Abstract
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.
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References
Karantonis, D.M., Narayanan, M.R., Mathie, M., Lovell, N.H., Celler, B.G.: Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring. IEEE Trans. on Information Tecnhology in Biomedicine 10(1) (2006)
Bouten, C.V., Koekkoek, K.T., Verduin, M., Kodde, R., Janssen, J.D.: A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity. IEEE Trans. Biomed. Eng. 44(3), 136–147 (1997)
Mathie, M.J., Coster, A.C.F., Lovell, N.H., Celler, B.G.: A pilot study of long term monitoring of human movements in the home using accelerometry. J. Telemed. Telecare 10, 144–151 (2004)
Fahrenberg, J., Foerster, F., Smeja, M., Muller, W.: Assessment of posture and motion by multichannel piezoresistive accelerometer recordings. Psychophysiol. 34, 607–612 (1997)
Foerster, F., Fahrenberg, J.: Motion pattern and posture: Correctly assessed by calibrated accelerometers. Behav. Res. Meth. Instrum. Comput. 32, 450–457 (2000)
Veltink, P.H., Bussmann, H.B., de Vries, W., Martens, W.L., van Lummel, R.C.: Detection of static and dynamic activities using uniaxial accelerometers. IEEE Trans. Rehabil. Eng. 4(4), 375–385 (1996)
Song, Y., Shin, S., Kim, S., Lee, D., Lee, K.H.: Speed estimation from a tri-axial accelerometer using neural networks. In: 29th annual international conference of the IEEE EMBS (2007)
Yang, J.-Y., Wang, J.-S., Chen, Y.-P.: Using acceleration measurements for activity recognition: an effective learning algorithm for constructing neural classifiers. Pattern Recognition Letters (2008)
Ravi, N., Dandekar, N., Mysore, P., Littman, M.L.: Activity recognition from accelerometer data. American Association for Artificial Intelligence (2005)
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Brezmes, T., Gorricho, JL., Cotrina, J. (2009). Activity Recognition from Accelerometer Data on a Mobile Phone. In: Omatu, S., et al. Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living. IWANN 2009. Lecture Notes in Computer Science, vol 5518. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02481-8_120
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DOI: https://doi.org/10.1007/978-3-642-02481-8_120
Publisher Name: Springer, Berlin, Heidelberg
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