# Wearable-Based Intelligent Emotion Monitoring in Older Adults during Daily Life Activities
## 2 Related Work  
There are several modalities to predict or monitor mental well-being and the affective state that include visual [15], textual [16] or speech [17]. Nevertheless, these modalities' trustworthiness can be compromised by a phenomenon known as _social masking_[18], which refers to the fact that participants can unconsciously or deliberately hide real emotions, i.e., faking a face gesture, or modulating voice's tonality. For this reason, physiological data are used in a wide range of studies to assess not only emotions but also stress or anxiety states [18]. [PERSON] et al. introduced in [10] the importance of endowing computers with the ability to understand and recognize human affective states by analyzing four different physiological signals. Since then, many efforts have been made to improve human-computer interactions [10]. Moreover, the authors in [19] monitored depressive moods by using EMA questionnaires to train different machine learning models from wearable lifestyle data, such as sleep, physical activity and physiological signals. In [20], the authors used the circumplex model of affect, presented by [PERSON] et al. [13], to label emotions. However, the proposed model is trained to predict only three different emotions: sadness, happiness, and neutrality. In this work, we discretize the circumplex model of affect [13] in eight different emotions: pleasure, excitement, arousal, distress, misery, depression, sleepiness, and contentmen. Figure 1: Graphical abstract of the proposed system to monitor affective states during daily life activities. There is an increasing interest in the use of wearable devices to collect physiological data. Many commercial wearable sensors have been used to predict and monitor emotions [21; 22]. However, for our work, we used the Empatica E4 [12] medical-certified wearable device. In [23], the authors proposed laboratory-based experiments using the Empatica E4 sensor to collect data and extract meaningful emotion-based features to feed a neural network. In addition, the work in [24] proposed two different architectures to process four physiological signals gathered from the Empatica E4 device using a public data benchmark [25]. The authors in [26] used Empatica E4 to collect physiological signals in an experiment to study blue light as a factor to reduce stress. In [27], the authors collected heart rate variability signals through the E4 to train different machine learning classifiers, with the goal of predicting emotions elicited in a virtual reality environment. The authors in [28] conducted a similar experiment to the one presented in this work by using data collected from the Empatica E4 sensor to detect affective changes in participants. They used controlled scenarios to induce emotional states during the experiment, having full control of the environment. In contrast, we did not control the participants' activities during data collection. All works mentioned previously followed a laboratory-based approach, that is, a controlled environment to collect data. Recent research [29] encouraged the importance of collecting data in non-controlled scenarios, also called in-the-wild environments. Few works have followed this approach [29]. For example, stress was addressed in [30], where the authors only analyzed a set of fixed activities of the daily life to be studied. On the contrary, in our work, we had no control over participants' activities during the collection of data, being that the data analysis task was more challenging and representative of real life. Additionally, in [31], in-the-wild physiological data were collected, which were combined with context information to define the perceived stress levels of individuals. In [32], the authors monitored changes in depression severity by combining physiological features extracted from the Empatica E4 device and behavioral features obtained from smartphones. In our approach, the feature vectors are selected from a pool of standard time-based features used in the time analysis using the _tsfresh_ library [33].