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A novel bio-kinematic encoder for human exercise representation and decomposition - Part 2 : Robustness and Optimisation
conference contribution
posted on 2013-01-01, 00:00 authored by S Li, Terry CaelliTerry Caelli, M Ferraro, Pubudu PathiranaPubudu PathiranaBio-kinematic characterisations of human exercises constitute dealing with parameters such as velocity, acceleration, joint angles, etc. A majority of these are measured directly from various sensors ranging from RGB cameras to inertial sensors. However, due to certain limitations associated with these sensors, such as inherent noise, filters are required to be implemented to subjugate the effect from the noise. When the two-component (trajectory shape and dynamics) bio-kinematic encoding model is being established to represent an exercise, reducing the effect from noise embedded in raw data will be important since the underlying model can be quite sensitive to noise. In this paper, we examine and compare some commonly used filters, namely least-square Gaussian filter, Savitzky-Golay filter and optimal Kalman filter, with four groups of real data collected from Microsoft Kinectc , and assert that Savitzky- Golay filter is the best one when establishing an underlying model for human exercise representation.
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Event
Control Automation and Information Sciences Conference (2nd : 2013 : Nha Trang, Vietnam)Pagination
30 - 35Publisher
IAMILocation
Nha Trang, VietnamPlace of publication
Nha Trang, VietnamStart date
2013-11-25End date
2013-11-28ISBN-13
9781467308137ISBN-10
1467308137Language
engPublication classification
E1 Full written paper - refereedTitle of proceedings
Proceedings of the 2nd International Conference on Control Automation and Information Sciences; ICCAIS 2013Usage metrics
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