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 stai...

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Bibliographic Details
Published in:Computers (Basel) Vol. 9; no. 2; p. 31
Main Authors: Coluzzi, Davide, Rivolta, Massimo W., Mastropietro, Alfonso, Porcelli, Simone, Mauri, Marco L., Civiello, Marta T. L., Denna, Enrico, Rizzo, Giovanna, Sassi, Roberto
Format: Journal Article
Language:English
Published: Basel MDPI AG 2020
Subjects:
Accelerometers
active ageing
Aging
Algorithms
Barometers
Climbing
Complexity
Datasets
Elevators & escalators
Experiments
Fourier transforms
human activity recognition
Older people
Sensors
stair step counting
Stairways
Walking
wearable sensors
Wrist
ISSN:2073-431X, 2073-431X
Online Access:Get full text
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Summary: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.
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ISSN:2073-431X
2073-431X
DOI:10.3390/computers9020031