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Fine-grained Human Activity Recognition Using Virtual On-body Acceleration Data
In this work we first introduce a measure to quantitatively assess the subtlety of human movements that are underlying activities of interest--the motion subtlety index (MSI)--which captures local pixel movements and pose changes in the vicinity of target virtual sensor locations, and correlate it to the eventual activity recognition accuracy.
Assessing the State of Self-Supervised Human Activity Recognition using Wearables
As such, self-supervision, i. e., the paradigm of 'pretrain-then-finetune' has the potential to become a strong alternative to the predominant end-to-end training approaches, let alone hand-crafted features for the classic activity recognition chain.
Towards Using Unlabeled Data in a Sparse-coding Framework for Human Activity Recognition
We propose a sparse-coding framework for activity recognition in ubiquitous and mobile computing that alleviates two fundamental problems of current supervised learning approaches.
