  Decoding human activity accurately from wearable sensors can aid in
applications related to healthcare and context awareness. The present
approaches in this domain use recurrent and/or convolutional models to capture
the spatio-temporal features from time-series data from multiple sensors. We
propose a deep neural network architecture that not only captures the
spatio-temporal features of multiple sensor time-series data but also selects,
learns important time points by utilizing a self-attention mechanism. We show
the validity of the proposed approach across different data sampling strategies
on six public datasets and demonstrate that the self-attention mechanism gave a
significant improvement in performance over deep networks using a combination
of recurrent and convolution networks. We also show that the proposed approach
gave a statistically significant performance enhancement over previous
state-of-the-art methods for the tested datasets. The proposed methods open
avenues for better decoding of human activity from multiple body sensors over
extended periods of time. The code implementation for the proposed model is
available at https://github.com/isukrit/encodingHumanActivity.
