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Sensor-based human activity recognition (HAR) is a paramount technology inthe Internet of Things services. HAR using representation learning, whichautomatically learns a feature representation from raw data, is the mainstreammethod because it is difficult to interpret relevant information from rawsensor data to design meaningful features. Ensemble learning is a robustapproach to improve generalization performance; however, deep ensemble learningrequires various procedures, such as data partitioning and training multiplemodels, which are time-consuming and computationally expensive. In this study,we propose Easy Ensemble (EE) for HAR, which enables the easy implementation ofdeep ensemble learning in a single model. In addition, we propose input maskingas a method for diversifying the input for EE. Experiments on a benchmarkdataset for HAR demonstrated the effectiveness of EE and input masking andtheir characteristics compared with conventional ensemble learning methods.

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