@phdthesis{ubo_mods_00184619,
  author = {Torkamaan, Helma},
  title = {Health Recommender Systems for Mental Health Promotion},
  year = {2022},
  address = {Duisburg, Essen},
  keywords = {Health recommender systems; Mobile health; Mental health promotion; Mood; Stress; Mood Tracking},
  abstract = {Recommender systems are today an essential part of software applications used in everyday life and facilitate the decision-making process for users by personalizing the options from which they can choose. An emerging and rapidly growing application domain for these systems is health care, and the majority of research contributions related to health recommender systems are about preventive health care. However, some crucial areas in this domain have been mostly overlooked. One such area is mental health promotion, which, despite its critical importance, has a relatively negligible share in existing solutions and research. User stress and mood are fundamental concepts in preventive health care, and proper skills for coping with stress and improving mood are crucial for individual mental well-being, which is the central theme of this dissertation. Health recommender systems have high potential benefits in personalizing health-related recommendations and, especially, engaging users in behavior change processes. A health recommender system for health promotion and behavior change is a holistic system that, ideally, uses techniques from ubiquitous computing to provide pervasive health. Building a health recommender system, therefore, is a multidisciplinary effort that engages various areas, which we summarize as tracking, interacting, and personalizing components, and address them in this dissertation regarding our recommendation domain, stress reduction. In particular, we discuss three major contributions to the problem of building health recommender systems for stress reduction and mood improvement: (1) establishing proper ways to track user mood; (2) building one of the first interactive mobile health recommender system research platforms and providing an extensive holistic dataset for flexible investigation of health recommender systems in the future; and (3) developing dynamic mood and health-aware, user-engaging algorithms and carefully comparing the performance and characteristics of the presented techniques. These contributions were the result of various mixed and longitudinal user studies which engaged with more than 2,500 users. This dissertation brings together for the first time various aspects of user decision-making - such as explicit short-term preferences, health needs, and long-term goals - for a holistic health-aware recommender system. By thoroughly discussing various components, this dissertation presents a roadmap for building health recommender systems, and interactive, mood-aware, and mental health-promoting systems in the future.},
  school = {University of Duisburg-Essen},
  doi = {10.17185/duepublico/76050},
  url = {https://doi.org/10.17185/duepublico/76050}
}


@inproceedings{ubo_mods_00168133,
  author = {Torkamaan, Helma and Ziegler, Jürgen},
  title = {Integrating Behavior Change and Persuasive Design Theories into an Example Mobile Health Recommender System},
  booktitle = {UbiComp ’21: Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers},
  year = {2021},
  publisher = {Association for Computing Machinery},
  address = {New York},
  pages = {218–225},
  keywords = {Behavior Change; Health recommender systems; Persuasive Design},
  isbn = {978-1-4503-8461-2},
  doi = {10.1145/3460418},
  url = {https://doi.org/10.1145/3460418.3479330},
  language = {en}
}


@article{ubo_mods_00159948,
  author = {Torkamaan, Helma and Ziegler, Jürgen},
  title = {Mobile mood tracking: An investigation of concise and adaptive measurement instruments},
  journal = {Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies},
  year = {2020},
  publisher = {Association for Computing Machinery},
  volume = {4},
  number = {4},
  pages = {155},
  keywords = {User Compliance},
  issn = {2474-9567},
  doi = {10.1145/3432207}
}


@inproceedings{Torkamaan_2020_exploring,
  author = {Torkamaan, Helma and Ziegler, Jürgen},
  title = {Exploring chatbot user interfaces for mood measurement: A study of validity and user experience},
  booktitle = {Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers},
  year = {2020},
  publisher = {Association for Computing Machinery (ACM)},
  address = {New York},
  pages = {135–138},
  keywords = {PANAS},
  abstract = {With the growth of interactive text or voice-enabled systems, such as intelligent personal assistants and chatbots, it is now possible to easily measure a user’s mood using a conversation-based interaction instead of traditional questionnaires. However, it is still unclear if such mood measurements would be valid, akin to traditional measures, and user-engaging. Using smartphones, we compare in this paper two of the most popular traditional measures of mood: International PANAS-Short Form (I-PANAS-SF) and Affect Grid. For each of these measures, we then investigate the validity of mood measurement with a modified, chatbot-based user interface design. Our preliminary results suggest that some mood measures may not be resilient to modifications and that their alteration could lead to invalid, if not meaningless results. This exploratory paper then presents and discusses four voice-based mood tracker designs and summarizes user perception of and satisfaction with these tools. \textcopyright 2020 Owner/Author.},
  isbn = {9781450380768},
  doi = {10.1145/3410530.3414395},
  url = {https://dl.acm.org/doi/10.1145/3410530.3414395}
}


@inproceedings{ubo_mods_00136781,
  author = {Torkamaan, Helma and Ziegler, Jürgen},
  title = {Rating-based preference elicitation for recommendation of stress intervention},
  booktitle = {ACM UMAP 2019 - Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization},
  year = {2019},
  publisher = {ACM},
  address = {New York},
  pages = {46–50},
  keywords = {Preference elicitation},
  isbn = {978-1-4503-6021-0},
  doi = {10.1145/3320435.3324990},
  url = {http://dl.acm.org/ft_gateway.cfm?id=3324990&type=pdf}
}


@inproceedings{ubo_mods_00117090,
  author = {Torkamaan, Helma and Ziegler, Jürgen},
  title = {Multi-criteria rating-based preference elicitation in health recommender systems},
  booktitle = {Proceedings of the Third International Workshop on Health Recommender Systems co-located with Twelfth ACM Conference on Recommender Systems (HealthRecSys’18)},
  series = {CEUR Workshop Proceedings},
  year = {2018},
  month = {oct},
  address = {Aachen},
  volume = {2216},
  pages = {18–23},
  keywords = {Recommender systems},
  issn = {1613-0073},
  url = {http://ceur-ws.org/Vol-2216/healthRecSys18_paper_7.pdf},
  venue = {Vancouver, BC, Canada},
  month_numeric = {10}
}


@inproceedings{ubo_mods_00115757,
  author = {Loepp, Benedikt and Ziegler, Jürgen},
  title = {Recommending Running Routes: Framework and Demonstrator},
  booktitle = {Proceedings of the 2nd Second Workshop on Recommendation in Complex Scenarios (ComplexRec ’18)},
  year = {2018},
  pages = {26–29},
  keywords = {Sports},
  url = {http://toinebogers.com/workshops/complexrec2018/resources/proceedings.pdf#page=26},
  abstract = {Recommending personalized running routes is a challenging task. When the runner’s specific background as well as needs, preferences and goals are taken into account, a recommender cannot only rely on e.g. a set of existing routes ran by others, but needs to individually generate each route while considering many different aspects that determine whether a suggestion will satisfy the runner in the end, e.g. height meters or areas passed. We describe a framework that summarizes these aspects, allowing to generate personalized running routes. Based on this framework, we present a prototypical smartphone app that we implemented to actually demonstrate how running routes can be recommended based on the different requirements a runner might have. A first small study where users had to try this app and ran some of the recommended routes underlines the general effectiveness of our approach.}
}


@article{ubo_mods_00084836,
  author = {Bakhtiyari, Kaveh and Beckmann, Nils and Ziegler, Jürgen},
  title = {Contactless heart rate variability measurement by IR and 3D depth sensors with respiratory sinus arrhythmia},
  journal = {Procedia Computer Science},
  year = {2017},
  volume = {109},
  pages = {498–505},
  abstract = {Heart rate variability (HRV) is known to be correlated with emotional arousal, cognitive depletion, and health status. Despite the accurate HRV detection by various body-attached sensors, a contactless method is desirable for the HCI purposes. In this research, we propose a non-invasive contactless HRV measurement by Microsoft Kinect 2 sensor with Respiratory Sinus Arrhythmia (RSA) correction. The Infrared and RGB cameras are used to measure the heart rate signal, and its 3D Depth sensor is employed to capture the human respiratory signal to correct the initially calculated HRV with RSA. The correlation analysis among the calculated HRVs by different methods and devices showed a significant improvement in reliable HRV measurements. This study enlightens the researchers and developers to choose a proper method for HRV calculations based on their required accuracy and application.},
  issn = {1877-0509},
  doi = {10.1016/j.procs.2017.05.319},
  note = {The 8th International Conference on Ambient Systems, Networks and Technologies, ANT-2017 and the 7th International Conference on Sustainable Energy Information Technology, SEIT 2017, 16-19 May 2017, Madeira, Portugal}
}


@inproceedings{ubo:67235,
  author = {Herrmanny, Katja and Beckmann, Nils and Nachbar, Katrin and Sauer, Hanno and Ziegler, Jürgen and Dogangün, Aysegül and ACM CHI 2016},
  chapter = {},
  title = {Using psychophysiological parameters to support users in setting effective activity goals},
  year = {2016},
  publisher = {ACM},
  address = {New York, NY, USA},
  pages = {1637–1646},
  isbn = {978-1-4503-4082-3},
  doi = {10.1145/2851581.2892378},
  booktitle = {CHI EA ’16: Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems}
}


@inproceedings{ubo:67231,
  author = {Herrmanny, Katja and Ziegler, Jürgen and Dogangün, Aysegül and PERSUASIVE 2016},
  editor = {Meschtscherjakov, Alexander and De Ruyter, Boris and Fuchsberger, Verena and Murer, Martin and Tscheligi, Manfred},
  chapter = {},
  title = {Supporting users in setting effective goals in activity tracking},
  series = {Lecture notes in computer science},
  year = {2016},
  publisher = {Springer International Publishing},
  address = {Cham},
  volume = {9638},
  pages = {15–26},
  isbn = {978-3-319-31509-6},
  doi = {10.1007/978-3-319-31510-2_2},
  booktitle = {Persuasive Technology: 11th International Conference ; PERSUASIVE 2016 ; Salzburg, Austria, April 5-7, 2016 ; Proceedings}
}