# Individualized Stress Mobile Sensing Using Self-Supervised Pre-Training
## 3 Methodology  
We propose a two-step process for training mobile sensing models, which can predict subjective labels such as stress using only a few training examples per user: (1) personalized SSL for representation learning of temporal EDA dynamics per user and (2) fine-tuning the pre-trained model to the stress-prediction task. During SSL, the pretext task helps the model learn interim data representations without ground truth labels. The pretext task is designed to predict the missing segments of the input data given the remaining segments. After this representation is learned, the downstream task involves fine-tuning the weights from the pretext task towards training the neural network model to predict stress. The overall architecture is shown in Figure 1.  
As depicted in Figure 1, the personalized SSL model was designed to accept the raw EDA signal for each subject. The raw EDA signal is divided into chunks of 7000-dimensional vectors, as described in Section 3.3, generating a large dataset of 7910 data points to pre-train the model. The pre-training phase involved the use of a 1D CNN model to predict 40 data points, as detailed in Section 3.3. The weights learned in the pre-training phase are transferred to another 1D CNN model, which is fine-tuned for the stress-prediction task. In addition, a second 1D CNN model with the same architecture as the fine-tuned model was employed for stress prediction, but without any pre-training. This is our control model, which we used as a baseline to quantify the benefits of our SSL over personalization without SSL. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Study** & **Focus** & **Opportunities** \\ \hline [PERSON] et al. [6] & ECG deep learning & Exploration of hybrid models combining ECG with other biosignals for improved stress prediction. \\ [PERSON] et al. [11] & Speech-enabled devices & Integration of voice-based feedback mechanisms to provide real-time stress relief interventions. \\ [PERSON] et al. [12] & EDA wearables & Development of compact and user-friendly wearable devices for mass adoption. \\ [PERSON] et al. [15] & Consumer EDA devices & Design of consumer-centric applications that utilize EDA data for wellness and mental health tracking. \\ [PERSON] et al. [16] & Empatica E4 device & Incorporation of multi-sensor data for a holistic health-monitoring system. \\ [PERSON] et al. [17] & Mental states & Broadening the scope to include the detection of other mental states like anxiety or excitement. \\ [PERSON] et al. [18] & Multimodal framework & Development of adaptive systems for other differently abled individuals beyond the visually impaired. \\ SimCLR [19] and BOYL [20] & Contrastive learning & Application of contrastive learning techniques to other images. \\ [PERSON] et al. [22] & Time series analysis & Implementation in real-time systems for quick anomaly detection in biosignals. \\ [PERSON] et al. [23] & EDA stress prediction & Exploration of non-invasive techniques for real-world applications. \\ CLOCS [24] & ECG learning & Expansion to include patient-specific health recommendations based on ECG patterns. \\ [PERSON] et al. [26] & ECG emotion & Development of emotion-aware devices and applications for personalized user experiences. \\ [PERSON] et al. [26] & Time series learning & Application in industries like finance or meteorology where time series data are abundant. \\ [PERSON] et al. [27] & TimeCLR & Incorporation of multivariate time series data for more-complex predictions. \\ [PERSON] et al. [28] & Time series representation & Optimized personalized multimodal model \\ [PERSON] et al. [29] & Less annotated data & Personalized multimodal machine learning model with least amount of labeled data \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of literature review. We compared these two models using varying numbers of labeled data points, as described in Sections 4.2 and 4.3.