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Found 10 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 of Regularization-based Continual Learning Approaches: Application to HAR
However, they remain very specific and difficult to compare.
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.
Federated Continual Learning through distillation in pervasive computing
Current solutions rely on the availability of large amounts of stored data at the client side in order to fine-tune the models sent by the server.
Federated Learning and catastrophic forgetting in pervasive computing: demonstration in HAR domain
Federated Learning has been introduced as a new machine learning paradigm enhancing the use of local devices.
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.
A distillation-based approach integrating continual learning and federated learning for pervasive services
Federated Learning, a new machine learning paradigm enhancing the use of edge devices, is receiving a lot of attention in the pervasive community to support the development of smart services.
