###### Abstract
ActiGraph is known to be relatively accurate enough for clinical use but is far more expensive than a low-cost activity tracker such as Fitbit. [PERSON] et al. [13] found a correlation between daytime activity levels and depression using ActiGraph. This study focused on depression only, and the total number of participants was 32, which was relatively low. [PERSON] et al. [14] used Fitbit to statistically evaluate sleep in depressive disorder. [PERSON] et al. [15] verified that anxiety symptoms affect sleep quality and sleep fragmentation from Actigraph data. [PERSON] et al. [10] collected activity tracking data from 1714 middle-aged and elderly participants using ActiGraph and extracted six features for the 24-h activity rhythms and sleep. They found statistically significant results that the 24-h activity rhythm and sleep are related to geriatric depression and anxiety. Their study was a major clinical motivation for our study to develop a classification model for identifying geriatric depression and anxiety using activity rhythms and sleep patterns. However, in our case, we used data from low-cost activity trackers for practicability. Some studies have attempted to apply machine learning techniques for identifying mood disorders using biosignals from various sensors. [PERSON] et al. [16] conducted a study to better recognize depression using electroencephalogram (EEG) features and machine learning methods. They studied only depression in a limited number of 28 subjects and used EEG in a controlled environment. [PERSON] et al. [17] conducted a study to predict depressive symptoms based on the Hamilton Depression Rating Scale (HDRS) by machine learning techniques applied to data acquired from sensors on E4 wearable wristbands and Android phones. They used additional input data, including electrodermal activity (EDA), location changes, phone-based communication and phone usage patterns. [PERSON] et al. [18] investigated the possibility of detecting depression using digital biomarkers, such as steps, heart rate, energy expenditure and sleep data, from consumer-grade wearable sensors. They discovered their classification model's potential to assist in depression screening with good accuracy. Since patients can have both depression and anxiety symptoms at the same time, multi-label classification arises when applying machine learning. In many fields, including biology, music and linguistics, interest in multi-label classification is increasing [19]. Multi-label classification problems allow one data sample to belong to multiple classes simultaneously [19; 20; 21]. In single-label classification problems, a data sample can only belong to one class. The binary relevance (BR) is one simple solution to the multi-label classification problem. BR solves multi-label classification problems by creating an independent binary classifier for each label [20; 22]. Several studies have used the BR to solve multi-label classification problems. [PERSON] et al. applied BR to specific features to perform classification for different labels [23]. They achieved an enhanced performance using label-specific features. [PERSON] et al. suggested sharing label-specific features between correlated labels for the classification for each label [24]. This method outperformed other multi-label classification methods. To overcome the limitation of the BR method in not considering correlations between labels, [PERSON] et al. proposed an approach that allows binary classifiers to discover label dependencies [25]. Comparing the performances of various classification algorithms is an important process for machine learning; however, previous studies have not applied multiple classification algorithms to the binary classification of each label in the BR method. This study aims to develop a clinically significant classification model to identify comorbid geriatric depression and anxiety using both activity tracking data from low-cost activity trackers and minimal questionnaire-based geriatric assessment scales.