Mental health monitoring with multimodal sensing and machine learning: A survey

Garcia-Ceja, Enrique; Riegler, Michael; Nordgreen, Tine; Jakobsen, Petter; Oedegaard, Ketil J.; Tørresen, Jim

doi:10.1016/j.pmcj.2018.09.003

Cited by 272 publications

(165 citation statements)

References 107 publications

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“…In this subsection, we describe some earlier research about depression/bipolar disorder, where they also applied machine learning to their study. E. Garcia-Ceja et al surveyed some of the recent research works in machine learning for MHMS [14]. They gave the different works labels: study type (association/detection/forecasting), study duration (short-term or long term), and sensor types (wearable/external/software or social media).…”

Section: Mental Health Monitoring Systemsmentioning

confidence: 99%

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One-Dimensional Convolutional Neural Networks on Motor Activity Measurements in Detection of Depression

Frogner

Noori

Halvorsen

et al. 2019

Proceedings of the 4th International Workshop on Multimedia for Personal Health &Amp; Health Care

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Teaching machines to learn patterns in data is very common these days, and it has a broad spectrum of applications everywhere. Sensors like smart-watches are getting more functionality each year, and more and more people buy them. Passing data from the watches, for example, activity or heart rate to machine learning algorithms, can generate significant results within many fields. Mental health is an example of a field where computer-generated predictions can be helpful to gain knowledge about patients. For example, machine learning can help predict that someone has a specific type of mental disorder. In this thesis, we present applied machine learning to detect depression. The dataset (collected for another study about behavioral patterns in schizophrenia vs. major depression) contains motor activity measurements for each minute in the measured period for each participant. Three machine learning models are trained to fit time-sliced segments of these measurements. The first model classifies participants into condition group (depressed) and control group (non-depressed). We trained another model to classify the depression level of participants (normal, mild or moderate). Finally, we trained a model that predicts MADRS scores. We evaluate the performance of classification models using leave one participant out validation as a technique, in which we achieved an average F1-score of 0.70 for detecting control/condition group and 0.30 for detecting the depression levels. The MADRS score prediction resulted in a mean squared error of approximately 4.0. These performance scores are promising, but not good enough to be used in the real world. However, not much more work is needed for the first model if we apply it to a dataset with more participants.

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Section: Mental Health Monitoring Systemsmentioning

confidence: 99%

“…The wearable sensor types include smart-watches and smartphones, external sensors could, for example, be cameras or microphones installed in an institution where the participants were patients. Some studies, where the sensor type was software or social media used services like Instagram to collect their data [14].…”

Section: Mental Health Monitoring Systemsmentioning

confidence: 99%

One-Dimensional Convolutional Neural Networks on Motor Activity Measurements in Detection of Depression

Frogner

Noori

Halvorsen

et al. 2019

Proceedings of the 4th International Workshop on Multimedia for Personal Health &Amp; Health Care

Self Cite

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“…Current reviews do not develop or apply existing frameworks of digital phenotyping (ie, frameworks of relationships between behaviours, digital traces/sensors and health conditions, eg, suggested by Mohr et al 12 or Garcia-Ceja et al 13) to highlight gaps, identify potential digital markers and generate new hypotheses. Huckvale, K. et al, 14 provide a general overview and mapping of digital phenotyping to clinical application, that is, prevention, screening, monitoring and treatment but do not attempt to map associations between digital markers and mental health outcomes.…”

Section: Study Rationalementioning

confidence: 99%

Digital phenotyping for assessment and prediction of mental health outcomes: a scoping review protocol

Spinazze

Rykov²,

Bottle

et al. 2019

BMJ Open

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IntroductionRapid advancements in technology and the ubiquity of personal mobile digital devices have brought forth innovative methods of acquiring healthcare data. Smartphones can capture vast amounts of data both passively through inbuilt sensors or connected devices and actively via user engagement. This scoping review aims to evaluate evidence to date on the use of passive digital sensing/phenotyping in assessment and prediction of mental health.Methods and analysisThe methodological framework proposed by Arksey and O’Malley will be used to conduct the review following the five-step process. A three-step search strategy will be used: (1) Initial limited search of online databases namely, MEDLINE for literature on digital phenotyping or sensing for key terms; (2) Comprehensive literature search using all identified keywords, across all relevant electronic databases: IEEE Xplore, MEDLINE, the Cochrane Database of Systematic Reviews, PubMed, the ACM Digital Library and Web of Science Core Collection (Science Citation Index Expanded and Social Sciences Citation Index), Scopus and (3) Snowballing approach using the reference and citing lists of all identified key conceptual papers and primary studies. Data will be charted and sorted using a thematic analysis approach.Ethics and disseminationThe findings from this systematic scoping review will be reported at scientific meetings and published in a peer-reviewed journal.

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“…Machine learning is used in many domains like marketing, health, e-commerce, finance, opinion analysis [22,23].…”

Section: Use Cases Of Machine Learningmentioning

confidence: 99%

Using Big Data-machine learning models for diabetes prediction and flight delays analytics

Nibareke

Laassiri

2020

J Big Data

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Introduction Nowadays large data volumes are daily generated at a high rate. Data from health system, social network, financial, government, marketing, bank transactions as well as the censors and smart devices are increasing. The tools and models have to be optimized. In this paper we applied and compared Machine Learning algorithms (Linear Regression, Naïve bayes, Decision Tree) to predict diabetes. Further more, we performed analytics on flight delays. The main contribution of this paper is to give an overview of Big Data tools and machine learning models. We highlight some metrics that allow us to choose a more accurate model. We predict diabetes disease using three machine learning models and then compared their performance. Further more we analyzed flight delay and produced a dashboard which can help managers of flight companies to have a 360° view of their flights and take strategic decisions. Case description We applied three Machine Learning algorithms for predicting diabetes and we compared the performance to see what model give the best results. We performed analytics on flights datasets to help decision making and predict flight delays. Discussion and evaluation The experiment shows that the Linear Regression, Naive Bayesian and Decision Tree give the same accuracy (0.766) but Decision Tree outperforms the two other models with the greatest score (1) and the smallest error (0). For the flight delays analytics, the model could show for example the airport that recorded the most flight delays. Conclusions Several tools and machine learning models to deal with big data analytics have been discussed in this paper. We concluded that for the same datasets, we have to carefully choose the model to use in prediction. In our future works, we will test different models in other fields (climate, banking, insurance.).

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Mental health monitoring with multimodal sensing and machine learning: A survey

Cited by 272 publications

References 107 publications

One-Dimensional Convolutional Neural Networks on Motor Activity Measurements in Detection of Depression

One-Dimensional Convolutional Neural Networks on Motor Activity Measurements in Detection of Depression

Digital phenotyping for assessment and prediction of mental health outcomes: a scoping review protocol

Using Big Data-machine learning models for diabetes prediction and flight delays analytics

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