# Wearable-Based Intelligent Emotion Monitoring in Older Adults during Daily Life Activities
## 3 Emotional State Monitoring Methodology
### Dataset Construction  
Data were collected from participants during their daily life activities. The E4 sensor was provided to participants with the instruction to wear it as much as possible during the day. During the night, the device was charged, and data were transferred. Participants had the option to disconnect the wearable device at any time, such as when engaging in highly private activities, when the E4 sensor was inconvenient for the activity being performed, or when there was a risk of damaging the sensor (e.g., during showering). We collected data from four older adults, focusing on personal models, which prioritized collection time over the quantity of participants. We believe that the number of days designated to collect data is sufficient to capture changes in the participants' affective states during their daily activities. Table 1 shows details for each participant and the corresponding collected data. The age of participants ranged between 55 and 67 years old. Gender was balanced in the dataset. Initially, we asked the participants to wear the sensor for two weeks; however, different participants wore it for a different number of days due to personal reasons. We can also see in Table 1 that participants answered an average per day of 3.54 to 5.33 times to the EMA questions. They were free to answer more times, and this had the possibility of resulting in an average bigger than 5, as occurred for participant P3. For each participant, we collected the physiological data and the corresponding EMA inputs, which were aligned using timestamps. Ground truth is composed of the input levels of valence and arousal (levels 0 to 4). From these levels, we extrapolated the emotional state using [PERSON]'s circumplex model [PERSON] (1988). We projected all possible combinations of valence-arousal pairs into the circle (see Figure 3B). As it can be observed in the figure, some valence-arousal projections are significantly close. We grouped them into the following emotional states: (1) pleasure, (2) excitement, (3) arousal, (4) distress, (5) misery, (6) depression, (7) sleepiness, and (8) contentment. These affective states were used in [PERSON] (1988) to validate \begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **P1** & **P2** & **P3** & **P4** \\ \hline Age & 67 & 55 & 60 & 63 \\ Gender & Male & Female & Male & Female \\ Days & 9 & 15 & 12 & 13 \\ Total EMAs & 42 & 57 & 64 & 46 \\ Avg. EMAs & 4.67 & 3.80 & 5.33 & 3.54 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of participants’ collected data. Figure 3: (**A**) Smartphone app with EMA questionnaire. (**B**) Projection of valence-arousal pair values in the plane. Between brackets we represent the value taken from our app. As an example, (0, 0) values from the app would fall around (\(-2\), \(-2\)) in the plane coordinates. Due to data scarcity, we group labels following [PERSON] (1988). [PERSON]'s affective theory. For this reason, as well as data scarcity, we decided to used them in our experiments. Different participants reported different numbers of affective or emotional states, and this resulted in an imbalanced distribution of labels per participant among the possible classes. This imbalance is justified due to the nature of the collected data: different people feel different emotional states during their daily life activities. Moreover, we carried out experiments on healthy participants. Therefore, we expected the labels to be imbalanced between positive and negative states, with positive states being more dominant, as our participants demonstrated a healthy condition in the questionnaires administered prior to data collection (see Figure 4). This imbalance affects the generalization capabilities discussed in the following section. The approach to collect data followed in this work differs from data collected in the lab, in that we did not have full control of the induced emotions or affective states. This dataset aims to collect emotional states _in the wild_.