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- W2088891137 abstract "Sensors embedded into clothing for measuring human movement are becoming more widespread in research, with applications in clinical diagnostics or rehabilitation studies. A major issue with their use is the undesired effect of fabric motion artifacts corrupting movement signals. This paper presents a method for learning body movements, viewing the undesired motion as stochastic perturbations to the sensed motion, and utilising errors-in-variables models to eliminate these errors in the learning process. Experiments, both in simulation and with a physical fabric-mounted sensor, indicate improved prediction accuracy as compared to standard learning methods." @default.
- W2088891137 created "2016-06-24" @default.
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- W2088891137 date "2014-11-01" @default.
- W2088891137 modified "2023-09-23" @default.
- W2088891137 title "Eliminating motion artifacts from fabric-mounted wearable sensors" @default.
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- W2088891137 doi "https://doi.org/10.1109/humanoids.2014.7041466" @default.
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