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Published in:
2021 | OriginalPaper | Chapter
Augmentation of Gait Cycles Using LSTM-MDN Networks in Person Identification System
Author : Aleksander Sawicki
Published in: Computer Information Systems and Industrial Management
Publisher: Springer International Publishing
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Abstract
This paper presents a novel data augmentation method to improve a walk-based person identification system. The proposed algorithm is based on trainable deep learning models, that are able to model the gait cycle of individual participants and generate perturbed augmented signals. In this study generative model involving two layers of Long Short-Term Memory (LSTM) and Mixture Density Network (MDN) was implemented.
The proposed approach was evaluated on a publicly available human gait database collected with 30 participants and captured with IMU sensors. The impact of using the proposed algorithm was compared with the case without data augmentation (baseline) and the case of augmentation with the classical state-of-the-art method. The use of an LSTM-MDN model in the augmentation process has promising results increasing f-score from 0.94 to 0.96. Whereas, use classical state-of-the-art augmentation method did not affect the person identification metrics.