Analyzing features for activity recognition
Proceedings of the 2005 joint conference on Smart objects and ambient intelligence: innovative context-aware services: usages and technologies
Using physical activity for user behavior analysis
Proceedings of the 1st international conference on PErvasive Technologies Related to Assistive Environments
ISWC '07 Proceedings of the 2007 11th IEEE International Symposium on Wearable Computers
Activity Recognition for Everyday Life on Mobile Phones
UAHCI '09 Proceedings of the 5th International on ConferenceUniversal Access in Human-Computer Interaction. Part II: Intelligent and Ubiquitous Interaction Environments
IEEE Transactions on Information Technology in Biomedicine
Activity classification using realistic data from wearable sensors
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Information Technology in Biomedicine
Creating and benchmarking a new dataset for physical activity monitoring
Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments
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With recent progress in wearable sensing it becomes reasonable for individuals to wear different sensors all day, thus global activity monitoring is establishing. The goals in global activity monitoring systems are amongst others to tell the type of activity that was performed, the duration and the intensity. With the information obtained this way, the individual's daily routine can be described in detail. One of the strong motivations to achieve these goals comes from healthcare: to be able to tell if individuals were performing enough physical activity to maintain or even promote their health. This paper focuses on the monitoring of aerobic activities, and targets two main goals: to estimate the intensity of activities, and to identify basic/recommended physical activities and postures. For these purposes, a dataset with 8 subjects and 14 different activities was recorded, including the basic activities and postures, but also examples of household (ironing, vacuum cleaning), sports (playing soccer, rope jumping) and everyday activities (ascending and descending stairs). Data from 3 accelerometers --- placed on lower arm, chest and foot --- and a heart rate monitor were analyzed. In this paper, first results are shown on both the intensity estimation and activity recognition tasks, with a performance of 87, 54% and 86, 80%, respectively.