title: Identifying types of physical activity with a single accelerometer: evaluating laboratory-trained algorithms in daily life.
abstract: 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.
authors.affiliation: Department of Care and Health Applications, Philips Research Laboratories, Eindhoven 5656AE, The Netherlands. illapha@ kth.se
authors.forename: Illapha Cuba
authors.initials: IC
authors.lastname: Gyllensten
authors.forename: Alberto G
authors.initials: AG
authors.lastname: Bonomi
mesh_terms: D000054:Acceleration; D000203:Activities of Daily Living / Q000145:classification; D000328:Adult; D003663:Decision Trees; D005260:Female; D006801:Humans; D008124:Locomotion / Q000502:physiology; D008297:Male; D015233:Models, Statistical; D018670:Monitoring, Ambulatory / Q000295:instrumentation / Q000379:methods*; D009043:Motor Activity / Q000502:physiology*; D016571:Neural Networks, Computer; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D060388:Support Vector Machine