@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}
}
@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 {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}
}
