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Identifying Types of Physical Activity With a Single Accelerometer: Evaluating Laboratory-trained Algorithms in Daily Life
KTH, School of Computer Science and Communication (CSC).
2011 (English)In: IEEE Transactions on Biomedical Engineering, ISSN 0018-9294, E-ISSN 1558-2531, Vol. 58, no 9, 2656-2663 p.Article in journal (Refereed) Published
Abstract [en]

Accurate identification of physical activity types has been achieved in laboratory conditions using single-site accelerometers and classification algorithms. This methodology is then applied to free-living subjects to determine activity behavior. This study is aimed at analyzing the reproducibility of the accuracy of laboratory-trained classification algorithms in free-living subjects during daily life. A support vector machine (SVM), a feed-forward neural network (NN), and a decision tree (DT) were trained with data collected by a waist-mounted accelerometer during a laboratory trial. The reproducibility of the classification performance was tested on data collected in daily life using a multiple-site accelerometer augmented with an activity diary for 20 healthy subjects (age: 30 +/- 9; BMI: 23.0 +/- 2.6 kg/m(2)). Leave-one-subject-out cross validation of the training data showed accuracies of 95.1 +/- 4.3%, 91.4 +/- 6.7%, and 92.2 +/- 6.6% for the SVM, NN, and DT, respectively. All algorithms showed a significantly decreased accuracy in daily life as compared to the reference truth represented by the IDEEA and diary classifications (75.6 +/- 10.4%, 74.8 +/- 9.7%, and 72.2 +/- 10.3%; p<0.05). In conclusion, cross validation of training data overestimates the accuracy of the classification algorithms in daily life.

Place, publisher, year, edition, pages
2011. Vol. 58, no 9, 2656-2663 p.
Keyword [en]
Assessment of daily physical activity, classification algorithms, intelligent device for energy expenditure and physical activity (IDEEA), physical activity, triaxial accelerometer
National Category
Biomedical Laboratory Science/Technology
Identifiers
URN: urn:nbn:se:kth:diva-39511DOI: 10.1109/TBME.2011.2160723ISI: 000294127700027Scopus ID: 2-s2.0-80052076109OAI: oai:DiVA.org:kth-39511DiVA: diva2:442886
Available from: 2011-09-22 Created: 2011-09-12 Last updated: 2017-12-08Bibliographically approved

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