Explainable gait recognition with prototyping encoder–decoder

Jucheol Moon, Yong Min Shin, Jin Duk Park, Nelson Hebert Minaya, Won Yong Shin, Sang Il Choi

Research output: Contribution to journalArticlepeer-review


Human gait is a unique behavioral characteristic that can be used to recognize individuals. Collecting gait information widely by the means of wearable devices and recognizing people by the data has become a topic of research. While most prior studies collected gait information using inertial measurement units, we gather the data from 40 people using insoles, including pressure sensors, and precisely identify the gait phases from the long time series using the pressure data. In terms of recognizing people, there have been a few recent studies on neural network-based approaches for solving the open set gait recognition problem using wearable devices. Typically, these approaches determine decision boundaries in the latent space with a limited number of samples. Motivated by the fact that such methods are sensitive to the values of hyper-parameters, as our first contribution, we propose a new network model that is less sensitive to changes in the values using a new prototyping encoder–decoder network architecture. As our second contribution, to overcome the inherent limitations due to the lack of transparency and interpretability of neural networks, we propose a new module that enables us to analyze which part of the input is relevant to the overall recognition performance using explainable tools such as sensitivity analysis (SA) and layer-wise relevance propagation (LRP).

Original languageEnglish
Article numbere0264783
JournalPloS one
Issue number3 March
Publication statusPublished - 2022 Mar

Bibliographical note

Funding Information:
Funding:Thisresearchwassupportedbythe NationalResearchFoundationofKorea(NRF) grants(No.2021R1A2C3004345andNo.

Publisher Copyright:
© 2022 Moon et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

All Science Journal Classification (ASJC) codes

  • General


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