Saga: Capturing Multi-granularity Semantics from Massive Unlabelled IMU Data for User Perception

• URL: http://arxiv.org/abs/2504.11726v1
• Date: Wed, 16 Apr 2025 03:03:42 GMT
• Title: Saga: Capturing Multi-granularity Semantics from Massive Unlabelled IMU Data for User Perception
• Authors: Yunzhe Li, Facheng Hu, Hongzi Zhu, Shifan Zhang, Liang Zhang, Shan Chang, Minyi Guo,
• Abstract summary: In this paper, we propose a novel fine-grained user perception approach, called Saga, which only needs a small amount of labelled IMU data to achieve stunning user perception accuracy.<n>The core idea of Saga is to first pre-train a backbone feature extraction model, utilizing the rich semantic information of different levels embedded in the massive unlabelled IMU data.<n>Saga can achieve over 90% accuracy of the full-fledged model trained on over ten thousands training samples with no additional system overhead.
• Score: 16.9766171115035
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Inertial measurement units (IMUs), have been prevalently used in a wide range of mobile perception applications such as activity recognition and user authentication, where a large amount of labelled data are normally required to train a satisfactory model. However, it is difficult to label micro-activities in massive IMU data due to the hardness of understanding raw IMU data and the lack of ground truth. In this paper, we propose a novel fine-grained user perception approach, called Saga, which only needs a small amount of labelled IMU data to achieve stunning user perception accuracy. The core idea of Saga is to first pre-train a backbone feature extraction model, utilizing the rich semantic information of different levels embedded in the massive unlabelled IMU data. Meanwhile, for a specific downstream user perception application, Bayesian Optimization is employed to determine the optimal weights for pre-training tasks involving different semantic levels. We implement Saga on five typical mobile phones and evaluate Saga on three typical tasks on three IMU datasets. Results show that when only using about 100 training samples per class, Saga can achieve over 90% accuracy of the full-fledged model trained on over ten thousands training samples with no additional system overhead.

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