ORIGINAL RESEARCH article
Front. Artif. Intell.
Sec. Machine Learning and Artificial Intelligence
Volume 8 - 2025 | doi: 10.3389/frai.2025.1640948
This article is part of the Research TopicMultimodal human action recognition in real or virtual environmentsView all articles
Optimizing Training of Time Series Diffusion Models via Similarity Score Functions: Application to Human Activity Recognition with IMU data
Provisionally accepted
- Ulm University of Applied Sciences, Ulm, Germany
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Denoising diffusion probabilistic models are able to generate synthetic sensor signals. The training process of such a model is controlled by a loss function which measures the difference between the noise that was added in the forward process and the noise that was predicted by the diffusion model. This enables the generation of realistic data. However, the randomness within the process and the loss function itself makes it difficult to estimate the quality of the data.Therefore, we examine multiple similarity metrics and adapt an existing metric to overcome this issue by monitoring the training and synthetisation process using those metrics. The adapted metric can even be fine-tuned on the input data to comply with the requirements of an underlying classification task. We were able to significantly reduce the amount of training epochs without a performance reduction in the classification task. An optimized training process not only saves resources, but also reduces the time for training generative models.
Keywords: diffusion model, time series, Similarity Score Functions, Synthetisation, Human activity recognition, Sport climbing
Received: 04 Jun 2025; Accepted: 18 Aug 2025.
Copyright: © 2025 Oppel, Spilz and Munz. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Heiko Oppel, Ulm University of Applied Sciences, Ulm, Germany
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