  Deep neural network is an effective choice to automatically recognize human
actions utilizing data from various wearable sensors. These networks automate
the process of feature extraction relying completely on data. However, various
noises in time series data with complex inter-modal relationships among sensors
make this process more complicated. In this paper, we have proposed a novel
multi-stage training approach that increases diversity in this feature
extraction process to make accurate recognition of actions by combining
varieties of features extracted from diverse perspectives. Initially, instead
of using single type of transformation, numerous transformations are employed
on time series data to obtain variegated representations of the features
encoded in raw data. An efficient deep CNN architecture is proposed that can be
individually trained to extract features from different transformed spaces.
Later, these CNN feature extractors are merged into an optimal architecture
finely tuned for optimizing diversified extracted features through a combined
training stage or multiple sequential training stages. This approach offers the
opportunity to explore the encoded features in raw sensor data utilizing
multifarious observation windows with immense scope for efficient selection of
features for final convergence. Extensive experimentations have been carried
out in three publicly available datasets that provide outstanding performance
consistently with average five-fold cross-validation accuracy of 99.29% on UCI
HAR database, 99.02% on USC HAR database, and 97.21% on SKODA database
outperforming other state-of-the-art approaches.
