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Found 2 papers, 1 papers with code
Unsupervised Deep Learning-based clustering for Human Activity Recognition
A large amount of data would be available due to the wide spread of mobile devices equipped with inertial sensors that can collect data to recognize human activities.
Homogenization of Existing Inertial-Based Datasets to Support Human Activity Recognition
The final aim of our work is the definition and implementation of a platform that integrates datasets of inertial signals in order to make available to the scientific community large datasets of homogeneous signals, enriched, when possible, with context information (e. g., characteristics of the subjects and device position).
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