Individual Gait Identification Using FBG Accelerometer-Based Bispectrum Features and Unsupervised Clustering

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.