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

--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. Recent years have witnessed a broad range of user perception applications utilizing inertial measurement units (IMUs), including user authentication [1]-[4], activity recognition [5]- [7], and health monitoring [8], [9]. However, the efficacy of such applications hinges on the availability of expensive and accurately labelled IMU data, which is a requirement often deemed impractical [6], [10]. Given the huge amount of raw IMU data easily generated on mobile devices, it is natural to ask whether users of such mobile devices can be well perceived with very few or even no labelled IMU data, referred to as the IMU-based user perception (IUP) problem. A practical solution to this problem needs to meet the following three rigid requirements. First, the solution can access plenty of unlabelled IMU data but should only require a small amount of labelled data. Second, the solution should be able to achieve high accuracy over multiple user perception tasks simultaneously to meet the diverse user perception needs.