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 inIEEE journal of biomedical and health informatics Vol. 18; no. 4; pp. 1225 - 1231
Main Authors Perriot, Bruno, Argod, Jerome, Pepin, Jean-Louis, Noury, Norbert
Format Journal Article
LanguageEnglish
Published United States IEEE 01.07.2014
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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ISSN2168-2194
2168-2208
2168-2208
DOI10.1109/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.
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
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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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