CORREDOR, A mobile Human-Centric Sensing System for Activity Recognition.
Luis G. Jaimes, Idalides J. Vergara-Laurens · 2015
This paper presents Corredor, a human-centricsensing system that encourage people’s physical activity. The main objective of Corredor is to help people, that suffer obesity, during their workout as part of their treatment. Corredor uses phone’s embedded sensors along with machine learning algorithms to recognize human activities such as running, walking and standing. Corrredor runs enterally in the user’s phone and does not require any external server processing. In addition, Corredor displays on the screen the followed route by the user, indicating the segments where the user was running, walking or standing. The system computes a set of 64 features from realtime accelerometer data using a 5 seconds sliding window with 50% of overlapping. The computed features are used to train a C4.5 decision tree which in turns is used to recognize workout activities. After system evaluation, our results show that Corredor achieves up to 93.7% overall accuracy. Finally, the application saves the historical data and is able to show them using Google Maps.