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Deep Generative Domain Adaptation with Temporal Attention for Cross-User Activity Recognition
Addressing this oversight, our research presents the Deep Generative Domain Adaptation with Temporal Attention (DGDATA) method.
Deep Generative Domain Adaptation with Temporal Relation Knowledge for Cross-User Activity Recognition
To bridge this gap, our study introduces a Conditional Variational Autoencoder with Universal Sequence Mapping (CVAE-USM) approach, which addresses the unique challenges of time-series domain adaptation in HAR by relaxing the i. i. d.
Cross-user activity recognition using deep domain adaptation with temporal relation information
To address this challenge, we introduce the Deep Temporal State Domain Adaptation (DTSDA) model, an innovative approach tailored for time series domain adaptation in cross-user HAR.
Cross-user activity recognition via temporal relation optimal transport
$ and do not consider the knowledge of temporal relation hidden in time series data for aligning data distribution.
Machine Learning Techniques for Sensor-based Human Activity Recognition with Data Heterogeneity -- A Review
Sensor-based Human Activity Recognition (HAR) is crucial in ubiquitous computing, analysing behaviours through multi-dimensional observations.
