Mental health monitoring with multimodal sensing and machine learning: A survey
Enrique Garcia-Ceja,Michael Riegler,Tine Nordgreen,Tine Nordgreen,Petter Jakobsen,Petter Jakobsen,Ketil J. Oedegaard,Ketil J. Oedegaard,Jim Torresen +8 more
TLDR
A classification taxonomy is proposed to guide the review of related works and present the overall phases of MHMS, allowing the automatic continuous monitoring of different mental conditions such as depression, anxiety, stress, and so on.About:
This article is published in Pervasive and Mobile Computing.The article was published on 2018-12-01 and is currently open access. It has received 186 citations till now. The article focuses on the topics: Mental health.read more
Citations
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Pervasive computing in the context of COVID-19 prediction with AI-based algorithms
TL;DR: The proposed research predicted 188 confirmed cases of COVID-19 disease using RNN with LSTM model and found that machine learning models are very efficient in predicting diseases.
Journal ArticleDOI
Systematic Review of Digital Phenotyping and Machine Learning in Psychosis Spectrum Illnesses.
TL;DR: This review examines passive digital phenotyping across the schizophrenia spectrum and bipolar disorders, with a focus on machine-learning studies, and identifies common data elements identified for inclusion in future studies.
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Smartphone based human activity monitoring and recognition using ML and DL: a comprehensive survey
Dipanwita Thakur,Suparna Biswas +1 more
TL;DR: This study unveils the “research gaps in the field of HAMR” to provide the future research directions in HAMR and presents a comprehensive survey of smartphone sensor based human activity monitoring and recognition using various ML and DL techniques.
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Machine Learning for Healthcare Wearable Devices: The Big Picture
TL;DR: A review of the different areas of the recent machine learning research for healthcare wearable devices is presented, and different challenges facing machine learning applications on wearable devices are discussed.
References
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Applied multiple regression/correlation analysis for the behavioral sciences
TL;DR: In this article, the Mathematical Basis for Multiple Regression/Correlation and Identification of the Inverse Matrix Elements is presented. But it does not address the problem of missing data.
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Data Mining: Concepts and Techniques
TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.
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Data Mining: Practical Machine Learning Tools and Techniques
TL;DR: This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.
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A Survey on Transfer Learning
Sinno Jialin Pan,Qiang Yang +1 more
TL;DR: The relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift are discussed.