Conference Paper

This study addresses the growing need for noninvasive,
secure, and efficient biometric identification methods
in Internet of Things (IoT) applications, where traditional
biometric systems often face challenges due to privacy concerns,
environmental constraints, and practical limitations. To tackle
these issues, we introduce a novel biometric identification
system that leverages custom-built multiplexed Fiber Bragg
Grating (FBG) accelerometers and bispectral feature
extraction. By applying bispectral analysis to the acquired gait
signals, we extract robust and discriminative features for
individual identification. Unsupervised clustering algorithms,
namely K-means and DBSCAN, were employed to categorize
individuals based on these features, successfully identifying ten
distinct clusters corresponding to ten participants and
demonstrating the system's effectiveness. The K-means model
achieved a Davies-Bouldin Index of 0.79 and a Silhouette Score
of 0.45, while DBSCAN yielded a Davies-Bouldin Index of 0.87
and a Silhouette Score of 0.36. Given the proprietary nature of
the data and the custom-built FBG accelerometers used in this
study, direct comparisons to state-of-the-art methods are not
available. However, these results underscore the potential of our
unique approach and technology to advance biometric
identification, providing a promising non-invasive, scalable, and
discreet solution with broad applicability in IoT environments.