Easy Ensemble: Simple Deep Ensemble Learning for Sensor-Based Human Activity Recognition

TL;DR

This paper introduces Easy Ensemble, a simplified deep ensemble learning method for sensor-based human activity recognition that improves performance without complex procedures.

Contribution

The study proposes Easy Ensemble, a single-model deep ensemble approach with techniques like input variationer and channel shuffle for HAR.

Findings

EE outperforms conventional ensemble methods on benchmark HAR datasets.

Techniques like input variationer and channel shuffle enhance ensemble effectiveness.

EE simplifies deep ensemble implementation, reducing computational costs.

Abstract

Sensor-based human activity recognition (HAR) is a paramount technology in the Internet of Things services. HAR using representation learning, which automatically learns a feature representation from raw data, is the mainstream method because it is difficult to interpret relevant information from raw sensor data to design meaningful features. Ensemble learning is a robust approach to improve generalization performance; however, deep ensemble learning requires various procedures, such as data partitioning and training multiple models, which are time-consuming and computationally expensive. In this study, we propose Easy Ensemble (EE) for HAR, which enables the easy implementation of deep ensemble learning in a single model. In addition, we propose various techniques (input variationer, stepwise ensemble, and channel shuffle) for the EE. Experiments on a benchmark dataset for HAR demonstrated the effectiveness of EE and various techniques and their characteristics compared with conventional ensemble learning methods.