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Found 8 papers, 1 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.
ContextGPT: Infusing LLMs Knowledge into Neuro-Symbolic Activity Recognition Models
Neuro-Symbolic AI (NeSy) provides an interesting research direction to mitigate this issue, by infusing common-sense knowledge about human activities and the contexts in which they can be performed into HAR deep learning classifiers.
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.
Neuro-Symbolic Approaches for Context-Aware Human Activity Recognition
In this work, we propose a novel approach based on a semantic loss function that infuses knowledge constraints in the HAR model during the training phase, avoiding symbolic reasoning during classification.
SelfAct: Personalized Activity Recognition based on Self-Supervised and Active Learning
Supervised Deep Learning (DL) models are currently the leading approach for sensor-based Human Activity Recognition (HAR) on wearable and mobile devices.
Ultrasound Detection of Subquadricipital Recess Distension
Joint bleeding is a common condition for people with hemophilia and, if untreated, can result in hemophilic arthropathy.
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.
