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@inBook{
 title = {Ultra-Low Power Context Recognition Fusing Sensor Data from an Energy-Neutral Smart Watch},
 type = {inBook},
 year = {2016},
 identifiers = {[object Object]},
 keywords = {context-recognition,sensor-fusion,smartwatch,ultra-low-power},
 pages = {331-343},
 volume = {170},
 websites = {http://dx.doi.org/10.1007/978-3-319-47075-7_38},
 publisher = {Springer International Publishing},
 series = {Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering},
 editors = {[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]},
 id = {a985aefb-ad83-3794-8b4e-75f1241234c9},
 created = {2018-07-12T21:32:28.356Z},
 file_attached = {false},
 profile_id = {f954d000-ce94-3da6-bd26-b983145a920f},
 group_id = {b0b145a3-980e-3ad7-a16f-c93918c606ed},
 last_modified = {2018-07-12T21:32:28.356Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Michele:context16},
 source_type = {incollection},
 private_publication = {false},
 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}
}