A Framework For Gait-Based User Demography Estimation Using Inertial Sensors

TL;DR

This paper introduces a deep learning framework combined with Layer-Wise Relevance Propagation to interpret gait-based demographic predictions from inertial sensor data, aiding clinicians and researchers in understanding key gait features.

Contribution

It presents a novel application of LRP to interpret deep learning models for gait-based demographic estimation, enhancing transparency and potential clinical insights.

Findings

Achieved accurate age and gender prediction from gait data.

Identified key gait variables influencing demographic classification.

Demonstrated interpretability of deep models in gait analysis.

Abstract

Human gait has been shown to provide crucial motion cues for various applications. Recognizing patterns in human gait has been widely adopted in various application areas such as security, virtual reality gaming, medical rehabilitation, and ailment identification. Furthermore, wearable inertial sensors have been widely used for not only recording gait but also to predict users' demography. Machine Learning techniques such as deep learning, combined with inertial sensor signals, have shown promising results in recognizing patterns in human gait and estimate users' demography. However, the black-box nature of such deep learning models hinders the researchers from uncovering the reasons behind the model's predictions. Therefore, we propose leveraging deep learning and Layer-Wise Relevance Propagation (LRP) to identify the important variables that play a vital role in identifying the users' demography such as age and gender. To assess the efficacy of this approach we train a deep neural network model on a large sensor-based gait dataset consisting of 745 subjects to identify users' age and gender. Using LRP we identify the variables relevant for characterizing the gait patterns. Thus, we enable interpretation of non-linear ML models which are experts in identifying the users' demography based on inertial signals. We believe this approach can not only provide clinicians information about the gait parameters relevant to age and gender but also can be expanded to analyze and diagnose gait disorders.