The increasing popularity of wearable devices in
recent years means that a diverse range of physiological and functional
data can now be captured continuously for applications
in sports, wellbeing, and healthcare. This wealth of information
requires efficient methods of classification and analysis where
deep learning is a promising technique for large-scale data analytics.
Whilst deep learning has been successful in implementations
that utilize high performance computing platforms, its use on
low-power wearable devices is limited by resource constraints.
In this paper, we propose a deep learning methodology, which
combines features learnt from inertial sensor data together with
complementary information from a set of shallow features to
enable accurate and real-time activity classification. The design of
this combined method aims to overcome some of the limitations
present in a typical deep learning framework where on-node
computation is required. To optimize the proposed method for
real-time on-node computation, spectral domain pre-processing
is used before the data is passed onto the deep learning framework.
The classification accuracy of our proposed deep learning
approach is evaluated against state-of-the-art methods using both
laboratory and real world activity datasets. Our results show the
validity of the approach on different human activity datasets,
outperforming other methods, including the two methods used
within our combined pipeline. We also demonstrate that the
computation times for the proposed method are consistent with
the constraints of real-time on-node processing on smartphones
and a wearable sensor platform.