Consistency Based Weakly Self-Supervised Learning for Human Activity Recognition with Wearables
This addresses the challenge of limited labeled data in ubiquitous computing for human activity recognition, though it is incremental as it builds on existing self-supervised and few-shot learning techniques.
The paper tackles the problem of human activity recognition with unlabeled wearable sensor data by proposing a weakly self-supervised two-stage approach that learns embeddings from activity similarities and fine-tunes with few-shot learning, achieving comparable performance to supervised methods on three benchmark datasets.
While the widely available embedded sensors in smartphones and other wearable devices make it easier to obtain data of human activities, recognizing different types of human activities from sensor-based data remains a difficult research topic in ubiquitous computing. One reason for this is that most of the collected data is unlabeled. However, many current human activity recognition (HAR) systems are based on supervised methods, which heavily rely on the labels of the data. We describe a weakly self-supervised approach in this paper that consists of two stages: (1) In stage one, the model learns from the nature of human activities by projecting the data into an embedding space where similar activities are grouped together; (2) In stage two, the model is fine-tuned using similarity information in a few-shot learning fashion using the similarity information of the data. This allows downstream classification or clustering tasks to benefit from the embeddings. Experiments on three benchmark datasets demonstrate the framework's effectiveness and show that our approach can help the clustering algorithm achieve comparable performance in identifying and categorizing the underlying human activities as pure supervised techniques applied directly to a corresponding fully labeled data set.