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.
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.
@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}
}