@inproceedings{shahmohammadi_smartwatch_2017,
	address = {Philadelphia, PA},
	title = {Smartwatch based activity recognition using active learning.},
	abstract = {—Human activity monitoring has become widely
popular in recent years, and has been utilized in a vast number of
fields and applications. Most of the activity recognition algorithms
proposed have emphasized the use of inertial sensors in
smartphone devices or other bodily-worn sensors. However,
wearable inertial sensors are not interactive, and smartphones are
not easily worn. Thus, with the advancement of smartwatches,
unique opportunities exist to provide user interaction and highly
accurate personalized activity recognition. Through the use of
Active Learning, an interactive machine learning technique,
specific behaviors can be learned by querying for unknown
actions. This paper describes a smartwatch-based active learning
method for activity recognition to identify 5 commonly performed
daily activities. The results of this study revealed that this system
can obtain a 93.3\% accuracy across 12 participants. From our
results, we demonstrate that an interactive learning approach
using active learning in smartwatches has significant advantages
over smartphones and other devices for activity recognition tasks.},
	booktitle = {2017 {IEEE}/{ACM} {Conference} on {Connected} {Health}: {Applications}, {Systems} and {Engineering} {Technologies},},
	author = {Shahmohammadi, F. and Hosseini, A. and King, C. E. and Sarrafzadeh, M.},
	year = {2017},
}

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