Accelerometer-based human abnormal movement detection in wireless sensor networks
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Activity Recognition from Accelerometer Data on a Mobile Phone
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Telehealth '07 The Third IASTED International Conference on Telehealth
Developing a wearable system for real-time physical activity monitoring in a home environment
Telehealth '07 The Third IASTED International Conference on Telehealth
A networked embedded computing platform for physical activity assessment
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Recognition of hand movements using wearable accelerometers
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GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
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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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Portable Activity Monitoring System for Temporal Parameters of Gait Cycles
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Mobile phone-based pervasive fall detection
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Preprocessing techniques for context recognition from accelerometer data
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Human activity recognition using inertial/magnetic sensor units
HBU'10 Proceedings of the First international conference on Human behavior understanding
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Accelerometry-based classification of human activities using Markov Modeling
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GPC'10 Proceedings of the 5th international conference on Advances in Grid and Pervasive Computing
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Expert Systems with Applications: An International Journal
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3DPH'09 Proceedings of the 2009 international conference on Modelling the Physiological Human
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Recognition of hand movements using wearable accelerometers
Journal of Ambient Intelligence and Smart Environments
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Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments
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The Journal of Supercomputing
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The real-time monitoring of human movement can provide valuable information regarding an individual's degree of functional ability and general level of activity. This paper presents the implementation of a real-time classification system for the types of human movement associated with the data acquired from a single, waist-mounted triaxial accelerometer unit. The major advance proposed by the system is to perform the vast majority of signal processing onboard the wearable unit using embedded intelligence. In this way, the system distinguishes between periods of activity and rest, recognizes the postural orientation of the wearer, detects events such as walking and falls, and provides an estimation of metabolic energy expenditure. A laboratory-based trial involving six subjects was undertaken, with results indicating an overall accuracy of 90.8% across a series of 12 tasks (283 tests) involving a variety of movements related to normal daily activities. Distinction between activity and rest was performed without error; recognition of postural orientation was carried out with 94.1% accuracy, classification of walking was achieved with less certainty (83.3% accuracy), and detection of possible falls was made with 95.6% accuracy. Results demonstrate the feasibility of implementing an accelerometry-based, real-time movement classifier using embedded intelligence