Fig 1.
Representation of the Pitch signal for detecting standing-up and sitting down phases.
Pitch signal or rotation around the mediolateral axis (dotted line) and reconstructed signal (solid line) using level 4 approximation of db5 wavelet. When the signal becomes negative (1a) the trunk moves forward until minimal angular velocity (1b). Subsequently when the participants stands-up the angular velocity also changes in direction. For sitting down the same pattern is visible (3a-3c).
Fig 2.
Representation of a yaw signal used for identifying the turn phases and of an AP acceleration signal for detecting steps during turns.
The upper trace represents the yaw signal or rotation around the vertical axis (dotted line) and reconstructed signal (solid line) using a level 6 approximation of db5 wavelet. The turn is indicated by an increase/decrease in the yaw amplitude depending on the direction of the turn. Start of turning is when the zero line is crossed (2a, 2d) and end of turn when the zero line is again crossed (2c; 3f). The lower trace represents the AP acceleration signal (dotted line), reconstructed at level 3 with a db5 wavelet (solid line). Peaks indicate foot contact instances.
Fig 3.
Representation of an AP acceleration signal for detecting steps during walking.
The signal represents the raw (dotted line) and reconstructed (solid line) anterior-posterior acceleration signal (Level 3 db5), used for defining step parameters. Arrows indicate heel strike.
Table 1.
Variables calculated for different phases of the iTUG.
Fig 4.
Variable Projection of Importance (VIP) scores and regression coefficient plot.
The regression coefficients are giving as bars in absolute values. To the left and right of the vertical dotted line, respectively, the negative and positive regression coefficients are shown. The dotted black line represents the VIP-scores (right y-axis). In order to be important to the model, the dots in the dotted line should be above the dashed line (VIP > 0.8, right Y-axis). The dark bars are the variables that entered the PLS-DA model. Note that due to the large number of variable included in the model, regression coefficients are relatively low.
Table 2.
VIP (Variable Importance for Projection) and Variance captured by the 3 LV in the PLS model.
Only variables with a VIP score higher than 0.8 are included. The means of the variables in the first dataset are also shown. Note that due to the large number of variables included in the model, regression coefficients (RC) are relatively low in this type of PLS models.
Fig 5.
Sensitivity and specificity plots.
To determine the optimal cut-off point, sensitivity and specificity are plotted against the threshold (A), the optimal cut-off point is present at 0.52. The sensitivity is plotted against 1—specificity for all cutoff values of the PLS-DA model in the ROC curve (B).