  Deep learning models for human activity recognition (HAR) based on sensor
data have been heavily studied recently. However, the generalization ability of
deep models on complex real-world HAR data is limited by the availability of
high-quality labeled activity data, which are hard to obtain. In this paper, we
design a similarity embedding neural network that maps input sensor signals
onto real vectors through carefully designed convolutional and LSTM layers. The
embedding network is trained with a pairwise similarity loss, encouraging the
clustering of samples from the same class in the embedded real space, and can
be effectively trained on a small dataset and even on a noisy dataset with
mislabeled samples. Based on the learned embeddings, we further propose both
nonparametric and parametric approaches for activity recognition. Extensive
evaluation based on two public datasets has shown that the proposed similarity
embedding network significantly outperforms state-of-the-art deep models on HAR
classification tasks, is robust to mislabeled samples in the training set, and
can also be used to effectively denoise a noisy dataset.
