A multi-task self-supervised approach trains a temporal CNN to detect transformations on sensory data, yielding features that match or exceed fully supervised performance in semi-supervised and transfer settings for smartphone-based HAR.
Deep convolutional neural networks on multichannel time series for human activity recognition
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Multi-task Self-Supervised Learning for Human Activity Detection
A multi-task self-supervised approach trains a temporal CNN to detect transformations on sensory data, yielding features that match or exceed fully supervised performance in semi-supervised and transfer settings for smartphone-based HAR.