Research Article
ClimbTheWorld: Real-time stairstep counting to increase physical activity
@INPROCEEDINGS{10.4108/icst.mobiquitous.2014.258013, author={Fabio Aiolli and Matteo Ciman and Michele Donini and Ombretta Gaggi}, title={ClimbTheWorld: Real-time stairstep counting to increase physical activity}, proceedings={11th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services}, publisher={ICST}, proceedings_a={MOBIQUITOUS}, year={2014}, month={11}, keywords={activity recognition energy consumption mobile computing ubiquitous applications}, doi={10.4108/icst.mobiquitous.2014.258013} }- Fabio Aiolli
Matteo Ciman
Michele Donini
Ombretta Gaggi
Year: 2014
ClimbTheWorld: Real-time stairstep counting to increase physical activity
MOBIQUITOUS
ICST
DOI: 10.4108/icst.mobiquitous.2014.258013
Abstract
The increasing number of people that are overweight due to a sedentary life requires persuasive strategies to convince people to change their behaviors. In this paper, we present a machine learning based technique to recognize and count stairsteps when a person climbs or descends stairs. This technique has been used as part of ClimbTheWorld, a real-time smartphone application that aims at persuading people to use stairs instead of elevators or escalators, since an engaging activity has more chance to change people's life habits. We perform a fine-grained analysis by exploiting smartphone sensors to recognize single stairsteps. Data-dependent sliding windows are used facilitating the learning process and reducing the computational cost. Finally, energy consumption is widely investigated to optimize the trade-off between classification precision and battery usage, to avoid exhausting smartphone battery.
