Characterization of Physical Activity in COPD Patients: Validation of a Robust Algorithm for Actigraphic Measurements in Living Situations
We have developed robust embedded algorithms for the real-time classification of activity detected by our wearable inertial device. We collected 224 h of accelerometric signals from 28 subjects [22 suffering from chronic obstructive pulmonary disease (COPD)] to develop and then evaluate our algorith...
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Published in | IEEE journal of biomedical and health informatics Vol. 18; no. 4; pp. 1225 - 1231 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
United States
IEEE
01.07.2014
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subjects | |
Online Access | Get full text |
ISSN | 2168-2194 2168-2208 2168-2208 |
DOI | 10.1109/JBHI.2013.2282617 |
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Abstract | We have developed robust embedded algorithms for the real-time classification of activity detected by our wearable inertial device. We collected 224 h of accelerometric signals from 28 subjects [22 suffering from chronic obstructive pulmonary disease (COPD)] to develop and then evaluate our algorithms. We describe the process for determining the most robust parameters of the algorithms. Our results with COPD patients show the feasibility of conducting real-time classification of their activities in everyday situations, with high fidelity. |
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AbstractList | We have developed robust embedded algorithms for the real-time classification of activity detected by our wearable inertial device. We collected 224 h of accelerometric signals from 28 subjects [22 suffering from chronic obstructive pulmonary disease (COPD)] to develop and then evaluate our algorithms. We describe the process for determining the most robust parameters of the algorithms. Our results with COPD patients show the feasibility of conducting real-time classification of their activities in everyday situations, with high fidelity.We have developed robust embedded algorithms for the real-time classification of activity detected by our wearable inertial device. We collected 224 h of accelerometric signals from 28 subjects [22 suffering from chronic obstructive pulmonary disease (COPD)] to develop and then evaluate our algorithms. We describe the process for determining the most robust parameters of the algorithms. Our results with COPD patients show the feasibility of conducting real-time classification of their activities in everyday situations, with high fidelity. We have developed robust embedded algorithms for the real-time classification of activity detected by our wearable inertial device. We collected 224 h of accelerometric signals from 28 subjects [22 suffering from chronic obstructive pulmonary disease (COPD)] to develop and then evaluate our algorithms. We describe the process for determining the most robust parameters of the algorithms. Our results with COPD patients show the feasibility of conducting real-time classification of their activities in everyday situations, with high fidelity. We have developed robust embedded algorithms for the real-time classification of activity detected by our wearable inertial device. We collected 224 h of accelerometric signals from 28 subjects [22 suffering from chronic obstructive pulmonary disease (COPD)] to develop and then evaluate our algorithms. We describe the process for determining the most robust parameters of the algorithms. Our results with COPD patients show the feasibility of conducting real-time classification of their activities in everyday situations, with high fidelity. [PUBLICATION ABSTRACT] |
Author | Noury, Norbert Argod, Jerome Pepin, Jean-Louis Perriot, Bruno |
Author_xml | – sequence: 1 givenname: Bruno surname: Perriot fullname: Perriot, Bruno email: bruno.perriot@sleepinnov.com organization: SleepInnov Technol., Moirans, France – sequence: 2 givenname: Jerome surname: Argod fullname: Argod, Jerome email: jerome.argod@sleepinnov.com organization: SleepInnov Technol., Moirans, France – sequence: 3 givenname: Jean-Louis surname: Pepin fullname: Pepin, Jean-Louis email: jpepin@chu-grenoble.fr organization: Pneumology unit, CHU, Grenoble, France – sequence: 4 givenname: Norbert surname: Noury fullname: Noury, Norbert email: norbert.noury@insa-lyon.fr organization: Lab. INL, Univ. of Lyon, Villeurbanne, France |
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Cites_doi | 10.1164/rccm.200407-855OC 10.1371/journal.pone.0039198 10.1016/S0140-6736(11)60968-9 10.1016/j.rmed.2010.01.012 10.1378/chest.129.3.536 10.1164/rccm.200203-166OC 10.1136/thorax.58.2.100 10.1183/09031936.96.09122584 10.1109/86.547939 10.1183/09031936.06.00064105 10.1109/TBME.2003.812189 |
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SubjectTerms | Acceleration Accelerometry Actigraphy - instrumentation Actigraphy - methods Actimetry Activities of Daily Living - classification Adult Aged Algorithms Biomedical monitoring Bluetooth body postures body sensor network chronic obstructive pulmonary disease (COPD) patients Correlation Feasibility Studies Female Humans Legged locomotion Male Middle Aged Monitoring Monitoring, Ambulatory - instrumentation Monitoring, Ambulatory - methods Posture - physiology Pulmonary Disease, Chronic Obstructive ROC Curve Sensors Signal Processing, Computer-Assisted Young Adult |
Title | Characterization of Physical Activity in COPD Patients: Validation of a Robust Algorithm for Actigraphic Measurements in Living Situations |
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