@article {27867,
	title = {Kvasir-Capsule, a video capsule endoscopy dataset},
	journal = {Scientific Data},
	volume = {8},
	year = {2021},
	pages = {142},
	publisher = {Springer Nature},
	abstract = {Artificial intelligence (AI) is predicted to have profound effects on the future of video capsule endoscopy (VCE) technology. The potential lies in improving anomaly detection while reducing manual labour. Existing work demonstrates the promising benefits of AI-based computer-assisted diagnosis systems for VCE. They also show great potential for improvements to achieve even better results. Also, medical data is often sparse and unavailable to the research community, and qualified medical personnel rarely have time for the tedious labelling work. We present\&nbsp;Kvasir-Capsule, a large VCE dataset collected from examinations at a Norwegian Hospital.\&nbsp;Kvasir-Capsule\&nbsp;consists of 117 videos which can be used to extract a total of 4,741,504 image frames. We have labelled and medically verified 47,238 frames with a bounding box around findings from 14 different classes. In addition to these labelled images, there are 4,694,266 unlabelled frames included in the dataset. The\&nbsp;Kvasir-Capsule\&nbsp;dataset can play a valuable role in developing better algorithms in order to reach true potential of VCE technology.},
	doi = {10.1038/s41597-021-00920-z},
	url = {http://www.nature.com/articles/s41597-021-00920-z},
	author = {Pia H. {Smedsrud} and Thambawita, Vajira and Hicks, Steven and Gjestang, Henrik and Oda Olsen {Nedrejord} and N{\ae}ss, Espen and Borgli, Hanna and Jha, Debesh and Tor Jan Derek {Berstad} and Sigrun L. {Eskeland} and Lux, Mathias and Espeland, H{\r a}vard and Petlund, Andreas and Duc Tien Dang {Nguyen} and Garcia-Ceja, Enrique and Johansen, Dag and Peter T. {Schmidt} and Toth, Ervin and Hugo Lewi {Hammer} and de Lange, Thomas and Riegler, Michael and Halvorsen, P{\r a}l}
}
@article {27595,
	title = {Applying machine learning in motor activity time series of depressed bipolar and unipolar patients compared to healthy controls},
	journal = {PLOS ONE},
	volume = {15},
	year = {2020},
	month = {Dec-08-2021},
	pages = {e0231995},
	publisher = {PLOS ONE},
	doi = {https://doi.org/10.1371/journal.pone.0231995},
	url = {https://dx.plos.org/10.1371/journal.pone.0231995},
	author = {Jakobsen, Petter and Garcia-Ceja, Enrique and Riegler, Michael and Lena Antonsen {Stabell} and Nordgreen, Tine and Torresen, Jim and Ole Bernt {Fasmer} and Ketil Joachim {Oedegaard}},
	editor = {Na, Kyoung-Sae}
}
@article {27601,
	title = {A Genetic Attack Against Machine Learning Classifiers to Steal Biometric Actigraphy Profiles from Health Related Sensor Data},
	journal = {Journal of Medical Systems},
	volume = {44},
	year = {2020},
	month = {Jan-10-2020},
	pages = {1-11},
	publisher = { Springer},
	issn = {0148-5598},
	doi = {10.1007/s10916-020-01646-y},
	url = {http://link.springer.com/10.1007/s10916-020-01646-y},
	author = {Garcia-Ceja, Enrique and Morin, Brice and Aguilar-Rivera, Anton and Riegler, Michael}
}
@article {27472,
	title = {HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy},
	journal = {Scientific Data},
	volume = {7},
	year = {2020},
	month = {08/2020},
	pages = {1-14},
	publisher = {Springer Nature},
	abstract = {Artificial intelligence is currently a hot topic in medicine. However, medical data is often sparse and hard to obtain due to legal restrictions and lack of medical personnel for the cumbersome and tedious process to manually label training data. These constraints make it difficult to develop systems for automatic analysis, like detecting disease or other lesions. In this respect, this article presents\&nbsp;HyperKvasir, the largest image and video dataset of the gastrointestinal tract available today. The data is collected during real gastro- and colonoscopy examinations at B{\ae}rum Hospital in Norway and partly labeled by experienced gastrointestinal endoscopists. The dataset contains 110,079 images and 374 videos, and represents anatomical landmarks as well as pathological and normal findings. The total number of images and video frames together is around 1 million. Initial experiments demonstrate the potential benefits of artificial intelligence-based computer-assisted diagnosis systems. The\&nbsp;HyperKvasir\&nbsp;dataset can play a valuable role in developing better algorithms and computer-assisted examination systems not only for gastro- and colonoscopy, but also for other fields in medicine.},
	keywords = {dataset, GI, Machine learning},
	doi = {10.1038/s41597-020-00622-y},
	url = {http://www.nature.com/articles/s41597-020-00622-y},
	author = {Borgli, Hanna and Thambawita, Vajira and Pia H. {Smedsrud} and Hicks, Steven and Jha, Debesh and Sigrun L. {Eskeland} and Kristin Ranheim {Randel} and Pogorelov, Konstantin and Lux, Mathias and Duc Tien Dang {Nguyen} and Johansen, Dag and Griwodz, Carsten and H{\r a}kon Kvale {Stensland} and Garcia-Ceja, Enrique and Peter T. {Schmidt} and Hugo Lewi {Hammer} and Riegler, Michael and Halvorsen, P{\r a}l and de Lange, Thomas}
}
@inproceedings {27596,
	title = {PSYKOSE: A Motor Activity Database of Patients with Schizophrenia},
	journal = {2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)},
	year = {2020},
	publisher = {IEEE},
	address = {Rochester, MN, USA},
	doi = {10.1109/CBMS49503.202010.1109/CBMS49503.2020.00064},
	url = {https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9169740https://ieeexplore.ieee.org/document/9182896/http://xplorestaging.ieee.org/ielx7/9169740/9182790/09182896.pdf?arnumber=9182896},
	author = {Jakobsen, Petter and Garcia-Ceja, Enrique and Lena Antonsen {Stabell} and Ketil Joachim {Oedegaard} and Jan Oystein {Berle} and Thambawita, Vajira and Hicks, Steven and Halvorsen, P{\r a}l and Ole Bernt {Fasmer} and Riegler, Michael}
}
@inproceedings {noori2019fusion,
	title = {Fusion of multiple representations extracted from a single sensor{\textquoteright}s data for activity recognition using CNNs},
	journal = {2019 International Joint Conference on Neural Networks (IJCNN)},
	year = {2019},
	pages = {1{\textendash}6},
	publisher = {IEEE},
	author = {Farzan Majeed {Noori} and Garcia-Ceja, Enrique and Md Zia {Uddin} and Riegler, Michael and T{\o}rresen, Jim}
}
@inproceedings {26860,
	title = {One-Dimensional Convolutional Neural Networks on Motor Activity Measurements in Detection of Depression},
	journal = {Proceedings of the 4th International Workshop on Multimedia for Personal Health \& Health Care  - HealthMedia {\textquoteright}19},
	year = {2019},
	month = {10/2019},
	pages = {9-15},
	publisher = {ACM Press},
	address = {New York, NY, USA},
	abstract = {Nowadays, it has become possible to measure different human activities using wearable devices. Besides measuring the number of daily steps or calories burned, these datasets have much more potential since different activity levels are also collected. Such data would be helpful in the field of psychology because it can relate to various mental health issues such as changes in mood and stress. In this paper, we present a machine learning approach to detect depression using a dataset with motor activity recordings of one group of people with depression and one group without, i.e., the condition group includes 23 unipolar and bipolar persons, and the control group includes 32 persons without depression. We use convolutional neural networks to classify the depressed and nondepressed patients. Moreover, different levels of depression were classified. Finally, we trained a model that predicts Montgomery{\r A}sberg Depression Rating Scale scores. We achieved an average F1-score of 0.70 for detecting the control and condition groups. The mean squared error for score prediction was approximately 4.0.\&nbsp;},
	isbn = {9781450369145},
	doi = {10.1145/334744410.1145/3347444.3356238},
	url = {http://dl.acm.org/citation.cfm?doid=3347444http://dl.acm.org/citation.cfm?doid=3347444.3356238http://dl.acm.org/ft_gateway.cfm?id=3356238\&ftid=2091091\&dwn=1},
	author = {Joakim Ihle {Frogner} and Farzan Majeed {Noori} and Halvorsen, P{\r a}l and Hicks, Steven and Garcia-Ceja, Enrique and Torresen, Jim and Riegler, Michael},
	editor = {Boll, Susanne and Lee, Jeannie and Meyer, Jochen and Nag, Nitish and Noel E. {O{\textquoteright}Connor}}
}
@article {garcia2019user,
	title = {User-adaptive models for activity and emotion recognition using deep transfer learning and data augmentation},
	journal = {User Modeling and User-Adapted Interaction},
	year = {2019},
	pages = {1{\textendash}29},
	publisher = {Springer Netherlands},
	author = {Garcia-Ceja, Enrique and Riegler, Michael and Anders K {Kvernberg} and Torresen, Jim}
}
@inproceedings {26171,
	title = {Depresjon: A Motor Activity Database of Depression Episodes in Unipolar and Bipolar Patients},
	journal = {Proceedings of the 9th ACM Multimedia Systems Conference},
	year = {2018},
	month = {06/2018},
	pages = {472-477},
	publisher = {ACM Press},
	address = {New York, NY, USA},
	abstract = {Wearable sensors measuring different parts of people{\textquoteright}s activity are a common technology nowadays. In research, data collected using these devices also draws attention. Nevertheless, datasets containing sensor data in the field of medicine are rare. Often, data is non-public and only results are published. This makes it hard for other researchers to reproduce and compare results or even collaborate. In this paper we present a unique dataset containing sensor data collected from patients suffering from depression. The dataset contains motor activity recordings of 23 unipolar and bipolar depressed patients and 32 healthy controls. For each patient we provide sensor data over several days of continuous measuring and also some demographic data. The severity of the patients{\textquoteright} depressive state was labeled using ratings done by medical experts on the Montgomery-Asberg Depression Rating Scale (MADRS). In this respect, the here presented dataset can be useful to explore and understand the association between depression and motor activity better. By making this dataset available, we invite and enable interested researchers the possibility to tackle this challenging and important societal problem.},
	isbn = {9781450351928},
	doi = {10.1145/3204949.3208125},
	author = {Garcia-Ceja, Enrique and Riegler, Michael and Jakobsen, Petter and T{\o}rresen, Jim and Nordgreen, Tine and Ketil J. {Oedegaard} and Ole Bernt {Fasmer}},
	editor = {Cesar, Pablo and Zink, Michael and Murray, Niall}
}
@inproceedings {26170,
	title = {Motor Activity Based Classification of Depression in Unipolar and Bipolar Patients},
	journal = {2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS)},
	year = {2018},
	month = {06/2018},
	pages = {316-321},
	publisher = {IEEE},
	address = {Karlstad, Sweden},
	abstract = {Wearable sensors measuring different parts of people{\textquoteright}s activity are a common technology nowadays. Data created using these devices holds a lot of potential besides measuring the quantity of daily steps or calories burned, since continuous recordings of heart rate and activity levels usually are collected. Furthermore, there is an increasing awareness in the field of psychiatry on how these activity data relates to various mental health issues such as changes in mood, personality, inability to cope with daily problems or stress and withdrawal from friends and activities. In this paper we present the analysis of a unique dataset containing sensor data collected from patients suffering from depression. The dataset contains motor activity recordings of 23 unipolar and bipolar depressed patients and 32 healthy controls. We apply machine learning to classify patients into depressed and nondepressed. For evaluation of the algorithms, leave one patient out validation is performed. The best results achieved are an F1 score of 0.73 and a MCC of 0.44. The overall findings show that sensor data contains information that can be used to determine the depression status of a person.},
	issn = {2372-9198},
	doi = {10.1109/CBMS.2018.00062},
	url = {https://ieeexplore.ieee.org/document/8417257/http://xplorestaging.ieee.org/ielx7/8410976/8417175/08417257.pdf?arnumber=8417257},
	author = {Garcia-Ceja, Enrique and Riegler, Michael and Jakobsen, Petter and Torresen, Jim and Nordgreen, Tine and Ketil J. {Oedegaard} and Ole Bernt {Fasmer}},
	editor = {Kane, Bridget}
}
