Abstract
In wearable computing environments, a wearable computer runs various applications using various sensors (wearable sensors). In the area of context awareness, though various systems using accelerometers have been proposed to recognize very minute motions and states, energy consumption was not taken into consideration. We propose a context-aware system that reduces energy consumption. In life, the granularity of required contexts differs according to the situation. Therefore, the proposed system changes the granularity of cognitive contexts of a user’s situation and supplies power on the basis of the optimal sensor combination. Higher accuracy is achieved with fewer sensors. In addition, in proportion to the remainder of power resources, the proposed system reduces the number of sensors within the tolerance of accuracy. Moreover, the accuracy is improved by considering context transition. Even if the number of sensors changes, no extra classifiers or training data are required because the data for shutting off sensors is complemented by our proposed algorithm. By using our system, power consumption can be reduced without large losses in accuracy.
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Murao, K., Terada, T., Takegawa, Y., Nishio, S. (2008). A Context-Aware System that Changes Sensor Combinations Considering Energy Consumption. In: Indulska, J., Patterson, D.J., Rodden, T., Ott, M. (eds) Pervasive Computing. Pervasive 2008. Lecture Notes in Computer Science, vol 5013. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79576-6_12
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DOI: https://doi.org/10.1007/978-3-540-79576-6_12
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