User-friendly system for recognition of activities with an accelerometer

Monitoring of a person's daily activities can provide valuable information for health care and prevention and can be an important supportive application in the field of ambient assisted living (AAL). The goals of this study are the classification of postures and activities using knowledge-based methods as well as the evaluation of the performance of these methods. The acceleration data are gained by a single tri-axial accelerometer, which is mounted on a specific position on the test subject. A data set for training and testing was gained by collecting data from subjects, who performed varying postures and activities. For these purposes, three different knowledge-based (decision tree and neural network) classification methods and a hybrid classifier were implemented, tested and evaluated. The results of the tests illustrated that the hybrid classifier performed best with an overall accuracy of 98.99%. The advantages of knowledge-based methods are the exchangeable knowledge base, which can be developed for different types of sensor positions and the state of health of the subject.

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

[2]  David G. Stork,et al.  Pattern classification, 2nd Edition , 2000 .

[3]  E K Antonsson,et al.  The frequency content of gait. , 1985, Journal of biomechanics.

[4]  Michael Georgiopoulos,et al.  OPTIMAL FEED-FORWARD NEURAL NETWORK ARCHITECTURES , .

[5]  F. Racioppi,et al.  Physical Activity and Health in Europe: Evidence for Action , 2006 .

[6]  M Struck,et al.  Automatic emergency detection using commercial accelerometers and knowledge-based methods , 2009, 2009 36th Annual Computers in Cardiology Conference (CinC).

[7]  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.

[8]  Yen-Ping Chen,et al.  Online classifier construction algorithm for human activity detection using a tri-axial accelerometer , 2008, Appl. Math. Comput..

[9]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[10]  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.

[11]  Ilkka Korhonen,et al.  Detection of Daily Activities and Sports With Wearable Sensors in Controlled and Uncontrolled Conditions , 2008, IEEE Transactions on Information Technology in Biomedicine.

[12]  M. Mathie,et al.  Detection of daily physical activities using a triaxial accelerometer , 2003, Medical and Biological Engineering and Computing.

[13]  Eliathamby Ambikairajah,et al.  Classification of a known sequence of motions and postures from accelerometry data using adapted Gaussian mixture models. , 2006, Physiological measurement.

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

[15]  Robert E. Davis,et al.  Statistics for the evaluation and comparison of models , 1985 .

[16]  A. Bauman,et al.  Health-enhancing physical activity across European Union countries: the Eurobarometer study , 2006, Journal of Public Health.

[17]  Ling Bao,et al.  Activity Recognition from User-Annotated Acceleration Data , 2004, Pervasive.

[18]  M. Makikawa,et al.  Development of an ambulatory physical activity and behavior map monitoring system , 1996, Proceedings of 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[19]  B. Celler,et al.  Accelerometry Based Classification of Walking Patterns Using Time-frequency Analysis , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.