In this study, a single tri-axial accelerometer placed on the waist was used to record the acceleration data for human physical activity classification. The data collection involved 24 subjects performing daily real-life activities in a naturalistic environment without researchers' intervention. For the purpose of assessing customers' daily energy expenditure, walking, running, cycling, driving, and sports were chosen as target activities for classification. This study compared a Bayesian classification with that of a Decision Tree based approach. A Bayes classifier has the advantage to be more extensible, requiring little effort in classifier retraining and software update upon further expansion or modification of the target activities. Principal components analysis was applied to remove the correlation among features and to reduce the feature vector dimension. Experiments using leave-one-subject-out and 10-fold cross validation protocols revealed a classification accuracy of ~80%, which was comparable with that obtained by a Decision Tree classifier.
|Title of host publication||Proceedings of the EMBC' 09; 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2-6 September 2009, Minneapolis, Minnesota|
|Place of Publication||Piscataway|
|Publisher||Institute of Electrical and Electronics Engineers|
|Publication status||Published - 2009|
Long, X., Yin, B., & Aarts, R. M. (2009). Single-accelerometer-based daily physical activity classification. In Proceedings of the EMBC' 09; 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2-6 September 2009, Minneapolis, Minnesota (pp. 6107-6110). Piscataway: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IEMBS.2009.5334925