Table 3.
Factor loadings based on a principal component analysis with orthogonal rotation for 25 feature variables from the baseline sensor signal data.
| Features | Component | ||
|---|---|---|---|
| 1 | 2 | 3 | |
| HR_Mean.1 | 0.964 | −0.060 | −0.020 |
| ACCY_Shape.1 | 0.961 | 0.058 | −0.093 |
| ACCX_Variance.1 | 0.942 | −0.096 | −0.199 |
| ACCX_Shape.1 | 0.942 | −0.247 | −0.257 |
| ACCZ_Shape.1 | 0.912 | 0.031 | −0.171 |
| HR_D.1 | 0.905 | −0.433 | 0.018 |
| ACCZ_D.1 | 0.884 | 0.131 | −0.013 |
| ACCZ_Variance.1 | 0.869 | 0.049 | −0.176 |
| ACCX_D.1 | 0.863 | 0.222 | 0.034 |
| ACCZ_Scale.1 | 0.852 | 0.138 | 0.002 |
| ACCY_Variance.1 | 0.846 | −0.161 | 0.180 |
| HR_Shape.1 | 0.817 | 0.082 | 0.028 |
| HR_Scale.1 | 0.814 | −0.520 | 0.025 |
| ACCX_Scale.1 | 0.780 | 0.300 | 0.083 |
| ACCY_D.1 | 0.693 | 0.002 | 0.276 |
| ACCY_Scale.1 | 0.603 | −0.029 | 0.339 |
| EDA_Shape.1 | 0.434 | 0.747 | −0.166 |
| HR_Variance.1 | 0.596 | −0.737 | 0.183 |
| EDA_D.1 | 0.390 | 0.704 | 0.010 |
| ACCZ_Mean.1 | 0.436 | 0.484 | 0.174 |
| ACCY_Mean.1 | −0.208 | 0.470 | −0.011 |
| ACCX_Mean.1 | −0.039 | 0.278 | −0.032 |
| EDA_Scale.1 | −0.089 | −0.051 | 0.995 |
| EDA_Variance.1 | −0.246 | −0.227 | 0.975 |
| EDA_Mean.1 | 0.333 | 0.335 | 0.572 |
Extraction method: principal component analysis. Rotation method: Promax with Kaiser normalization. Rotation converged in 5 iterations. Numbers in bold indicate high factor loadings onto each component based on the standard threshold in SPSS, unless a higher factor loading was present on a different component.
ACCX, accelerometer x-axis; ACCY, accelerometer y-axis; ACCZ, accelerometer z-axis; EDA, electrodermal activity; HR, heart rate.