randomHAR: Improving Ensemble Deep Learners for Human Activity
  Recognition with Sensor Selection and Reinforcement Learning
Yiran Huang Yexu Zhou Till Riedel Likun Fang Michael Beigl
Deep learning has proven to be an effective approach in the field of Human
activity recognition (HAR), outperforming other architectures that require
manual feature engineering. Despite recent advancements, challenges inherent to
HAR data, such as noisy data, intra-class variability and inter-class
similarity, remain. To address these challenges, we propose an ensemble method,
called randomHAR. The general idea behind randomHAR is training a series of
deep learning models with the same architecture on randomly selected sensor
data from the given dataset. Besides, an agent is trained with the
reinforcement learning algorithm to identify the optimal subset of the trained
models that are utilized for runtime prediction. In contrast to existing work,
this approach optimizes the ensemble process rather than the architecture of
the constituent models. To assess the performance of the approach, we compare
it against two HAR algorithms, including the current state of the art, on six
HAR benchmark datasets. The result of the experiment demonstrates that the
proposed approach outperforms the state-of-the-art method, ensembleLSTM.