Toward Flexibility in Sensor Placement for Motion Capture Systems: A Signal Processing Approach

Abstract

Human body motion can be captured by body area sensor networks. Accurate sensor placement with respect to anatomical landmarks is one of the main factors determining the accuracy of motion-capture systems. Changes in position of the sensors cause increased variability in the motion data, so isolating the characteristic features that represent the most important motion patterns is our concern. As accurate sensor placement is time-consuming and hard to achieve, we propose a signal processing technique that can enable salient data to be isolated. By using functional principal component analysis (f-PCA), we compensate for the variation in data due to changes in the on-body positioning of sensors. More precisely, we investigate the use of f-PCA for filtering and interpreting motion data, whilst accounting for variability in the sensor origin. Data are collected through a marker-based motion capture system from two designed experiments based on human body and robot arm movement. Results show differences between similar actions across different sessions of marker wearing with random changes in position of sensors. After applying the f-PCA filter on the data, we show how uncertainties due to sensor position changes can be compensated for.