@article {28328,
	title = {Complexity and Variability Analyses of Motor Activity Distinguish Mood States in Bipolar Disorder},
	journal = {PLOS ONE},
	volume = {17},
	number = {1},
	year = {2022},
	pages = {e0262232},
	publisher = {PLOS ONE},
	author = {Jakobsen, Petter and Stautland, Andrea and Riegler, Michael and C{\^o}t{\'e}-Allard, Ulysse and Sepasdar, Zahra and Nordgreen, Tine and Torresen, Jim and Ole Bernt {Fasmer} and Ketil J. {Oedegaard}}
}
@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 {27594,
	title = {Human Activity Recognition from Multiple Sensors Data Using Multi-fusion Representations and CNNs},
	journal = {ACM Transactions on Multimedia Computing, Communications, and Applications},
	volume = {16},
	year = {2020},
	month = {Mar-06-2021},
	pages = {1 - 19},
	publisher = {ACM},
	address = {New York},
	issn = {1551-6857},
	doi = {10.1145/3377882},
	url = {https://dl.acm.org/doi/10.1145/3377882},
	author = {Farzan Majeed {Noori} and Riegler, Michael and Md Zia {Uddin} and Torresen, Jim}
}
@inproceedings {noori2019heart,
	title = {Heart Rate Prediction from Head Movement during Virtual Reality Treatment for Social Anxiety},
	journal = {2019 International Conference on Content-Based Multimedia Indexing (CBMI)},
	year = {2019},
	pages = {1{\textendash}5},
	publisher = {IEEE},
	author = {Farzan Majeed {Noori} and Kahlon, Smiti and Lindner, Philip and Nordgreen, Tine and Torresen, Jim and Riegler, Michael}
}
@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 {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}
}
