Eliminating Motion Artifacts from Fabric-mounted Wearable Sensors

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

4 Citations (Scopus)


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.
Original languageEnglish
Title of host publication2014 IEEE-RAS International Conference on Humanoid Robots
Number of pages6
Publication statusPublished - Nov 2014


  • Machine learning
  • Wearable sensors
  • Human motion modelling


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