Smartphone-based Mental State Estimation: A Survey from a Machine Learning Perspective
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Ken Ohta | Yusuke Fukazawa | Naoki Yamamoto | Keiichi Ochiai | Takashi Hamatani | Akira Uchiyama | Keiichi Ochiai | Ken Ohta | Yusuke Fukazawa | A. Uchiyama | Naoki Yamamoto | Takashi Hamatani | Akira Uchiyama
[1] Wazir Zada Khan,et al. Mobile Phone Sensing Systems: A Survey , 2013, IEEE Communications Surveys & Tutorials.
[2] Bing Liu,et al. Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data , 2006, Data-Centric Systems and Applications.
[3] Mirco Musolesi,et al. Using Autoencoders to Automatically Extract Mobility Features for Predicting Depressive States , 2018, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..
[4] Kazuhiko Taira,et al. An examination of sleep health, lifestyle and mental health in junior high school students , 2002, Psychiatry and clinical neurosciences.
[5] V. Osmani,et al. Correlation of significant places with self-reported state of bipolar disorder patients , 2014 .
[6] Akane Sano,et al. Identifying Objective Physiological Markers and Modifiable Behaviors for Self-Reported Stress and Mental Health Status Using Wearable Sensors and Mobile Phones: Observational Study , 2018, Journal of medical Internet research.
[7] Laura E. Barnes,et al. DemonicSalmon: Monitoring mental health and social interactions of college students using smartphones , 2018, Smart Health.
[8] Gin S Malhi,et al. Bipolar depression: phenomenological overview and clinical characteristics. , 2004, Bipolar disorders.
[9] Thomas Stütz,et al. Smartphone Based Stress Prediction , 2015, UMAP.
[10] Tanzeem Choudhury,et al. Passive and In-Situ assessment of mental and physical well-being using mobile sensors , 2011, UbiComp '11.
[11] Ceyhun Ozgur,et al. MatLab vs. Python vs. R , 2021 .
[12] Akane Sano,et al. Stress Recognition Using Wearable Sensors and Mobile Phones , 2013, 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction.
[13] Oscar Mayora-Ibarra,et al. Using smart phone mobility traces for the diagnosis of depressive and manic episodes in bipolar patients , 2014, AH.
[14] Rui Wang,et al. Tracking Depression Dynamics in College Students Using Mobile Phone and Wearable Sensing , 2018, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..
[15] Robert Tibshirani,et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.
[16] Norito Kawakami,et al. The Stress Check Program: a new national policy for monitoring and screening psychosocial stress in the workplace in Japan , 2016, Journal of occupational health.
[17] W. Katon,et al. The longitudinal effects of depression on physical activity. , 2009, General hospital psychiatry.
[18] Enzo Pasquale Scilingo,et al. Smartphone Application for the Analysis of Prosodic Features in Running Speech with a Focus on Bipolar Disorders: System Performance Evaluation and Case Study , 2015, Sensors.
[19] Patrick C. Staples,et al. A comparison of passive and active estimates of sleep in a cohort with schizophrenia , 2017, npj Schizophrenia.
[20] Fanglin Chen,et al. StudentLife: assessing mental health, academic performance and behavioral trends of college students using smartphones , 2014, UbiComp.
[21] Cem Ersoy,et al. Stress detection in daily life scenarios using smart phones and wearable sensors: A survey , 2019, J. Biomed. Informatics.
[22] Maarten De Vos,et al. Detecting Bipolar Depression From Geographic Location Data , 2016, IEEE Transactions on Biomedical Engineering.
[23] Scott Lundberg,et al. A Unified Approach to Interpreting Model Predictions , 2017, NIPS.
[24] David C. Atkins,et al. Smartphone-Based Passive Assessment of Mobility in Depression: Challenges and Opportunities. , 2018, Mental health and physical activity.
[25] Mirco Musolesi,et al. Towards multi-modal anticipatory monitoring of depressive states through the analysis of human-smartphone interaction , 2016, UbiComp Adjunct.
[26] Stephen P. Boyd,et al. Toeplitz Inverse Covariance-based Clustering of Multivariate Time Series Data , 2018, IJCAI.
[27] Skyler Place,et al. Behavioral Indicators on a Mobile Sensing Platform Predict Clinically Validated Psychiatric Symptoms of Mood and Anxiety Disorders , 2017, Journal of medical Internet research.
[28] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[29] J. Marden. Analyzing and Modeling Rank Data , 1996 .
[30] A. Muaremi,et al. Towards Measuring Stress with Smartphones and Wearable Devices During Workday and Sleep , 2013, BioNanoScience.
[31] Tanzeem Choudhury,et al. Towards circadian computing: "early to bed and early to rise" makes some of us unhealthy and sleep deprived , 2014, UbiComp.
[32] Oscar Mayora-Ibarra,et al. Smartphone-Based Recognition of States and State Changes in Bipolar Disorder Patients , 2015, IEEE Journal of Biomedical and Health Informatics.
[33] Ron Kohavi,et al. A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.
[34] Yu Huang,et al. Monitoring social anxiety from mobility and communication patterns , 2017, UbiComp/ISWC Adjunct.
[35] Mattia Prosperi,et al. A systematic review of the effectiveness of mobile apps for monitoring and management of mental health symptoms or disorders. , 2018, Journal of psychiatric research.
[36] Yu Huang,et al. Assessing social anxiety using gps trajectories and point-of-interest data , 2016, UbiComp.
[37] Daphna Weinshall,et al. Real-Time Schizophrenia Monitoring Using Wearable Motion Sensitive Devices , 2017, MobiHealth.
[38] Alexander Russell,et al. Joint Modeling of Heterogeneous Sensing Data for Depression Assessment via Multi-task Learning , 2018, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..
[39] Peter E. Hart,et al. Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.
[40] A Ehlers,et al. Psychophysiological differences between subgroups of social phobia. , 1995, Journal of abnormal psychology.
[41] Mounir Ghogho,et al. On the Use of Sensors in Mental Healthcare , 2019, Intelligent Environments.
[42] Yusuke Fukazawa,et al. Real-time On-Device Troubleshooting Recommendation for Smartphones , 2019, KDD.
[43] Alexander Russell,et al. Multi-view Bi-clustering to Identify Smartphone Sensing Features Indicative of Depression , 2016, 2016 IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE).
[44] Manoj Vengal,et al. Usefulness of salivary alpha amylase as a biomarker of chronic stress and stress related oral mucosal changes – a pilot study , 2014, Journal of clinical and experimental dentistry.
[45] Akane Sano,et al. Predicting Tomorrow's Mood, Health, and Stress Level using Personalized Multitask Learning and Domain Adaptation , 2017, AffComp@IJCAI.
[46] B. Löwe,et al. The Patient Health Questionnaire Somatic, Anxiety, and Depressive Symptom Scales: a systematic review. , 2010, General hospital psychiatry.
[47] Russell A. McCann,et al. mHealth for mental health: Integrating smartphone technology in behavioral healthcare. , 2011 .
[48] Vera Maljkovic,et al. Developing Measures of Cognitive Impairment in the Real World from Consumer-Grade Multimodal Sensor Streams , 2019, KDD.
[49] W. Rössler,et al. Using Smartphones to Monitor Bipolar Disorder Symptoms: A Pilot Study , 2016, JMIR mental health.
[50] Konrad Paul Kording,et al. Distributed under Creative Commons Cc-by 4.0 the Relationship between Mobile Phone Location Sensor Data and Depressive Symptom Severity , 2022 .
[51] K. Larkin,et al. Situational determinants of social anxiety in clinic and nonclinic samples: physiological and cognitive correlates. , 1986, Journal of consulting and clinical psychology.
[52] Andrew T. Campbell,et al. Next-generation psychiatric assessment: Using smartphone sensors to monitor behavior and mental health. , 2015, Psychiatric rehabilitation journal.
[53] Jakob E. Bardram,et al. Smartphone Data as an Electronic Biomarker of Illness Activity in Bipolar Disorder , 2015, European Psychiatry.
[54] Matjaz Gams,et al. Automatic Detection of Perceived Stress in Campus Students Using Smartphones , 2015, 2015 International Conference on Intelligent Environments.
[55] D. Eisenberg,et al. Mental health problems and help-seeking behavior among college students. , 2010, The Journal of adolescent health : official publication of the Society for Adolescent Medicine.
[56] Peter A. Flach,et al. Confirmation-Guided Discovery of First-Order Rules with Tertius , 2004, Machine Learning.
[57] Jun Ota,et al. Predicting anxiety state using smartphone-based passive sensing , 2019, J. Biomed. Informatics.
[58] Lars Vedel Kessing,et al. Smartphone-Based Self-Assessment of Stress in Healthy Adult Individuals: A Systematic Review , 2017, Journal of medical Internet research.
[59] Akane Sano,et al. Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones , 2015, 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN).
[60] K. Ogino,et al. Association of arginase I or nitric oxide-related factors with job strain in healthy workers , 2017, PloS one.
[61] Richard J. Holden,et al. Systematic review of smartphone-based passive sensing for health and wellbeing , 2018, J. Biomed. Informatics.
[62] Oscar Mayora-Ibarra,et al. Smartphone app usage as a predictor of perceived stress levels at workplace , 2015, 2015 9th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth).
[63] Eric J Topol,et al. Can mobile health technologies transform health care? , 2013, JAMA.
[64] M. van Eck,et al. The Effects of Perceived Stress, Traits, Mood States, and Stressful Daily Events on Salivary Cortisol , 1996, Psychosomatic medicine.
[65] H. Riper,et al. Mobile Phone-Based Unobtrusive Ecological Momentary Assessment of Day-to-Day Mood: An Explorative Study , 2016, Journal of medical Internet research.
[66] M. Bulgheroni,et al. Mobile Phone and Wearable Sensor-Based mHealth Approaches for Psychiatric Disorders and Symptoms: Systematic Review , 2019, JMIR mental health.
[67] Mirco Musolesi,et al. Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis , 2015, UbiComp.
[68] Carson Labrado,et al. Stress Detection and Management: A Survey of Wearable Smart Health Devices , 2017, IEEE Consumer Electronics Magazine.
[69] E. D. de Geus,et al. Effects of work stress on ambulatory blood pressure, heart rate, and heart rate variability. , 2000, Hypertension.
[70] I. Miller,et al. Functional impairment as a predictor of short-term symptom course in bipolar I disorder. , 2008, Bipolar disorders.
[71] Björn Schuller,et al. Opensmile: the munich versatile and fast open-source audio feature extractor , 2010, ACM Multimedia.
[72] Konrad Paul Kording,et al. Mobile Phone Sensor Correlates of Depressive Symptom Severity in Daily-Life Behavior: An Exploratory Study , 2015, Journal of medical Internet research.
[73] Oscar Mayora-Ibarra,et al. Monitoring activity of patients with bipolar disorder using smart phones , 2013, MoMM '13.
[74] B. Padmaja,et al. DetectStress: A Novel Stress Detection System Based on Smartphone and Wireless Physical Activity Tracker , 2019 .
[75] O. Mayora,et al. Activity and emotion recognition to support early diagnosis of psychiatric diseases , 2008, Pervasive 2008.
[76] F. Wahle,et al. Mobile Sensing and Support for People With Depression: A Pilot Trial in the Wild , 2016, JMIR mHealth and uHealth.
[77] Michael Beigl,et al. A wearable system for mood assessment considering smartphone features and data from mobile ECGs , 2016, UbiComp Adjunct.
[78] Rui Wang,et al. CrossCheck: toward passive sensing and detection of mental health changes in people with schizophrenia , 2016, UbiComp.
[79] S. Dickerson,et al. Acute stressors and cortisol responses: a theoretical integration and synthesis of laboratory research. , 2004, Psychological bulletin.
[80] Tanzeem Choudhury,et al. Automatic detection of social rhythms in bipolar disorder , 2016, J. Am. Medical Informatics Assoc..
[81] Jinbo Bi,et al. Multi-view Sparse Co-clustering via Proximal Alternating Linearized Minimization , 2015, ICML.
[82] Yoshio Nakamura,et al. Heart rate variability, trait anxiety, and perceived stress among physically fit men and women. , 2000, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.
[83] Naoki Yamamoto,et al. Physiological Stress Level Estimation Based on Smartphone Logs , 2018, 2018 Eleventh International Conference on Mobile Computing and Ubiquitous Network (ICMU).
[84] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[85] M. McInnis,et al. Predicting Mood Disturbance Severity with Mobile Phone Keystroke Metadata: A BiAffect Digital Phenotyping Study , 2018, Journal of medical Internet research.
[86] D. Kupfer,et al. Lifestyle regularity and activity level as protective factors against bereavement-related depression in late-life , 1995 .
[87] Konrad P Kording,et al. Mobile Phone Detection of Semantic Location and Its Relationship to Depression and Anxiety , 2017, JMIR mHealth and uHealth.
[88] Lior Rokach,et al. Ensemble learning: A survey , 2018, WIREs Data Mining Knowl. Discov..
[89] C. Dancu,et al. Physiological, cognitive and behavioral aspects of social anxiety. , 1985, Behaviour research and therapy.
[90] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[91] J. Ross Quinlan,et al. C4.5: Programs for Machine Learning , 1992 .
[92] Shaohan Hu,et al. DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data Processing , 2016, WWW.
[93] Jure Leskovec,et al. Modeling Interdependent and Periodic Real-World Action Sequences , 2018, WWW.
[94] John Zimmerman,et al. Detection of Behavior Change in People with Depression , 2014, AAAI Workshop: Modern Artificial Intelligence for Health Analytics.
[95] Carlos Guestrin,et al. "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, ArXiv.
[96] P. Chauvin,et al. The role of daily mobility in mental health inequalities: the interactive influence of activity space and neighbourhood of residence on depression. , 2011, Social science & medicine.