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

• URL: http://arxiv.org/abs/2203.04153v1
• Date: Tue, 8 Mar 2022 15:30:32 GMT
• Title: Easy Ensemble: Simple Deep Ensemble Learning for Sensor-Based Human Activity Recognition
• Authors: Tatsuhito Hasegawa, Kazuma Kondo
• Abstract summary: We propose Easy Ensemble (EE) for sensor-based human activity recognition (HAR) EE enables the easy implementation of deep ensemble learning in a single model. In addition, we propose input masking as a method for diversifying the input for EE.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 input masking as a method for diversifying the input for EE. Experiments on a benchmark dataset for HAR demonstrated the effectiveness of EE and input masking and their characteristics compared with conventional ensemble learning methods.

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