Virtual Fusion With Contrastive Learning for Single-Sensor-Based Activity Recognition

Various types of sensors can be used for human activity recognition (HAR), and each of them has different strengths and weaknesses. Sometimes, a single sensor cannot fully observe the user’s motions from its perspective, which causes wrong predictions. While sensor fusion provides more information for HAR, it comes with many inherent drawbacks, such as user privacy and acceptance, costly setup, operation, and maintenance. To deal with this problem, we propose virtual fusion—a new method that takes advantage of unlabeled data from multiple time-synchronized sensors during training, but only needs one sensor for inference. Contrastive learning is adopted to exploit multimodal correlation, aiding unimodal classification. Virtual fusion gives significantly better accuracy than training with the same single sensor, and in some cases, it even surpasses actual fusion using multiple sensors at test time. We also extend this method to a more general version called actual fusion within virtual fusion (AFVF), which uses a subset of training sensors during inference. Our method achieves state-of-the-art accuracy and
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-score on UCI-HAR and PAMAP2 benchmark datasets. Implementation is available upon request.