A neural network based application for remote monitoring of human behaviour

The daily behavior of an individual is an important indicator to identify and prevent health problems, so that some works are focusing their investigations on techniques to carry out remote and continuous monitoring of such human behaviors. In this context, our work presents a neural network based application for the automated classification of human movements. This application identifies six basic movements, from the signals obtained from a mobile triaxial accelerometer, and uses these movements to figure out behavioral patterns from their uses. The set of features used to characterize the accelerometer curves provided a classification accuracy of about 91%. Another contribution of this study was to raise up important practical issues that must be considered as extensions of this kind of intelligent monitoring application.

[1]  Merryn J Mathie,et al.  Accelerometry: providing an integrated, practical method for long-term, ambulatory monitoring of human movement , 2004, Physiological measurement.

[2]  Tae-Seong Kim,et al.  Accelerometer’s position independent physical activity recognition system for long-term activity monitoring in the elderly , 2010, Medical & Biological Engineering & Computing.

[3]  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.

[4]  Fernando Morgado Dias,et al.  On-line Training of Neural Networks: A Sliding Window Approach for the Levenberg-Marquardt Algorithm , 2005, IWINAC.

[5]  B. Steele,et al.  Quantitating physical activity in COPD using a triaxial accelerometer. , 2000, Chest.

[6]  Bart Vanrumste,et al.  Detection of nocturnal frontal lobe seizures in pediatric patients by means of accelerometers: A first study , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[7]  Frank Sposaro,et al.  iFall: An android application for fall monitoring and response , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[8]  Dong Xuan,et al.  PerFallD: A pervasive fall detection system using mobile phones , 2010, 2010 8th IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops).

[9]  J B Bussmann,et al.  Measuring physical strain during ambulation with accelerometry. , 2000, Medicine and science in sports and exercise.

[10]  Jeffrey M. Hausdorff,et al.  Gait variability and fall risk in community-living older adults: a 1-year prospective study. , 2001, Archives of physical medicine and rehabilitation.

[11]  B. G. Celler,et al.  Classification of basic daily movements using a triaxial accelerometer , 2004, Medical and Biological Engineering and Computing.

[12]  Alessio Vecchio,et al.  Monitoring of Human Movements for Fall Detection and Activities Recognition in Elderly Care Using Wireless Sensor Network: a Survey , 2010 .

[13]  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.

[14]  Nikolaos G. Bourbakis,et al.  Prognosis—A Wearable Health-Monitoring System for People at Risk: Methodology and Modeling , 2010, IEEE Transactions on Information Technology in Biomedicine.