@inproceedings {43291,
	title = {ScopeSense: An 8.5-Month Sport, Nutrition, and Lifestyle Lifelogging Dataset},
	journal = {International Conference on Multimedia Modeling (MMM)},
	volume = {13833},
	year = {2023},
	pages = {502 - 514},
	publisher = {Springer International Publishing},
	address = {Cham},
	abstract = {Nowadays, most people have a smartphone that can track their everyday activities. Furthermore, a significant number of people wear advanced smartwatches to track several vital biomarkers in addition to activity data. However, it is still unclear how these data can actually be used to improve certain aspects of people{\textquoteright}s lives. One of the key challenges is that the collected data is often massive and unstructured. Therefore, a link to other important information (e.g., when, what, and how much food was consumed) is required. It is widely believed that such detailed and structured longitudinal data about a person is essential to model and provide personalized and precise guidance. Despite the strong belief of researchers about the power of such a data-driven approach, respective datasets have been difficult to collect. In this study, we present a unique dataset from two individuals performing a structured data collection over eight and a half months. In addition to the sensor data, we collected their nutrition, training, and well-being data. The availability of nutrition data with many other important objectives and subjective longitudinal data streams may facilitate research related to food for a healthy lifestyle. Thus, we present a sport, nutrition, and lifestyle logging dataset called\&nbsp;ScopeSense\&nbsp;from two individuals and discuss its potential use. The dataset is fully open for researchers, and we consider this study as a potential starting point for developing methods to collect and create knowledge for a larger cohort of people.},
	isbn = {978-3-031-27076-5},
	issn = {0302-9743},
	doi = {10.1007/978-3-031-27077-210.1007/978-3-031-27077-2_39},
	url = {https://link.springer.com/content/pdf/10.1007/978-3-031-27077-2https://link.springer.com/content/pdf/10.1007/978-3-031-27077-2.pdfhttps://link.springer.com/10.1007/978-3-031-27077-2_39https://link.springer.com/content/pdf/10.1007/978-3-031-27077-2_39},
	author = {Riegler, Michael and Thambawita, Vajira and Chatterjee, Ayan and Nguyen, Thu and Hicks, Steven and Telle-Hansen, Vibeke and Svein Arne {Pettersen} and Johansen, Dag and Jain, Ramesh and Halvorsen, P{\r a}l},
	editor = {Dang-Nguyen, Duc-Tien and Gurrin, Cathal and Larson, Martha and Alan F. {Smeaton} and Rudinac, Stevan and Dao, Minh-Son and Trattner, Christoph and Chen, Phoebe}
}
@inproceedings {42726,
	title = {LSTM Step Prediction and Ontology-Based Recommendation Generation in Activity eCoaching},
	journal = {2022 18th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)},
	year = {2022},
	month = {10/2022},
	pages = {13-18},
	publisher = {IEEE},
	address = {Greece},
	author = {Chatterjee, Ayan and Pahari, Nibedita and Riegler, Michael and Prinz, Andreas}
}
@article {42994,
	title = {Machine learning and ontology in eCoaching for personalized activity level monitoring and recommendation generation},
	journal = {Scientific Reports},
	volume = {12},
	year = {2022},
	month = {Jan-12-2022},
	pages = {19825},
	publisher = {Nature},
	doi = {10.1038/s41598-022-24118-4},
	url = {https://www.nature.com/articles/s41598-022-24118-4https://www.nature.com/articles/s41598-022-24118-4.pdfhttps://www.nature.com/articles/s41598-022-24118-4.pdfhttps://www.nature.com/articles/s41598-022-24118-4},
	author = {Chatterjee, Ayan and Pahari, Nibedita and Prinz, Andreas and Riegler, Michael}
}
@inproceedings {42502,
	title = {Prediction Modeling in Activity eCoaching for Tailored Recommendation Generation: A Conceptualization},
	journal = {International Symposium on Medical Measurements and Applications (MeMeA)},
	year = {2022},
	publisher = {IEEE},
	doi = {10.1109/MeMeA54994.2022.9856556},
	author = {Chatterjee, Ayan and Prinz, Andreas and Riegler, Michael}
}
