Physically Plausible Data Augmentations for Wearable IMU-based Human Activity Recognition Using Physics Simulation

TL;DR

This paper introduces Physically Plausible Data Augmentation (PPDA) using physics simulation to generate realistic sensor data for human activity recognition, improving model performance and reducing data collection needs.

Contribution

It systematically characterizes PPDA methods for HAR, demonstrating their advantages over traditional signal transformations and showing their effectiveness in reducing data requirements.

Findings

PPDA improves macro F1 scores by up to 13 percentage points.

PPDA achieves similar performance with 60% fewer training subjects.

Physics-based augmentation enhances data realism and model generalization.

Abstract

The scarcity of high-quality labeled data in sensor-based Human Activity Recognition (HAR) hinders model performance and limits generalization across real-world scenarios. Data augmentation is a key strategy to mitigate this issue by enhancing the diversity of training datasets. Signal Transformation-based Data Augmentation (STDA) techniques have been widely used in HAR. However, these methods are often physically implausible, potentially resulting in augmented data that fails to preserve the original meaning of the activity labels. In this study, we introduce and systematically characterize Physically Plausible Data Augmentation (PPDA) enabled by physics simulation. PPDA leverages human body movement data from motion capture or video-based pose estimation and incorporates various realistic variabilities through physics simulation, including modifying body movements, sensor placements, and hardware-related effects. We compare the performance of PPDAs with traditional STDAs on three public datasets of daily activities and fitness workouts. First, we evaluate each augmentation method individually, directly comparing PPDAs to their STDA counterparts. Next, we assess how combining multiple PPDAs can reduce the need for initial data collection by varying the number of subjects used for training. Experiments show consistent benefits of PPDAs, improving macro F1 scores by an average of 3.7 pp (up to 13 pp) and achieving competitive performance with up to 60% fewer training subjects than STDAs. As the first systematic study of PPDA in sensor-based HAR, these results highlight the advantages of pursuing physical plausibility in data augmentation and the potential of physics simulation for generating synthetic Inertial Measurement Unit data for training deep learning HAR models. This cost-effective and scalable approach therefore helps address the annotation scarcity challenge in HAR.