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Found 4 papers, 2 papers with code
Comparing Self-Supervised Learning Techniques for Wearable Human Activity Recognition
Although supervised learning methods are the most effective in this task, their effectiveness is constrained to using a large amount of labeled data during training.
Combining Public Human Activity Recognition Datasets to Mitigate Labeled Data Scarcity
In this paper, we propose a novel strategy to combine publicly available datasets with the goal of learning a generalized HAR model that can be fine-tuned using a limited amount of labeled data on an unseen target domain.
Personalized Semi-Supervised Federated Learning for Human Activity Recognition
Indeed, FedHAR combines active learning and label propagation to semi-automatically annotate the local streams of unlabeled sensor data, and it relies on FL to build a global activity model in a scalable and privacy-aware fashion.
Context-driven Active and Incremental Activity Recognition
While the majority of the proposed techniques are based on supervised learning, semi-supervised approaches are being considered to significantly reduce the size of the training set required to initialize the recognition model.
