@article{
 title = {Sensor Placement Variations in Wearable Activity Recognition},
 type = {article},
 year = {2014},
 identifiers = {[object Object]},
 keywords = {activity-recognition,mobile,ubicomp,wearable},
 pages = {32-41},
 websites = {http://dx.doi.org/10.1109/mprv.2014.73},
 month = {10},
 publisher = {IEEE},
 id = {9c37a2f2-5de0-3e4b-b2e3-beb92ae88a2a},
 created = {2018-07-12T21:32:43.514Z},
 file_attached = {false},
 profile_id = {f954d000-ce94-3da6-bd26-b983145a920f},
 group_id = {b0b145a3-980e-3ad7-a16f-c93918c606ed},
 last_modified = {2018-07-12T21:32:43.514Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {kunze:placement},
 source_type = {article},
 private_publication = {false},
 abstract = {This article explores how placement variations in user-carried electronic appliances influence human action recognition and how such influence can be mitigated. The authors categorize possible variations into three classes: placement on different body parts (such as a jacket pocket versus a hip holster versus a trouser pocket), small displacement within a given coarse location (such as a device shifting in a pocket), and different orientations. For each of these variations, they present a systematic evaluation of the impact on human action recognition and give an overview of possible approaches to deal with them. They conclude with a detailed practical example on how to compensate for on-body placements variations that builds on an extension of their previous work. This article is part of a special issue on wearable computing.},
 bibtype = {article},
 author = {Kunze, Kai and Lukowicz, Paul},
 journal = {IEEE Pervasive Computing},
 number = {4}
}

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