## 2 Materials and Methods
### Minimal Geriatric Assessment Feature Selection  
As shown in the previous work by [PERSON] et al. (2016), the classification model trained using only activity tracking features from low-cost activity trackers was not sufficiently accurate for use in the field. In this study, we designed a novel way to incorporate questionnaire-based assessment scale data as well as activity tracking data in the development of an accurate model. In this study, we suggest a method to select each set of minimal questionnaire items for depression and anxiety as follows for use with the activity tracking data in our multi-label classifier development. In addition to the current 352 data set described in Section 2.1, Ewha Womans University Mokdong Hospital had previous data sets related to geriatric assessment scales previously collected. We additionally used those data sets to increase the accuracy and generality when selecting minimal assessment features, as described below. The total number of sample data by symptoms used to derive minimal assessment features is presented in Table 4. Note that the previous data set was used only for the selection of minimal assessment features, not for the multi-label classification described in the next section. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Label** & **Current Data Set** & **Prev. Data Set** & **Total** \\ \hline None & 241 & 389 & 630 \\ Depression only & 15 & 319 & 334 \\ Anxiety only & 49 & 24 & 73 \\ Both & 47 & 46 & 93 \\ \hline Total & 352 & 778 & 1130 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Number of samples used to derive minimal assessment features. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Total** & **None** & **Depression Only** & **Anxiety Only** & **Both** \\  & \(\left(N=352\right)\) & \(\left(N=241\right)\) & \(\left(N=15\right)\) & \(\left(N=49\right)\) & \(\left(N=47\right)\) \\ \hline
**Demographic** & & & & & \\ Ages (years) & \(72.48\pm 5.9\) & \(72.29\pm 5.9\) & \(74.33\pm 5.73\) & \(72.92\pm 6.04\) & \(72.45\pm 6.2\) \\ Female (\%) & \(73.01\) & \(74.27\) & \(46.67\) & \(77.56\) & \(70.21\) \\
**Activity rhythm** & & & & & \\ IS & \(0.37\pm 0.13\) & \(0.38\pm 0.13\) & \(0.33\pm 0.11\) & \(0.36\pm 0.12\) & \(0.37\pm 0.12\) \\ IV & \(1.19\pm 0.25\) & \(1.19\pm 0.25\) & \(1.16\pm 0.19\) & \(1.20\pm 0.26\) & \(1.20\pm 0.26\) \\ L5 & \(651.42\pm 95.55\) & \(652.63\pm 92.68\) & \(612.82\pm 80.30\) & \(666.64\pm 107.48\) & \(641.51\pm 104.07\) \\
**Sleep** & & & & & \\ TST & \(396.58\pm 92.35\) & \(385.94\pm 86.84\) & \(482.02\pm 100.96\) & \(408.46\pm 84.05\) & \(411.49\pm 96.65\) \\ WASO & \(36.28\pm 15.44\) & \(35.19\pm 15.53\) & \(42.73\pm 15.29\) & \(35.57\pm 12.61\) & \(40.52\pm 14.97\) \\ SOL & \(19.26\pm 15.57\) & \(18.90\pm 13.56\) & \(18.11\pm 10.96\) & \(19.39\pm 18.80\) & \(21.35\pm 20.27\) \\ SQ & \(90.94\pm 3.92\) & \(91.09\pm 3.81\) & \(90.71\pm 4.67\) & \(91.39\pm 3.08\) & \(89.71\pm 4.08\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Characteristic of Input Features. The minimal assessment features for depression and anxiety are derived as follows. First, the importance of the assessment data were calculated using the assessment data in the complete data sets. In this study, the _F-value_, a statistical method that highlights the differences between groups [33; 34], was used to calculate the feature importance, as follows:  
\[F\text{-}value=\frac{variance\,of\,group\,means(MeanSquareBetween)}{mean\,of\, within\,group\, variances(MeanSquareError)}. \tag{3}\]  
The feature importance of K-SGDS and K-GAI in descending order using the _F-values_ are shown in Figure 2a,b, respectively. Second, the candidate sets of the assessment features for each K-SGDS and K-GAI were constructed by accumulating the feature importance in the order of highest ranking. For example, in K-SGDS, Data Set 1 had GDS 8; Set 2 had GDS 8 and 14; Set 3 had GDS 8, 14 and 3; and in K-GAI, Set 1 had GAI 11; Set 2 had GAI 11 and 5; Set 3 had GAI 11, 5 and 20.