Activity Recognition from Accelerometer Data on a Mobile Phone

Real-time monitoring of human movements can be easily envisaged as a useful tool for many purposes and future applications. This paper presents the implementation of a real-time classification system for some basic human movements using a conventional mobile phone equipped with an accelerometer. The aim of this study was to check the present capacity of conventional mobile phones to execute in real-time all the necessary pattern recognition algorithms to classify the corresponding human movements. No server processing data is involved in this approach, so the human monitoring is completely decentralized and only an additional software will be required to remotely report the human monitoring. The feasibility of this approach opens a new range of opportunities to develop new applications at a reasonable low-cost.

[1]  P H Veltink,et al.  Detection of static and dynamic activities using uniaxial accelerometers. , 1996, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[2]  J. D. Janssen,et al.  A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity , 1997, IEEE Transactions on Biomedical Engineering.

[3]  J. Fahrenberg,et al.  Assessment of posture and motion by multichannel piezoresistive accelerometer recordings. , 1997, Psychophysiology.

[4]  Friedrich Foerster,et al.  Motion pattern and posture: Correctly assessed by calibrated accelerometers , 2000, Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc.

[5]  M. Mathie,et al.  A pilot study of long-term monitoring of human movements in the home using accelerometry , 2004, Journal of telemedicine and telecare.

[6]  Michael L. Littman,et al.  Activity Recognition from Accelerometer Data , 2005, AAAI.

[7]  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.

[8]  Doheon Lee,et al.  Speed Estimation From a Tri-axial Accelerometer Using Neural Networks , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[9]  Jeen-Shing Wang,et al.  Using acceleration measurements for activity recognition: An effective learning algorithm for constructing neural classifiers , 2008, Pattern Recognit. Lett..