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 elderlyand in general, promoting active ageing. One of the relevant variables to be tracked is the number ofstair steps (single stair steps) performed daily, which is more challenging than counting flight of stairsand detecting stair climbing. In this study, we proposed a minimal complexity algorithm composedof a hierarchical classifier and a linear model to estimate the number of stair steps performedduring everyday activities. The algorithm was calibrated on accelerometer and barometer recordingsmeasured using a sensor platform worn at the wrist from 20 healthy subjects. It was then tested on10 older people, specifically enrolled for the study. The algorithm was then compared with otherthree state-of-the-art methods, which used the accelerometer, the barometer or both. The experimentsshowed the good performance of our algorithm (stair step counting error: 13.8%), comparable withthe 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 amemory footprint of about 4 kB.