Power and accuracy trade-offs in sound-based context recognition systems
Pervasive and Mobile Computing
The Mobile Sensing Platform: An Embedded Activity Recognition System
IEEE Pervasive Computing
Activity monitoring using an intelligent mobile phone: a validation study
Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments
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Minimizing the number of computations a low-power device makes is important to achieve long battery life. In this paper we present a framework for a low-power device to minimize the number of calculations needed to detect and classify simple activities of daily living such as sitting, standing, walking, reaching, and eating. This technique uses wavelet analysis as part of the feature set extracted from accelerometer data. A log-likelihood ratio test and Hidden Markov Models (HMM) are used to detect transitions and classify different activities. A trade-off is made between power and accuracy.