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

Tatsuhito Hasegawa; Kazuma Kondo

doi:10.1109/jiot.2022.3222221

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arXiv · 2203.04153

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

Tatsuhito Hasegawa·Kazuma Kondo

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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.

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BibTeX RIS

Tatsuhito Hasegawa, Kazuma Kondo. 2022-03-08. Easy Ensemble: Simple Deep Ensemble Learning for Sensor-Based Human Activity Recognition. https://doi.org/10.1109/jiot.2022.3222221

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