11 citations as recorded by crossref.

1. An Explainable Deep Learning Approach for Stress Detection in Wearable Sensor Measurements M. Moser et al. https://doi.org/10.3390/s24165085
2. Cycling under stress: Analysing the impact of sociodemographic and psychological factors on cyclists’ perceived safety: A German example C. Schmidt-Hamburger et al. https://doi.org/10.1016/j.trf.2025.07.004
3. Assessing the association between urban features and human physiological stress response using wearable sensors in different urban contexts Z. Zhang et al. https://doi.org/10.1016/j.healthplace.2022.102924
4. Assessing the association between overcrowding and human physiological stress response in different urban contexts: a case study in Salzburg, Austria Z. Zhang et al. https://doi.org/10.1186/s12942-023-00334-7
5. Understanding the influence of urban characteristics on cyclists’ stress measured through wearable sensors: A quantitative open data approach M. Moser et al. https://doi.org/10.1177/23998083251394426
6. A multimodal approach combining wearable sensors and point of view videos to evaluate stress in urban cycling D. Graf et al. https://doi.org/10.1145/3785480
7. Identifying urban stress and bicycle infrastructure relationships: a mixed-methods citizen-science approach N. Haug et al. https://doi.org/10.1080/21650020.2023.2267636
8. Scenario Discovery for urban planning: The case of green urbanism and the impact on stress L. Lahoz et al. https://doi.org/10.1016/j.futures.2025.103727
9. Healing the city: a diagnostic approach to decoding stress in urban public spaces N. Haug et al. https://doi.org/10.1080/23748834.2025.2571264
10. Emotive facets of place meet urban analytics A. Acedo et al. https://doi.org/10.1111/tgis.12990
11. The Feasibility of Integrating Wearable Cameras and Health Trackers for Measuring Personal Exposure to Urban Features Z. Zhang et al. https://doi.org/10.4018/IJEPR.313181