Data-Driven Meta-Analysis and Public-Dataset Evaluation for Sensor-Based Gait Age Estimation

TL;DR

This paper conducts a comprehensive meta-analysis and large-scale experiments on sensor-based gait age estimation, demonstrating that deep learning models can achieve high accuracy and providing insights into gait features related to aging.

Contribution

It combines a broad meta-analysis with new large-scale experiments and interpretability methods to establish performance baselines and practical guidelines for gait age estimation.

Findings

Deep networks achieve up to 96% accuracy in gait age estimation.

Multi-sensor fusion reduces error to as low as 3.4 years.

Key gait metrics correlate with age, aiding interpretability.

Abstract

Estimating a person's age from their gait has important applications in healthcare, security and human-computer interaction. In this work, we review fifty-nine studies involving over seventy-five thousand subjects recorded with video, wearable and radar sensors. We observe that convolutional neural networks produce an average error of about 4.2 years, inertial-sensor models about 4.5 years and multi-sensor fusion as low as 3.4 years, with notable differences between lab and real-world data. We then analyse sixty-three thousand eight hundred forty-six gait cycles from the OU-ISIR Large-Population dataset to quantify correlations between age and five key metrics: stride length, walking speed, step cadence, step-time variability and joint-angle entropy, with correlation coefficients of at least 0.27. Next, we fine-tune a ResNet34 model and apply Grad-CAM to reveal that the network attends to the knee and pelvic regions, consistent with known age-related gait changes. Finally, on a one hundred thousand sample subset of the VersatileGait database, we compare support vector machines, decision trees, random forests, multilayer perceptrons and convolutional neural networks, finding that deep networks achieve up to 96 percent accuracy while processing each sample in under 0.1 seconds. By combining a broad meta-analysis with new large-scale experiments and interpretable visualizations, we establish solid performance baselines and practical guidelines for reducing gait-age error below three years in real-world scenarios.