Self-supervised Human Activity Recognition (HAR) has been gradually gaining a lot of attention in ubiquitous computing community. Its current focus primarily lies in how to overcome the challenge of manually labeling complicated and intricate sensor data from wearable devices, which is often hard to interpret. However, current self-supervised algorithms encounter three main challenges: performance variability caused by data augmentations in contrastive learning paradigm, limitations imposed by traditional self-supervised models, and the computational load deployed on wearable devices by current mainstream transformer encoders. To comprehensively tackle these challenges, this paper proposes a powerful self-supervised approach for HAR from a novel perspective of denoising autoencoder, the first of its kind to explore how to reconstruct masked sensor data built on a commonly employed, well-designed, and computationally efficient fully convolutional network. Extensive experiments demonstrate that our proposed Masked Convolutional AutoEncoder (MaskCAE) outperforms current state-of-the-art algorithms in self-supervised, fully supervised, and semi-supervised situations without relying on any data augmentations, which fills the gap of masked sensor data modeling in HAR area. Visualization analyses show that our MaskCAE could effectively capture temporal semantics in time series sensor data, indicating its great potential in modeling abstracted sensor data. An actual implementation is evaluated on an embedded platform.