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Recent advances in machine learning showed that pre-training representationsacquired via self-supervised learning could achieve high accuracy on tasks withsmall training data. Unlike in vision and natural language processing domains,such pre-training for IMU-based applications is challenging, as there are onlya few publicly available datasets with sufficient size and diversity to learngeneralizable representations. To overcome this problem, we propose IMG2IMU, anovel approach that adapts pre-train representation from large-scale images todiverse few-shot IMU sensing tasks. We convert the sensor data into visuallyinterpretable spectrograms for the model to utilize the knowledge gained fromvision. Further, we apply contrastive learning on an augmentation set wedesigned to learn representations that are tailored to interpreting sensordata. Our extensive evaluations on five different IMU sensing tasks show thatIMG2IMU consistently outperforms the baselines, illustrating that visionknowledge can be incorporated into a few-shot learning environment for IMUsensing tasks.

IMG2IMU: Applying Knowledge from Large-Scale Images to IMU Applications via Contrastive Learning