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Smartphone-based transport mode detection for elderly care

: Cardoso, N.; Madureira, J.; Pereira, N.


Institute of Electrical and Electronics Engineers -IEEE-:
IEEE 18th International Conference on e-Health Networking, Applications and Services, Healthcom 2016 : 14-17 September 2016, Munich, Germany
Piscataway, NJ: IEEE, 2016
ISBN: 978-1-5090-3370-6
ISBN: 978-1-5090-3371-3
International Conference on e-Health Networking, Applications and Services (Healthcom) <18, 2016, Munich>
Fraunhofer AICOS ()

Smartphones are everywhere, and they are a very attractive platform to perform unobtrusive monitoring of users. In this work, we use common features of modern smartphones to build a human activity recognition (HAR) system for elderly care. We have built a classifier that detects the transport mode of the user including whether an individual is inactive, walking, in bus, in car, in train or in metro. We evaluated our approach using over 24 hours of transportation data from a group of 15 individuals. Our tests show that our classifier can detect the transportation mode with over 90% accuracy.