  Physical activity is recognized as an essential component of overall health.
One measure of physical activity, the step count, is well known as a predictor
of long-term morbidity and mortality. Step Counting (SC) is the automated
counting of the number of steps an individual takes over a specified period of
time and space. Due to the ubiquity of smartphones and smartwatches, most
current SC approaches rely on the built-in accelerometer sensors on these
devices. The sensor signals are analyzed as multivariate time series, and the
number of steps is calculated through a variety of approaches, such as
time-domain, frequency-domain, machine-learning, and deep-learning approaches.
Most of the existing approaches rely on dividing the input signal into windows,
detecting steps in each window, and summing the detected steps. However, these
approaches require the determination of multiple parameters, including the
window size. Furthermore, most of the existing deep-learning SC approaches
require ground-truth labels for every single step, which can be arduous and
time-consuming to annotate. To circumvent these requirements, we present a
novel SC approach utilizing many-to-one attention-based LSTM. With the proposed
LSTM network, SC is solved as a regression problem, taking the entire sensor
signal as input and the step count as the output. The analysis shows that the
attention-based LSTM automatically learned the pattern of steps even in the
absence of ground-truth labels. The experimental results on three publicly
available SC datasets demonstrate that the proposed method successfully counts
the number of steps with low values of mean absolute error and high values of
SC accuracy.
