration – an MLP trained on combined resampled spectra and DWT features – attains an accuracy of approximately 83 per cent, which serves as an upper bound under our protocol, substantially outperforming classical instancebased methods (approx. 41 per cent) and SVD (approx. 42 per cent). The SpectralCNN, trained on resampled spectra alone, achieves 74 per cent accuracy. Confusion matrix analysis reveals that residual errors are concentrated among subject pairs with statistically overlapping signatures, suggesting the presence of intrinsically hard users and a potential biometric ceiling for this modality. Embedded profiling on an STM32F446RE Cortex-M4 microcontroller indicates that lifting-based wavelet features enable low-latency, low-energy scoring, requiring approximately 0.55 ms and 18 micro-J per 256-point spectrum for Lift-bior feature extraction plus Random Forest inference (versus approx. 33 micro-J for the equivalent db4-DWT pipeline). All code, data split scripts, and Jupyter notebooks are released open source to facilitate reproducibility and enable rigorous future comparisons.