@article{
 title = {Gesture spotting with body-worn inertial sensors to detect user activities},
 type = {article},
 year = {2008},
 identifiers = {[object Object]},
 keywords = {eating,gesture,inertial,mhealth,sensors},
 pages = {2010-2024},
 volume = {41},
 websites = {http://www.sciencedirect.com/science/article/pii/S0031320307005110},
 month = {6},
 id = {ab86951e-b42a-32db-84f7-150e48a6c872},
 created = {2018-07-12T21:32:35.074Z},
 file_attached = {false},
 profile_id = {f954d000-ce94-3da6-bd26-b983145a920f},
 group_id = {b0b145a3-980e-3ad7-a16f-c93918c606ed},
 last_modified = {2018-07-12T21:32:35.074Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {amft:gesture},
 source_type = {article},
 private_publication = {false},
 abstract = {We present a method for spotting sporadically occurring gestures in a continuous data stream from body-worn inertial sensors. Our method is based on a natural partitioning of continuous sensor signals and uses a two-stage approach for the spotting task. In a first stage, signal sections likely to contain specific motion events are preselected using a simple similarity search. Those preselected sections are then further classified in a second stage, exploiting the recognition capabilities of hidden Markov models. Based on two case studies, we discuss implementation details of our approach and show that it is a feasible strategy for the spotting of various types of motion events.},
 bibtype = {article},
 author = {Junker, Holger and Amft, Oliver and Lukowicz, Paul and Tröster, Gerhard},
 journal = {Pattern Recognition},
 number = {6}
}

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