features to train their machine learning models. High frequency data may aid in predicting some activities more reliably. The study by Staudenmayer et al. (2009) showed that high frequency data can help in better predicting repetitive activities like cycling in free living conditions.

Regarding the time use data, the interviewers were trained to elicit a narrative of each participant’s reference day, establishing first when the participant woke up and went to bed, and then recording the type and timing of the activities in between, in accordance with the respondent’s recollection. The time use module allowed for the specification of both main and secondary activity for each 15-minute interval. This feature of the module aimed at capturing simultaneous activities.

Having said that, recall-based time use reports are prone to potential errors, such as bad recollection of the performed activity or attributing the activity to the wrong time interval (e.g., Bell et al., 2019). This way of labeling the data also has the caveat that simultaneous activities can get mixed (Seymour et al., 2020). Especially, for short duration activities there is a possibility that more than one activity can be performed within the 15-minute time window. Hence, overlap in the activities can lead to mislabeling the physical activity sensor signal data but also to rounding errors of the time frames in which different activities are performed.

Furthermore, the activity list for the time use module does not precisely identify tasks or activity levels but are meant to provide a broader classification of how people spend their time. For example, “Work for wage” could mean any number of different activities like working in the field (active work), in a school (less active), or in an office (near sedentary). The same applies for ‘Traveling and commuting’. Therefore, predicting some time use activities using physical activity sensor signal data can be more difficult than other, more exclusive activities like ‘Fetching water’, ‘Watching TV’, or ‘Resting’.

All these limitations can affect the performance of machine learning models. This study therefore also aims to assess the extent to which broadly grouped activities can be predicted using sensor signal data from wearable devices in combination with background information like age, gender, and the time of day.

# **3 Methods**

To achieve the goal of predicting time use activities using real-time physical activity sensor data, we leverage the power of machine learning techniques. In the past ‘vector magnitude’ methods have been employed to objectively gauge the intensity of physical activity from accelerometer data in large datasets (e.g., Mannini et al., 2013; Doherty et al., 2017; Menai et al., 2018; Kapteyn et al., 2018). More recent studies also employ machine learning methods in large scale accelerometer datasets to objectively measure specific activities (Willetts et al., 2018; Sundararajan et al., 2021; Nunavath et al, 2021; Pfeiffer et al., 2022), demonstrating the broad applicability of such methods.

Some of these studies effectively used machine learning techniques in similar contexts. For example, Willets et al. (2018) conducted an extensive study on 24-hour level physical activity on 96,220 UK Biobank participants. Utilizing a Random Forest machine learning algorithm trained on data from 132 adults, they were able to classify six distinct activities based on accelerometer data, which are bicycling, sitting/standing, walking, driving, sleeping, and mixed activities. Impressively, their model achieved an average prediction accuracy of 87 percent. Furthermore, their research highlighted variations in activity patterns on an hourly, daily, and seasonal basis among adults aged 45 to 79. In a more recent study, Sundararajan et al. (2021) employed a Random Forest model to identify sleep patterns using accelerometer data. Their model differentiated between sleep and wake states with a 74 percent score on unseen data. Moreover, by integrating their sleep detection algorithms with daytime data, they adeptly utilized machine learning to ascertain whether the accelerometer was being worn, achieving a score exceeding 93 percent. Both studies underscore the precision with which machine learning models can predict physical activities.

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