Aparajita Saraf


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IMU2CLIP: Language-grounded Motion Sensor Translation with Multimodal Contrastive Learning
Seungwhan Moon | Andrea Madotto | Zhaojiang Lin | Aparajita Saraf | Amy Bearman | Babak Damavandi
Findings of the Association for Computational Linguistics: EMNLP 2023

We present IMU2CLIP, a novel pre-training approach to align Inertial Measurement Unit (IMU) motion sensor recordings with text and video, by projecting them into the joint representation space of Contrastive Language-Image Pre-training (CLIP). The proposed approach allows IMU2CLIP to translate human motions (as measured by IMU sensors) into their corresponding textual descriptions and videos – while preserving the transitivity across these modalities. We introduce several new IMU-based Wearable AI applications such as motion-based media search, or an LM-based multimodal reasoning with motion sensor data – all using text as the grounding platform. In addition, we show that IMU2CLIP significantly improves downstream performances when fine-tuned for each application, demonstrating its universal usage as a new pre-trained resource. Our code and models will be released publicly.