Design and Validation of a Minimal Complexity Algorithm for Stair Step Counting

Wearable sensors play a significant role for monitoring the functional ability of the elderly
and in general, promoting active ageing. One of the relevant variables to be tracked is the number of
stair steps (single stair steps) performed daily, which is more challenging than counting flight of stairs
and detecting stair climbing. In this study, we proposed a minimal complexity algorithm composed
of a hierarchical classifier and a linear model to estimate the number of stair steps performed
during everyday activities. The algorithm was calibrated on accelerometer and barometer recordings
measured using a sensor platform worn at the wrist from 20 healthy subjects. It was then tested on
10 older people, specifically enrolled for the study. The algorithm was then compared with other
three state-of-the-art methods, which used the accelerometer, the barometer or both. The experiments
showed the good performance of our algorithm (stair step counting error: 13.8%), comparable with
the best state-of-the-art (p > 0.05), but using a lower computational load and model complexity.
Finally, the algorithm was successfully implemented in a low-power smartwatch prototype with a
memory footprint of about 4 kB.