@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 {42713,
	title = {Prediction of Schizophrenia from Activity Data using Hidden Markov Model Parameters},
	journal = {Neural Computing and Applications},
	year = {2022},
	publisher = { Springer},
	author = {Boeker, Matthias and Riegler, Michael and Hugo Lewi {Hammer} and Jakobsen, Petter and Halvorsen, P{\r a}l}
}
@inproceedings {27641,
	title = {HTAD: A Home-Tasks Activities Dataset with Wrist-accelerometer and Audio Features},
	journal = {27th International Conference on Multimedia Modeling},
	year = {2021},
	pages = {196-205},
	publisher = { Springer},
	keywords = {Accelerometer, Activity  recognition, Audio, dataset, Sensor fusion},
	author = {Garcia-Ceja, Enrique and Thambawita, Vajira and Hicks, Steven and Jha, Debesh and Jakobsen, Petter and Hugo Lewi {Hammer} and Halvorsen, P{\r a}l and Riegler, Michael}
}
@inproceedings {28136,
	title = {HYPERAKTIV: An Activity Dataset from Patients with Attention-Deficit/Hyperactivity Disorder (ADHD)},
	journal = {Proceedings of the 12th ACM Multimedia Systems Conference (MMSys {\textquoteright}21)},
	year = {2021},
	pages = {314{\textendash}319},
	publisher = {ACM},
	abstract = {Machine learning research within healthcare frequently lacks the public data needed to be fully reproducible and comparable. Datasets are often restricted due to privacy concerns and legal requirements that come with patient-related data. Consequentially, many algorithms and models get published on the same topic without a standard benchmark to measure against. Therefore, this paper presents HYPERAKTIV, a public dataset containing health, activity, and heart rate data from patients diagnosed with attention deficit hyperactivity disorder, better known as ADHD. The dataset consists of data collected from 51 patients with ADHD and 52 clinical controls. In addition to the activity and heart rate data, we also include a series of patient attributes such as their age, sex, and information about their mental state, as well as output data from a computerized neuropsychological test. Together with the presented dataset, we also provide baseline experiments using traditional machine learning algorithms to predict ADHD based on the included activity data. We hope that this dataset can be used as a starting point for computer scientists who want to contribute to the field of mental health, and as a common benchmark for future work in ADHD analysis.},
	doi = {10.1145/3458305.3478454},
	url = {https://dl.acm.org/doi/10.1145/3458305.3478454},
	author = {Hicks, Steven and Stautland, Andrea and Ole Bernt {Fasmer} and Fr{\o}land, Wenche and Hugo Lewi {Hammer} and Halvorsen, P{\r a}l and Mjeldheim, Kristin and Ketil J. {Oedegaard} and Osnes, Berge and Vigdis Elin Gi{\ae}v {Syrstad} and Riegler, Michael and Jakobsen, Petter}
}
@inproceedings {27331,
	title = {Toadstool: A Dataset for Training Emotional IntelligentMachines Playing Super Mario Bros},
	journal = {The ACM Multimedia Systems Conference (MMSys)},
	year = {2020},
	publisher = {ACM},
	address = {The ACM Multimedia Systems Conference (MMSys)},
	abstract = {Games are often defined as engines of experience, and they are heavily relying on emotions, they arouse in players. In this paper, we present a dataset called Toadstool\&nbsp;as well as a reproducible methodology to extend on the dataset. The dataset consists of video, sensor, and demographic data collected from ten participants playing Super Mario Bros, an iconic and famous video game. The sensor data is collected through an Empatica E4 wristband, which provides high-quality measurements and is graded as a medical device. In addition to the dataset and the methodology for data collection, we present a set of baseline experiments which show that we can use video game frames together with the facial expressions to predict the blood volume pulse of the person playing Super Mario Bros. With the dataset and the collection methodology we aim to contribute to research on emotionally aware machine learning algorithms, focusing on reinforcement learning and multimodal data fusion. We believe that the presented dataset can be interesting for a manifold of researchers to explore exciting new interdisciplinary questions.\&nbsp;},
	doi = {10.1145/3339825.3394939},
	url = {https://dl.acm.org/doi/10.1145/3339825.3394939},
	author = {Svoren, Henrik and Thambawita, Vajira and Halvorsen, P{\r a}l and Jakobsen, Petter and Garcia-Ceja, Enrique and Farzan Majeed {Noori} and Hugo Lewi {Hammer} and Lux, Mathias and Riegler, Michael and Hicks, Steven}
}
@article {26167,
	title = {Mental Health Monitoring with Multimodal Sensing and Machine Learning: A Survey},
	journal = {Pervasive and Mobile Computing},
	volume = {51},
	year = {2018},
	month = {12/2018},
	pages = {1-26},
	publisher = {Elsevier},
	abstract = {Personal and ubiquitous sensing technologies such as smartphones have allowed the continuous collection of data in an unobtrusive manner. Machine learning methods have been applied to continuous sensor data to predict user contextual information such as location, mood, physical activity, etc. Recently, there has been a growing interest in leveraging ubiquitous sensing technologies for mental health care applications, thus, allowing the automatic continuous monitoring of different mental conditions such as depression, anxiety, stress, and so on. This paper surveys recent research works in mental health monitoring systems (MHMS) using sensor data and machine learning. We focused on research works about mental disorders/conditions such as: depression, anxiety, bipolar disorder, stress, etc. We propose a classification taxonomy to guide the review of related works and present the overall phases of MHMS. Moreover, research challenges in the field and future opportunities are also discussed.},
	doi = {https://doi.org/10.1016/j.pmcj.2018.09.003},
	author = {Garcia-Ceja, Enrique and Riegler, Michael and Nordgreen, Tine and Jakobsen, Petter and Ketil J. {Oedegaard} and T{\o}rresen, Jim}
}
