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This paper presents the development of a system based on computational intelligence techniques and on an accelerometer to perform, in a comfortable and non-intrusive manner, the recognition of basic movements of a person's routine. The information provided by this system can be directed to support promoting health and well-being of the individual, as well as diagnosing and remote patient monitoring. The system provides an overall success rate in recognition of movements around 93% by using support vector machines for signal classification and Fisher's discriminant ratio to select the most significant features.

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