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Pre-training representations acquired via self-supervised learning couldachieve high accuracy on even tasks with small training data. Unlike in visionand natural language processing domains, pre-training for IMU-basedapplications is challenging, as there are few public datasets with sufficientsize and diversity to learn generalizable representations. To overcome thisproblem, we propose IMG2IMU that adapts pre-trained representation fromlarge-scale images to diverse IMU sensing tasks. We convert the sensor datainto visually interpretable spectrograms for the model to utilize the knowledgegained from vision. We further present a sensor-aware pre-training method forimages that enables models to acquire particularly impactful knowledge for IMUsensing applications. This involves using contrastive learning on ouraugmentation set customized for the properties of sensor data. Our evaluationwith four different IMU sensing tasks shows that IMG2IMU outperforms thebaselines pre-trained on sensor data by an average of 9.6%p F1-score,illustrating that vision knowledge can be usefully incorporated into IMUsensing applications where only limited training data is available.

IMG2IMU: Translating Knowledge from Large-Scale Images to IMU Sensing Applications