The main distinction lies in the fact that these studies primarily concentrated on specific, more easily identifiable activities, such as walking, running, driving, and sleeping. In contrast, our work, focuses on non-specific, broadly defined time allocation activities, like farming, taking care of children, working for wage, and collecting foodstuff.

# **3.1 Dataset merging**

Building a supervised machine learning model requires data on observations and relevant predictor variables. Data containing known outcomes, serving as dependent variables, are essential for model building and evaluation. We utilize the reported time use activities collected through the time use survey module as our dependent variable.

The first step in preparing the data for machine learning modeling involved merging the time use survey data with the physical activity sensor data. The sensor-based physical activity tracking study spanned over two consecutive weeks. During this period, participants were surveyed twice with 24hour recall-based surveys to gain insight into their time use activities. The amalgamation of the datasets took place on the participant level for these two days. The aggregated dataset was further augmented with demographic information of the participants as well as meta information derived from the timestamp.

Harmonizing the data registration time intervals was crucial. The physical activity sensor data were recorded at one-minute intervals, whereas the time-use data were recorded at 15-minute increments. To synchronize the datasets at a consistent granularity, the time-use data were restructured to align with the one-minute interval of the sensor data.

# **3.2 Dataset cleaning**

The combined dataset underwent cleaning, which involved discarding data points with missing or unclear information on time use activities. Subsequently, data points indicating the accelerometer was not being worn were also removed. The wearable device registers inactivity when detached from the body, such as during bathing, and the corresponding time stamps registered by the device as non-wear were marked and discarded accordingly. One additional consideration was that approximately 10 percent of the entries had reported secondary activities simultaneous with the primary activities. From a modeling standpoint, these dual activities introduce more uncertainty instead of clarity. Therefore, such entries were removed from the analysis.

Lastly, we scrutinized the minute level data to identify extreme outliers within 15-minute level epochs. The data-points deviating more than two-sigma from the sample distribution were discarded. This means that all data points diverging from the general behavior pattern of a specific activity within the 15-minute epoch were excluded from the dataset. The discarded outliers accounted for 2.6 percent of the data. The remaining data were considered suitable for the scope of the current study. The cleaned dataset comprised 388 unique participants, indicating, that 27 participants were removed during the cleaning process from the original sample size of 415 participants.

# **3.3 Feature engineering**

The raw data comprises four base variables: accelerations along three axes and the step count. Additional features were engineered from the extracted base variables of the triaxial accelerometer. In the realm of data science, a feature is a characteristic of data. These are used to enhance data analysis. In addition to the base and generated features, we incorporated extra features based on background information and on the timestamp of the time-use activities, representing the day of the week (weekday or weekend day) and segment of the day (am or pm).

We conducted a correlation analysis on the feature engineered data to avoid unnecessary growth of the data space. Correlated variables can add more weight to a variable without adding more value to a model. All highly correlated variables were identified and excluded from the dataset. A Pearson’s correlation (or anticorrelation) with a threshold _r_ -value of 0.95 was adopted.

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