| Popis: |
The human hand needs a large number of sensors to measure kinematics owing to its large number of degrees of freedom. Existing devices like data gloves and optical trackers are associated with calibration, line of sight, and accuracy problems. In this paper, we attempt to measure the full hand kinematics using Electromagnetic Tracking Sensors (EMTS) which are accurate, free of line-of-sight problems, and require no calibration. However, EMTS provides output in the form of rotation groups which are defined on a nonlinear manifold. Hence, linear operations required for experimental analysis such as linear dimensionality reduction are not valid. Also, these sensors are expensive, utilize space in terms of cabling, and require a reduced sensor layout. In this paper, we present measurement methods, test the utility of linear and non-linear dimensionality reduction techniques on quaternions and exponential maps. We also performed sensor reduction using a Gini feature selection based on random forest algorithm. The kinematic measurement results show that EMTS yield superior posture reproduction with an error of less than 1 degree. Autoencoder, a nonlinear dimensionality reduction technique, was successfully applied on quaternions which was tuned to perform better than Principal Component Analysis (PCA) in reducing dimensions. The reduced sensor layout with 8 sensors was able to predict full hand kinematics with a Root Mean Square Error (RMSE) of 5.1 degrees. |
