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.

Design and validation of a minimal complexity algorithm for stair step counting

Coluzzi D.;Rivolta M. W.;Mastropietro A.;Porcelli S.;Rizzo G.;Sassi R.
2020

Abstract

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.
COMPUTERS
Active ageing
Human activity recognition
Stair step counting
Wearable sensors
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/1205547
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