Scaling Human Activity Recognition: A Comparative Evaluation of Synthetic Data Generation and Augmentation Techniques

• URL: http://arxiv.org/abs/2506.07612v2
• Date: Fri, 13 Jun 2025 13:43:21 GMT
• Title: Scaling Human Activity Recognition: A Comparative Evaluation of Synthetic Data Generation and Augmentation Techniques
• Authors: Zikang Leng, Archith Iyer, Thomas Plötz,
• Abstract summary: Human activity recognition (HAR) is often limited by the scarcity of labeled datasets.<n>Recent work has explored generating virtual inertial measurement unit (IMU) data via cross-modality transfer.
• Score: 1.0712226955584796
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Human activity recognition (HAR) is often limited by the scarcity of labeled datasets due to the high cost and complexity of real-world data collection. To mitigate this, recent work has explored generating virtual inertial measurement unit (IMU) data via cross-modality transfer. While video-based and language-based pipelines have each shown promise, they differ in assumptions and computational cost. Moreover, their effectiveness relative to traditional sensor-level data augmentation remains unclear. In this paper, we present a direct comparison between these two virtual IMU generation approaches against classical data augmentation techniques. We construct a large-scale virtual IMU dataset spanning 100 diverse activities from Kinetics-400 and simulate sensor signals at 22 body locations. The three data generation strategies are evaluated on benchmark HAR datasets (UTD-MHAD, PAMAP2, HAD-AW) using four popular models. Results show that virtual IMU data significantly improves performance over real or augmented data alone, particularly under limited-data conditions. We offer practical guidance on choosing data generation strategies and highlight the distinct advantages and disadvantages of each approach.

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