Stress detection using time-frequency analysis and machine learning framework

Stress is a prevalent and inherent phenomenon in people. It triggers the production of hormones that assist in managing the scenarios; nevertheless, chronic stress adversely impacts physical and mental health, which may result in detrimental effects such as depression, anxiety, digestive and heart diseases. Thus, early stress detection is essential to avoiding such negative effects. Addressing this challenge, this research attempted to create a Machine Learning (ML) based stress identification model utilizing two available datasets, namely K-EmoCon and WESAD, which acquired most discriminative signals for stress identification - Inter Beat Interval (IBI), Electro Dermal Activity (EDA) using the Empatica E4 wrist band. Time-Frequency features are extracted from these signals using Ensemble Empirical Mode Decomposition (EEMD) based on Hilbert Transform (HT). Instantaneous Frequency (IF) from IBI and EDA were fed as input to traditional ML models, showing a reduction of the computational power needed, which is especially relevant for setups with limited resources. Among those models, k-NN provides the highest accuracy of about 99.85% and an F1-score of 99.87%. Furthermore, real-time data acquired using a Fitbit smartwatch is also validated using the proposed approach, thereby improving the model's efficiency.