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Found 6 papers, 3 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.
Evaluation and comparison of federated learning algorithms for Human Activity Recognition on smartphones
Unfortunately, however, the most popular federated learning algorithms have been shown not to be adapted to some highly heterogeneous pervasive computing environments.
Lightweight Transformers for Human Activity Recognition on Mobile Devices
Human Activity Recognition (HAR) on mobile devices has shown to be achievable with lightweight neural models learned from data generated by the user's inertial measurement units (IMUs).
Federated Self-Supervised Learning in Heterogeneous Settings: Limits of a Baseline Approach on HAR
In the case of supervised learning, labeling is entrusted to the clients.
A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and Comparison
About this, Federated Learning has been recently proposed for distributed model training in the edge.
