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

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