Information Retrieval
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
SATIRE: a software architecture for smart AtTIRE
Proceedings of the 4th international conference on Mobile systems, applications and services
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Activity recognition from accelerometer data
IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
Grammar-based, posture- and context-cognitive detection for falls with different activity levels
Proceedings of the 2nd Conference on Wireless Health
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This paper develops an algorithm for robust human activity recognition in the face of imprecise sensor placement. It is motivated by the emerging body sensor networksthat monitor human activities (as opposed to environmental phenomena) for medical, entertainment, health-and-wellness, training, assisted-living, or entertainment reasons. Activities such as sitting, writing, and walking have been successfully inferred from data provided by body-worn accelerometers. A common concern with previous approaches is their sensitivity with respect to sensor placement. This paper makes two contributions. First, we explicitly address robustness of human activity recognition with respect to changes in accelerometer orientation. We develop a novel set of features based on relative activity-specific body-energy allocation and successfully apply them to recognize human activities in the presence of imprecise sensor placement. Second, we evaluate the accuracy of the approach using empirical data from body-worn sensors.