Unsupervised, Dynamic Identification of Physiological and Activity Context in Wearable Computing
ISWC '03 Proceedings of the 7th IEEE International Symposium on Wearable Computers
Learning recurrent behaviors from heterogeneous multivariate time-series
Artificial Intelligence in Medicine
ISWC '07 Proceedings of the 2007 11th IEEE International Symposium on Wearable Computers
A hybrid discriminative/generative approach for modeling human activities
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Using wearable activity type detection to improve physical activity energy expenditure estimation
Proceedings of the 12th ACM international conference on Ubiquitous computing
A practical approach to recognizing physical activities
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
IEEE Transactions on Information Technology in Biomedicine
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This work explores the automatic recognition of physical activity intensity patterns from multi-axial accelerometry and heart rate signals. Data collection was carried out in free-living conditions and in three controlled gymnasium circuits, for a total amount of 179.80 h of data divided into: sedentary situations (65.5%), light-to-moderate activity (17.6%) and vigorous exercise (16.9%). The proposed machine learning algorithms comprise the following steps: time-domain feature definition, standardization and PCA projection, unsupervised clustering (by k-means and GMM) and a HMM to account for long-term temporal trends. Performance was evaluated by 30 runs of a 10-fold cross-validation. Both k-means and GMM-based approaches yielded high overall accuracy (86.97% and 85.03%, respectively) and, given the imbalance of the dataset, meritorious F-measures (up to 77.88%) for non-sedentary cases. Classification errors tended to be concentrated around transients, what constrains their practical impact. Hence, we consider our proposal to be suitable for 24 h-based monitoring of physical activity in ambulatory scenarios and a first step towards intensity-specific energy expenditure estimators.