Similarity Embedding Networks for Robust Human Activity Recognition
This work addresses the challenge of robust human activity recognition for applications like healthcare or fitness tracking, but it is incremental as it builds on existing deep learning methods with a focus on embedding techniques.
The paper tackles the problem of limited generalization in human activity recognition due to scarce high-quality labeled data by proposing a similarity embedding network trained with pairwise similarity loss, which significantly outperforms state-of-the-art models on public datasets and shows robustness to mislabeled samples.
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.