  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.
