  Deep neural networks are parametrized by several thousands or millions of
parameters, and have shown tremendous success in many classification problems.
However, the large number of parameters makes it difficult to integrate these
models into edge devices such as smartphones and wearable devices. To address
this problem, knowledge distillation (KD) has been widely employed, that uses a
pre-trained high capacity network to train a much smaller network, suitable for
edge devices. In this paper, for the first time, we study the applicability and
challenges of using KD for time-series data for wearable devices. Successful
application of KD requires specific choices of data augmentation methods during
training. However, it is not yet known if there exists a coherent strategy for
choosing an augmentation approach during KD. In this paper, we report the
results of a detailed study that compares and contrasts various common choices
and some hybrid data augmentation strategies in KD based human activity
analysis. Research in this area is often limited as there are not many
comprehensive databases available in the public domain from wearable devices.
Our study considers databases from small scale publicly available to one
derived from a large scale interventional study into human activity and
sedentary behavior. We find that the choice of data augmentation techniques
during KD have a variable level of impact on end performance, and find that the
optimal network choice as well as data augmentation strategies are specific to
a dataset at hand. However, we also conclude with a general set of
recommendations that can provide a strong baseline performance across
databases.
