Ultra-Low Power Context Recognition Fusing Sensor Data from an Energy-Neutral Smart Watch. Magno, M., Cavigelli, L., Andri, R., & Benini, L. Volume 170. Ultra-Low Power Context Recognition Fusing Sensor Data from an Energy-Neutral Smart Watch, pages 331-343. Springer International Publishing, 2016.
Ultra-Low Power Context Recognition Fusing Sensor Data from an Energy-Neutral Smart Watch [link]Website  abstract   bibtex   
Today sensors and wearable technologies are gaining popularity, with people increasingly surrounded by ” smart” objects. Machine learning is used with great success in wearable devices and sensors in several real-world applications. In this paper we address the challenges of context recognition on low energy and self-sustainable wearable devices. We present an energy efficient multi-sensor context recognition system based on decision tree to classify 3 different indoor or outdoor contexts. An ultra-low power smart watch provided with a micro-power camera, microphone, accelerometer, and temperature sensors has been used to real field tests. Experimental results demonstrate both high mean accuracy of 81.5 % (up to 89 % peak) and low energy consumption (only 2.2 mJ for single classification) of the solution, and the possibility to achieve a self-sustainable system in combination with body worn energy harvesters.
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 publisher = {Springer International Publishing},
 series = {Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering},
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 abstract = {Today sensors and wearable technologies are gaining popularity, with people increasingly surrounded by ” smart” objects. Machine learning is used with great success in wearable devices and sensors in several real-world applications. In this paper we address the challenges of context recognition on low energy and self-sustainable wearable devices. We present an energy efficient multi-sensor context recognition system based on decision tree to classify 3 different indoor or outdoor contexts. An ultra-low power smart watch provided with a micro-power camera, microphone, accelerometer, and temperature sensors has been used to real field tests. Experimental results demonstrate both high mean accuracy of 81.5 % (up to 89 % peak) and low energy consumption (only 2.2 mJ for single classification) of the solution, and the possibility to achieve a self-sustainable system in combination with body worn energy harvesters.},
 bibtype = {inBook},
 author = {Magno, Michele and Cavigelli, Lukas and Andri, Renzo and Benini, Luca},
 book = {Internet of Things. IoT Infrastructures}
}
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