Monitoring physical activity and energy expenditure with smartphones

Monitoring physical activity and energy expenditure is important for maintaining adequate activity levels with an impact in health and well-being. This paper presents a smartphone based method for classification of inactive postures and physical activities including the calculation of energy expenditure. The implemented solution considers two different positions for the smartphone, the user's pocket or belt. The signal from the accelerometer embedded in the smartphone is used to classify the activities resorting to a decision tree classifier. The average accuracy of the classification task for all activities is 99.5% for the pocket usage and 99.4% when the phone is used in the belt. Using the output of the activity classifier we also compute an estimation of the energy expended by the user. The proposed solution is a trustworthy smartphone based activity monitor, classifying the activities of daily living throughout the entire day and allowing to assess the associated energy expenditure without causing any change in user's routines.

[1]  Nigel H. Lovell,et al.  Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring , 2006, IEEE Transactions on Information Technology in Biomedicine.

[2]  James Fogarty,et al.  iLearn on the iPhone: Real-Time Human Activity Classification on Commodity Mobile Phones , 2008 .

[3]  Yoshihiro Kawahara,et al.  A Calorie Count Application for a Mobile Phone Based on METS Value , 2008, 2008 5th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks.

[4]  A C Pinheiro Volp,et al.  Energy expenditure: components and evaluation methods. , 2011, Nutricion hospitalaria.

[5]  Lei Gao,et al.  Activity recognition using dynamic multiple sensor fusion in body sensor networks , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[6]  Roy Want iPhone: Smarter Than the Average Phone , 2010, IEEE Pervasive Computing.

[7]  Edward Sazonov,et al.  Highly accurate classification of postures and activities by a shoe-based monitor through classification with rejection , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[8]  Bohn Stafleu van Loghum,et al.  Online … , 2002, LOG IN.

[9]  Tim Lüth,et al.  Accelerometer based real-time activity analysis on a microcontroller , 2011, 2011 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops.

[10]  Aiko M. Hormann,et al.  Programs for Machine Learning. Part I , 1962, Inf. Control..

[11]  Yeh-Liang Hsu,et al.  A Review of Accelerometry-Based Wearable Motion Detectors for Physical Activity Monitoring , 2010, Sensors.

[12]  Martin Klepal,et al.  Mobile Phone-Based Displacement Estimation for Opportunistic Localisation Systems , 2009, 2009 Third International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies.

[13]  Davide Anguita,et al.  Human Activity Recognition on Smartphones Using a Multiclass Hardware-Friendly Support Vector Machine , 2012, IWAAL.

[14]  Miguel A. Labrador,et al.  A Survey on Human Activity Recognition using Wearable Sensors , 2013, IEEE Communications Surveys & Tutorials.