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VALERIAN: Invariant Feature Learning for IMU Sensor-based Human Activity Recognition in the Wild
In this work, we examine two in-the-wild HAR datasets and DivideMix, a state-of-the-art learning with noise labels (LNL) method to understand the extent and impacts of noisy labels in training data.
CROMOSim: A Deep Learning-based Cross-modality Inertial Measurement Simulator
With the prevalence of wearable devices, inertial measurement unit (IMU) data has been utilized in monitoring and assessment of human mobility such as human activity recognition (HAR).
Invariant Feature Learning for Sensor-based Human Activity Recognition
In recent years, many deep models have been applied to HAR problems.
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