Deep Multi-Class Novelty Detection in Structural Vibrations with Modified Contrastive Loss Framework

Indian Institute of Technology, Delhi, Roorkee

Code to be updated soon
Abstract Figure

Overview of the data pipeline for novelty detection in structural vibrations. (a) Data collection from footfall events, (b) Event extraction and Continuous Wavelet Transform (CWT) generation, (c) Novelty detection using a contrastive loss framework distinguishing regular and novel events.

Abstract

In this paper, we introduce a framework for multi-class novelty detection using structural vibration signals. Structural vibration-based person identification is a promising soft-biometric approach with potential applications in elderly care and access control. However, current research faces two key challenges. The first challenge is the lack of large-scale datasets necessary for thorough evaluation in structural vibration gait recognition. To address this, we created a new dataset with recordings from fifty individuals. The second challenge lies in the limited exploration of deep learning methods for large-scale multi-class novelty detection in structural vibration data. To fill this gap, we propose the energy-shifted contrastive loss function, specifically designed for this task. Our results demonstrate that the proposed framework achieves 96.57% accuracy in multi-class classification. For novelty detection, it achieves an Receiver Operating Characteristic—Area Under the Curve (ROC-AUC) score of 89.15% for single footsteps, which improves to 93.83% with five consecutive footsteps.

BibTeX

TBD