The relationship between recall and precision
Journal of the American Society for Information Science
Machine Learning for Sequential Data: A Review
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
An activity recognition system for mobile phones
Mobile Networks and Applications
Evidential fusion of sensor data for activity recognition in smart homes
Pervasive and Mobile Computing
Activity recognition from accelerometer data
IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
Activity recognition using cell phone accelerometers
ACM SIGKDD Explorations Newsletter
Location and activity recognition using ewatch: a wearable sensor platform
Ambient Intelligence in Everyday Life
IEEE Transactions on Information Technology in Biomedicine
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This paper presents a process for extracting knowledge for physical activity recognition, from accelerometer data provided by mobile devices. Starting from a dataset collected by three different users, knowledge discovery is performed through a phase of feature extraction from raw data, minimizing the number of statistical features and optimizing the classification process. The development and comparison of classifying models over this new dataset, using both offline and online algorithms, is also described. Phases of data acquisition, pre-processing and classification are detailed, and experimental results for different machine learning algorithms are provided. For these results, different evaluation criteria are used, and the best algorithm is selected according to these criteria. Final results show success rates around 98%, while other similar works offer rates around 87%.