Semi-Supervised Convolutional Neural Networks for Human Activity Recognition
This work addresses the challenge of generalization in activity recognition for real-world applications by combining feature learning with semi-supervised learning, though it is incremental as it builds on existing CNNs and semi-supervised techniques.
The paper tackles the problem of limited labeled data for human activity recognition by proposing semi-supervised convolutional neural networks that learn features from raw sensor data, resulting in up to 18% improvement in mean F1-score over supervised and traditional semi-supervised methods on three real-world datasets.
Labeled data used for training activity recognition classifiers are usually limited in terms of size and diversity. Thus, the learned model may not generalize well when used in real-world use cases. Semi-supervised learning augments labeled examples with unlabeled examples, often resulting in improved performance. However, the semi-supervised methods studied in the activity recognition literatures assume that feature engineering is already done. In this paper, we lift this assumption and present two semi-supervised methods based on convolutional neural networks (CNNs) to learn discriminative hidden features. Our semi-supervised CNNs learn from both labeled and unlabeled data while also performing feature learning on raw sensor data. In experiments on three real world datasets, we show that our CNNs outperform supervised methods and traditional semi-supervised learning methods by up to 18% in mean F1-score (Fm).