Computerized analysis of daily life motor activity for ambulatory monitoring
Technology and Health Care
Classification of Posture and Movement Using a 3-axis Accelerometer
ICCIT '07 Proceedings of the 2007 International Conference on Convergence Information Technology
Automatic feature selection for context recognition in mobile devices
Pervasive and Mobile Computing
Managing catastrophic events by wearable mobile systems
MobileResponse'07 Proceedings of the 1st international conference on Mobile information technology for emergency response
Inexpensive and automatic calibration for acceleration sensors
UCS'04 Proceedings of the Second international conference on Ubiquitous Computing Systems
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Information Technology in Biomedicine
ICEC'07 Proceedings of the 6th international conference on Entertainment Computing
A real-time algorithm based on triaxial accelerometer for the detection of human activity state
Proceedings of the 6th International Conference on Body Area Networks
Technology and Health Care
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Assessment of human activity and posture with triaxial accelerometers provides insightful information about the functional ability: classification of human activities in rehabilitation and elderly surveillance contexts has been already proposed in the literature. In the meanwhile, recent technological advances allow developing miniaturized wearable devices, integrated within garments, which may extend this assessment to novel tasks, such as real-time remote surveillance of workers and emergency operators intervening in harsh environments. We present an algorithm for human posture and activity-level detection, based on the real-time processing of the signals produced by one wearable triaxial accelerometer. The algorithm is independent of the sensor orientation with respect to the body. Furthermore, it associates to its outputs a "reliability" value, representing the classification quality, in order to launch reliable alarms only when effective dangerous conditions are detected. The system was tested on a customized device to estimate the computational resources needed for real-time functioning. Results exhibit an overall 96.2 % accuracy when classifying both static and dynamic activities.