@inproceedings{, author = {Klingner, S{\"{o}}ren and Han, Zhiwei and Liu, Yuanting and Fang, Fang and Altakrouri, Bashar and Michel, Bruno and Weiss, Jonas R. M. and Sridhar, Arvind and Chau, Sophie Mai}, title = {Firefighter Virtual Reality Simulation for Personalized Stress Detection}, booktitle = {the 43rd German Conference on AI}, publisher = {Springer}, pages = {343-347}, year = {2020}, month = sep, abstract = {Classifying stress in firefighters poses challenges, such as accurate personalized labeling, unobtrusive recording, and training of adequate models. Acquisition of labeled data and verification in cage mazes or during hot trainings is time consuming. Virtual Reality (VR) and Internet of Things (IoT) wearables provide new opportunities to create better stressors for firefighter missions through an immersive simulation. In this demo, we present a VR-based setup that enables to simulate firefighter missions to trigger and more easily record specific stress levels. The goal is to create labeled datasets for personalized multilevel stress detection models that include multiple biosignals, such as heart rate variability from electrocardiographic RR intervals. The multi-level stress setups can be configured, consisting of different levels of mental stressors. The demo shows how we established the recording of a baseline and virtual missions with varying challenge levels to create a personalized stress calibration.}, } @article{221069791, author = {Matthes, Stefan and Han, Zhiwei and Qiu, Tianming and Michel, Bruno and Klingner, S{\"{o}}ren and Shen, Hao and Liu, Yuanting and Altakrouri, Bashar}, title = {Personalized Stress Detection with Self-supervised Learned Features}, publisher = {Arxiv}, year = {2020}, abstract = {Automated stress detection using physiological sensors is challenging due to inaccurate labeling and individual bias in the sensor data. Previous methods consider stress detection as a supervised classification task, where bad labeling leads to a large performance drop. Furthermore, the poor generalizability to unseen subjects reveals the importance of personalizing stress detection for both interand intra-individual sensor data variability. Towards this end we present a label-free feature extractor and an efficient personalization method with the ”human in the loop” approach. First, we capture the intra-individual variability and encode it in self-supervised learned features, which are usually well separable and independent of noisy stress labels. Next, personalization is achieved by assigning labels to critical reference points via very few interactions between subject and wearable device. The promising results of the conducted experiments show the effectiveness and efficiency of our proposed method.}, howpublished = {preprint}, keywords = {stress detection; self-supervised learning}, url = {https://www.semanticscholar.org/paper/Personalized-Stress-Detection-with-Self-supervised-Matthes-Han/95f19123d9fc0351e2311e93390604c81141f74b}, }