Accelerometry-based recognition of the placement sites of a wearable sensor release_rel22ygyfndglom5bkwscwq5qq

by Andrea Mannini, Angelo M. Sabatini, Stephen S. Intille

Published in Pervasive and Mobile Computing by Elsevier BV.

2015   Volume 21, p62-74

Abstract

This work describes an automatic method to recognize the position of an accelerometer worn on five different parts of the body: ankle, thigh, hip, arm and wrist from raw accelerometer data. Automatic detection of body position of a wearable sensor would enable systems that allow users to wear sensors flexibly on different body parts or permit systems that need to automatically verify sensor placement. The two-stage location detection algorithm works by first detecting time periods during which candidates are walking (regardless of where the sensor is positioned). Then, assuming that the data refer to walking, the algorithm detects the position of the sensor. Algorithms were validated on a dataset that is substantially larger than in prior work, using a leave-one-subject-out cross-validation approach. Correct walking and placement recognition were obtained for 97.4% and 91.2% of classified data windows, respectively.
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Type  article-journal
Stage   published
Date   2015-08-01
Language   en ?
DOI  10.1016/j.pmcj.2015.06.003
PubMed  26213528
PMC  PMC4510470
Wikidata  Q35877212
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ISSN-L:  1574-1192
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