MPTrain: a mobile, music and physiology-based personal trainer
Proceedings of the 8th conference on Human-computer interaction with mobile devices and services
Junior: The Stanford entry in the Urban Challenge
Journal of Field Robotics - Special Issue on the 2007 DARPA Urban Challenge, Part II
Nericell: rich monitoring of road and traffic conditions using mobile smartphones
Proceedings of the 6th ACM conference on Embedded network sensor systems
ISWC '07 Proceedings of the 2007 11th IEEE International Symposium on Wearable Computers
A step counter service for Java-enabled devices using a built-in accelerometer
Proceedings of the 1st International Workshop on Context-Aware Middleware and Services: affiliated with the 4th International Conference on Communication System Software and Middleware (COMSWARE 2009)
UIC'10 Proceedings of the 7th international conference on Ubiquitous intelligence and computing
Heart Rate Prediction Model Based on Physical Activities Using Evolutionary Neural Network
ICGEC '10 Proceedings of the 2010 Fourth International Conference on Genetic and Evolutionary Computing
A robust dead-reckoning pedestrian tracking system with low cost sensors
PERCOM '11 Proceedings of the 2011 IEEE International Conference on Pervasive Computing and Communications
uDirect: A novel approach for pervasive observation of user direction with mobile phones
PERCOM '11 Proceedings of the 2011 IEEE International Conference on Pervasive Computing and Communications
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Aiming to realize the application which supports users to enjoy walking with an appropriate physical load, we propose a method to estimate physical load and its variation during walking only with available functions of a smartphone. Since physical load has a linear relationship with heart rate, our purpose is to estimate heart rate with a smartphone. To this end, we build heart rate prediction models which predict heart rate variation from walking data including acceleration and walking speed by machine learning. In order to track unexpected change of physical load, we focus attention on oxygen uptake which has a similar property to heart rate and devise a novel technique to estimate the oxygen uptake from acceleration and GPS data so that it is used as an input of the model. Moreover, to adapt to difference of heart rate variation among individuals, we devise techniques to optimize parameters for each profile-based category of users and to normalize heart rate to absorb individual difference. We applied the proposed method to actual walking data on various routes by different persons and confirmed that the method estimates heart rate variation with the mean error of less than 7 beat per minute.