Domain Knowledge Based Weakly Self-Supervised Human Activity Recognition With Wearables

Recognizing different types of human activities from wearable sensor-based data remains a challenging research topic in ubiquitous computing, despite the availability of embedded sensors in smartphones and wearable devices. The lack of labeled data poses a significant hurdle for existing human activity recognition (HAR) systems that heavily rely on supervised methods. In this paper, we propose a novel weakly self-supervised approach consisting of two stages. Firstly, our model leverages the inherent nature of human activities to project the data into an embedding space, grouping similar activities together. Secondly, the model is fine-tuned using similarity information in a few-shot learning fashion, enhancing the embeddings' discriminative power. This enables downstream classification or clustering tasks to benefit from the learned embeddings. We evaluate our framework on three benchmark datasets and demonstrate its effectiveness. Our approach achieves comparable performance to pure supervised techniques applied directly to fully labeled datasets, thereby aiding in identifying and categorizing underlying human activities. The results highlight the potential of our approach to improve clustering algorithms for activity recognition tasks in real-world scenarios with limited labeled data.