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Missing values in user activity classification applications utilizing wireless sensors
Proceedings of the 6th ACM international symposium on Mobility management and wireless access
Review: Ambient intelligence: Technologies, applications, and opportunities
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ADL Monitoring System Using FSR Arrays and Optional 3-Axis Accelerometer
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Automatic feature selection for context recognition in mobile devices
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Wearable sensor activity analysis using semi-Markov models with a grammar
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IEEE Transactions on Information Technology in Biomedicine - Special section on new and emerging technologies in bioinformatics and bioengineering
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IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Information Technology in Biomedicine
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HBU'10 Proceedings of the First international conference on Human behavior understanding
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UIC'10 Proceedings of the 7th international conference on Ubiquitous intelligence and computing
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EuroSSC'10 Proceedings of the 5th European conference on Smart sensing and context
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IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
Anatomy of automatic mobile carbon footprint calculator
GPC'11 Proceedings of the 6th international conference on Advances in grid and pervasive computing
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
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Pattern Recognition Letters
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Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments
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Physical activity has a positive impact on people's well-being, and it may also decrease the occurrence of chronic diseases. Activity recognition with wearable sensors can provide feedback to the user about his/her lifestyle regarding physical activity and sports, and thus, promote a more active lifestyle. So far, activity recognition has mostly been studied in supervised laboratory settings. The aim of this study was to examine how well the daily activities and sports performed by the subjects in unsupervised settings can be recognized compared to supervised settings. The activities were recognized by using a hybrid classifier combining a tree structure containing a priori knowledge and artificial neural networks, and also by using three reference classifiers. Activity data were collected for 68 h from 12 subjects, out of which the activity was supervised for 21 h and unsupervised for 47 h. Activities were recognized based on signal features from 3-D accelerometers on hip and wrist and GPS information. The activities included lying down, sitting and standing, walking, running, cycling with an exercise bike, rowing with a rowing machine, playing football, Nordic walking, and cycling with a regular bike. The total accuracy of the activity recognition using both supervised and unsupervised data was 89% that was only 1% unit lower than the accuracy of activity recognition using only supervised data. However, the accuracy decreased by 17% unit when only supervised data were used for training and only unsupervised data for validation, which emphasizes the need for out-of-laboratory data in the development of activity-recognition systems. The results support a vision of recognizing a wider spectrum, and more complex activities in real life settings.