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The proliferation of IoT and mobile devices equipped with heterogeneous sensors has enabled new applications that rely on the fusion of time-series data generated by multiple sensors with different modalities. While there are promising deep neural network architectures for multimodal fusion, their performance falls apart quickly in the presence of consecutive missing data and noise across multiple modalities/sensors, the issues that are prevalent in realworld settings. We propose Centaur, a multimodal fusion model for human activity recognition (HAR) that is robust to these data quality issues. Centaur combines a data cleaning module, which is a denoising autoencoder with convolutional layers, and a multimodal fusion module, which is a deep convolutional neural network with the self-attention mechanism to capture cross-sensor correlation. We train Centaur using a stochastic data corruption scheme and evaluate it on three datasets that contain data generated by multiple inertial measurement units. We show that Centaur's data cleaning module outperforms two state-of-the-art autoencoder-based architectures, and its multimodal fusion module outperforms four strong baselines. Compared to two robust fusion architectures from the related work, Centaur is more robust especially to consecutive missing data that occur in multiple sensor channels, achieving on average 11.59-17.52% higher accuracy in the HAR task.

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