Human-Computer Interaction (HCI) research increasingly focuses on developing systems that can recognize and respond to human stress, a key factor in preventing the negative health effects of prolonged stress exposure. Currently, progress in the domain of automated stress recognition based on multi-modal data shows clear potential but is especially hindered by the lack of available datasets and standardized protocols for data collection. Our research aims to contribute towards filling this gap by employing a framework for conducting experiments and data collection in the affective computing domain, supporting improved reuse and reproducibility of results. Specifically in our analysis, we apply a multi-modal approach integrating physiological signals to conduct and evaluate automated stress recognition. By employing standard classifiers, our study achieved notable results: in a ternary classification setting (distinguishing baseline, physical, and overall stress), we attained an accuracy of 79%, while a binary classification (baseline vs. stress) reached up to 89% accuracy. These findings not only replicate existing research in the stress detection domain but clearly show the advantage of using multi-modal data and also establish a benchmark for future analysis studies.