This paper is in the following e-collection/theme issue:

Digital Biomarkers and Digital Phenotyping (191) Data Science (270) New Methods (944) Machine Learning (1465) Ecological Momentary Assessment (EMA) (309) mHealth for Data Collection and Research (955)

Published on in Vol 8, No 9 (2020): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/17818, first published .
Using Machine Learning and Smartphone and Smartwatch Data to Detect Emotional States and Transitions: Exploratory Study

Using Machine Learning and Smartphone and Smartwatch Data to Detect Emotional States and Transitions: Exploratory Study

Using Machine Learning and Smartphone and Smartwatch Data to Detect Emotional States and Transitions: Exploratory Study

Authors of this article:

Madeena Sultana1, 2 Author Orcid Image ;   Majed Al-Jefri1, 3 Author Orcid Image ;   Joon Lee1, 2, 4 Author Orcid Image
Article Authors Cited by (22) Tweetations (14) Metrics

Journals

  1. H Hasan M, Al-Ramini A, Abdel-Rahman E, Jafari R, Alsaleem F. Colocalized Sensing and Intelligent Computing in Micro-Sensors. Sensors 2020;20(21):6346 View
  2. Baumeister H, Bauereiss N, Zarski A, Braun L, Buntrock C, Hoherz C, Idrees A, Kraft R, Meyer P, Nguyen T, Pryss R, Reichert M, Sextl T, Steinhoff M, Stenzel L, Steubl L, Terhorst Y, Titzler I, Ebert D. Clinical and Cost-Effectiveness of PSYCHOnlineTHERAPY: Study Protocol of a Multicenter Blended Outpatient Psychotherapy Cluster Randomized Controlled Trial for Patients With Depressive and Anxiety Disorders. Frontiers in Psychiatry 2021;12 View
  3. Opoku Asare K, Terhorst Y, Vega J, Peltonen E, Lagerspetz E, Ferreira D. Predicting Depression From Smartphone Behavioral Markers Using Machine Learning Methods, Hyperparameter Optimization, and Feature Importance Analysis: Exploratory Study. JMIR mHealth and uHealth 2021;9(7):e26540 View
  4. Llamocca P, López V, Santos M, Čukić M. Personalized Characterization of Emotional States in Patients with Bipolar Disorder. Mathematics 2021;9(11):1174 View
  5. Taliaz D, Souery D. A New Characterization of Mental Health Disorders Using Digital Behavioral Data: Evidence from Major Depressive Disorder. Journal of Clinical Medicine 2021;10(14):3109 View
  6. Adler D, Wang F, Mohr D, Choudhury T, Chen C. Machine learning for passive mental health symptom prediction: Generalization across different longitudinal mobile sensing studies. PLOS ONE 2022;17(4):e0266516 View
  7. Lekkas D, Price G, Jacobson N. Using smartphone app use and lagged-ensemble machine learning for the prediction of work fatigue and boredom. Computers in Human Behavior 2022;127:107029 View
  8. Carlier C, Niemeijer K, Mestdagh M, Bauwens M, Vanbrabant P, Geurts L, van Waterschoot T, Kuppens P. In Search of State and Trait Emotion Markers in Mobile-Sensed Language: Field Study. JMIR Mental Health 2022;9(2):e31724 View
  9. Calatrava-Nicolás F, Gutiérrez-Maestro E, Bautista-Salinas D, Ortiz F, González J, Vera-Repullo J, Jiménez-Buendía M, Méndez I, Ruiz-Esteban C, Mozos O. Robotic-Based Well-Being Monitoring and Coaching System for the Elderly in Their Daily Activities. Sensors 2021;21(20):6865 View
  10. Naegelin M, Weibel R, Kerr J, Schinazi V, La Marca R, von Wangenheim F, Hoelscher C, Ferrario A. An interpretable machine learning approach to multimodal stress detection in a simulated office environment. Journal of Biomedical Informatics 2023;139:104299 View
  11. Younis E, Zaki S, Kanjo E, Houssein E. Evaluating Ensemble Learning Methods for Multi-Modal Emotion Recognition Using Sensor Data Fusion. Sensors 2022;22(15):5611 View
  12. Hart A, Reis D, Prestele E, Jacobson N. Using Smartphone Sensor Paradata and Personalized Machine Learning Models to Infer Participants’ Well-being: Ecological Momentary Assessment. Journal of Medical Internet Research 2022;24(4):e34015 View
  13. Moura I, Teles A, Viana D, Marques J, Coutinho L, Silva F. Digital Phenotyping of Mental Health using multimodal sensing of multiple situations of interest: A Systematic Literature Review. Journal of Biomedical Informatics 2023;138:104278 View
  14. De Boer C, Ghomrawi H, Zeineddin S, Linton S, Kwon S, Abdullah F. A Call to Expand the Scope of Digital Phenotyping. Journal of Medical Internet Research 2023;25:e39546 View
  15. Haleem M, Ekuban A, Antonini A, Pagliara S, Pecchia L, Allocca C. Deep-Learning-Driven Techniques for Real-Time Multimodal Health and Physical Data Synthesis. Electronics 2023;12(9):1989 View
  16. Hirten R, Suprun M, Danieletto M, Zweig M, Golden E, Pyzik R, Kaur S, Helmus D, Biello A, Landell K, Rodrigues J, Bottinger E, Keefer L, Charney D, Nadkarni G, Suarez-Farinas M, Fayad Z. A machine learning approach to determine resilience utilizing wearable device data: analysis of an observational cohort. JAMIA Open 2023;6(2) View
  17. Gutierrez Maestro E, De Almeida T, Schaffernicht E, Martinez Mozos Ó. Wearable-Based Intelligent Emotion Monitoring in Older Adults during Daily Life Activities. Applied Sciences 2023;13(9):5637 View
  18. Zhang A, Wu Z, Wu E, Wu M, Snyder M, Zou J, Wu J. Leveraging physiology and artificial intelligence to deliver advancements in health care. Physiological Reviews 2023;103(4):2423 View
  19. Jacob S, Vinod P, Subramanian A, Menon V. Affect sensing from smartphones through touch and motion contexts. Multimedia Systems 2023;29(5):2495 View
  20. dos Santos M, Heckler W, Bavaresco R, Barbosa J. Machine learning applied to digital phenotyping: A systematic literature review and taxonomy. Computers in Human Behavior 2024;161:108422 View
  21. Oh K, Ko J, Jin N, Han S, Yoon C, Shin J, Ko M. Understanding Morning Emotions by Analyzing Daily Wake-Up Alarm Usage: A Longitudinal Observational Study (Preprint). JMIR Human Factors 2023 View