Identifying Types of Physical Activity With a Single Accelerometer : Evaluating Laboratory-trained Algorithms in Daily Life

• 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.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Medicinsk bioteknologi -- Biomedicinsk laboratorievetenskap/teknologi (hsv//swe)

MEDICAL AND HEALTH SCIENCES  -- Medical Biotechnology -- Biomedical Laboratory Science/Technology (hsv//eng)

Nyckelord

Assessment of daily physical activity

classification algorithms

intelligent device for energy expenditure and physical activity (IDEEA)

physical activity

triaxial accelerometer

Publikations- och innehållstyp

ref (ämneskategori)

art (ämneskategori)