Ensemble Methods for Classification of Physical Activities from Wrist Accelerometry

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Chowdhury, AK
Tjondronegoro, D
Chandran, V
Trost, SG
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Purpose To investigate whether the use of ensemble learning algorithms improve physical activity recognition accuracy compared to the single classifier algorithms, and to compare the classification accuracy achieved by three conventional ensemble machine learning methods (bagging, boosting, random forest) and a custom ensemble model comprising four algorithms commonly used for activity recognition (binary decision tree, k nearest neighbor, support vector machine, and neural network). Methods The study used three independent data sets that included wrist-worn accelerometer data. For each data set, a four-step classification framework consisting of data preprocessing, feature extraction, normalization and feature selection, and classifier training and testing was implemented. For the custom ensemble, decisions from the single classifiers were aggregated using three decision fusion methods: weighted majority vote, naïve Bayes combination, and behavior knowledge space combination. Classifiers were cross-validated using leave-one subject out cross-validation and compared on the basis of average F1 scores. Results In all three data sets, ensemble learning methods consistently outperformed the individual classifiers. Among the conventional ensemble methods, random forest models provided consistently high activity recognition; however, the custom ensemble model using weighted majority voting demonstrated the highest classification accuracy in two of the three data sets. Conclusions Combining multiple individual classifiers using conventional or custom ensemble learning methods can improve activity recognition accuracy from wrist-worn accelerometer data.

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Medicine and Science in Sports and Exercise

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© 2017 LWW. This is a non-final version of an article published in final form in Medicine and Science in Sports and Exercise, 2017, Volume 49, Issue 9, p 1965–1973. Reproduced in accordance with the copyright policy of the publisher. Please refer to the journal link for access to the definitive, published version.

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Sports science and exercise

Medical physiology

Health services and systems

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Chowdhury, AK; Tjondronegoro, D; Chandran, V; Trost, SG, Ensemble Methods for Classification of Physical Activities from Wrist Accelerometry, Medicine and Science in Sports and Exercise, 2017, 49 (9), pp. 1965-1973