Learning Multi-level Features For Sensor-based Human Action Recognition

Understand

This paper proposes a multi-level feature learning framework for human action recognition using a single body-worn inertial sensor.

• The framework consists of three phases, respectively designed to analyze signal-based (low-level), components (mid-level) and semantic (high-level) information.
• Low-level features capture the time and frequency domain property while mid-level representations learn the composition of the action.
• The Max-margin Latent Pattern Learning (MLPL) method is proposed to learn high-level semantic descriptions of latent action patterns as the output of our framework.