Attention-Based Deep Learning Framework for Human Activity Recognition with User Adaptation

Understand

Sensor-based human activity recognition (HAR) requires to predict the action of a person based on sensor-generated time series data.

• HAR has attracted major interest in the past few years, thanks to the large number of applications enabled by modern ubiquitous computing devices.
• While several techniques based on hand-crafted feature engineering have been proposed, the current state-of-the-art is represented by deep learning architectures that automatically obtain high level representations and that use recurrent neural networks (RNNs) to extract temporal dependencies in the input.
• RNNs have several limitations, in particular in dealing with long-term dependencies.