Beyond Mobile Apps: A Survey of Technologies for Mental Well-Being

Mental health problems are on the rise globally and strain national health systems worldwide. Mental disorders are closely associated with fear of stigma, structural barriers such as financial burden, and lack of available services and resources which often prohibit the delivery of frequent clinical advice and monitoring. Technologies for mental well-being exhibit a range of attractive properties which facilitate the delivery of state of the art clinical monitoring. This review article provides an overview of traditional techniques followed by their technological alternatives, sensing devices, behaviour changing tools, and feedback interfaces. The challenges presented by these technologies are then discussed with data collection, privacy and battery life being some of the key issues which need to be carefully considered for the successful deployment of mental health tool-kits. Finally, the opportunities this growing research area presents are discussed including the use of portable tangible interfaces combining sensing and feedback technologies. Capitalising on the captured data these ubiquitous devices offer, state of the art machine learning algorithms can lead to the develop

[1]  Andrea Gaggioli,et al.  New Technologies to Manage Exam Anxiety , 2011, Annual Review of Cybertherapy and Telemedicine.

[2]  Sougata Sen,et al.  The Case for a Commodity Hardware Solution for Stress Detection , 2018, UbiComp/ISWC Adjunct.

[3]  Yoshua Bengio,et al.  Learning deep physiological models of affect , 2013, IEEE Computational Intelligence Magazine.

[4]  John Garcia and Andrew R. Gustavson The Science of Self-Report , 1997 .

[5]  Peter Washington,et al.  A Wearable Social Interaction Aid for Children with Autism , 2016, CHI Extended Abstracts.

[6]  A. Mitchell,et al.  Case finding and screening clinical utility of the Patient Health Questionnaire (PHQ-9 and PHQ-2) for depression in primary care: a diagnostic meta-analysis of 40 studies , 2016, BJPsych Open.

[7]  U. Rajendra Acharya,et al.  Heart rate variability: a review , 2006, Medical and Biological Engineering and Computing.

[8]  Si Sun,et al.  Investigating the Role of Context in Perceived Stress Detection in the Wild , 2018, UbiComp/ISWC Adjunct.

[9]  J. Durlak,et al.  Promoting and protecting youth mental health through evidence-based prevention and treatment. , 2005, The American psychologist.

[10]  T. Wallace,et al.  BreatheWell: Developing a Stress Management App on Wearables for TBI & PTSD , 2017 .

[11]  Saif Mohammad,et al.  NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweets , 2013, *SEMEVAL.

[12]  J. Markowitz,et al.  The 16-Item quick inventory of depressive symptomatology (QIDS), clinician rating (QIDS-C), and self-report (QIDS-SR): a psychometric evaluation in patients with chronic major depression , 2003, Biological Psychiatry.

[13]  M. Hutchesson,et al.  Self-monitoring of dietary intake by young women: online food records completed on computer or smartphone are as accurate as paper-based food records but more acceptable. , 2015, Journal of the Academy of Nutrition and Dietetics.

[14]  M. Hogan,et al.  A protocol for a randomised active-controlled trial to evaluate the effects of an online mindfulness intervention on executive control, critical thinking and key thinking dispositions in a university student sample , 2016, BMC psychology.

[15]  T. Ohkubo,et al.  Pulse wave velocity and the second derivative of the finger photoplethysmogram in treated hypertensive patients: their relationship and associating factors , 2002, Journal of hypertension.

[16]  Federico Sarzotti,et al.  Self-Monitoring of Emotions and Mood Using a Tangible Approach , 2018, Comput..

[17]  O. Wahl Mental health consumers' experience of stigma. , 1999, Schizophrenia bulletin.

[18]  Zengchang Qin,et al.  Emotion Classification with Data Augmentation Using Generative Adversarial Networks , 2018, PAKDD.

[19]  Xu Chen,et al.  MoodExplorer , 2018, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

[20]  Julie M. Skutch,et al.  A pilot study of the DBT coach: an interactive mobile phone application for individuals with borderline personality disorder and substance use disorder. , 2011, Behavior therapy.

[21]  J. V. D. Linden,et al.  A sprite in the dark: supporting conventional mental healthcare practices with a tangible device , 2016 .

[22]  P. Emmelkamp,et al.  Virtual reality exposure therapy in anxiety disorders: a systematic review of process‐and‐outcome studies , 2010, Depression and anxiety.

[23]  Daniela Micucci,et al.  UniMiB AAL: An android sensor data acquisition and labeling suite , 2018 .

[24]  Eman M. G. Younis,et al.  Towards unravelling the relationship between on-body, environmental and emotion data using sensor information fusion approach , 2018, Inf. Fusion.

[25]  K. Fitzpatrick,et al.  Delivering Cognitive Behavior Therapy to Young Adults With Symptoms of Depression and Anxiety Using a Fully Automated Conversational Agent (Woebot): A Randomized Controlled Trial , 2017, JMIR mental health.

[26]  Daniel Roggen,et al.  Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition , 2016, Sensors.

[27]  Maarten De Vos,et al.  Detecting Bipolar Depression From Geographic Location Data , 2016, IEEE Transactions on Biomedical Engineering.

[28]  Simon J. Julier,et al.  DeepBreath: Deep learning of breathing patterns for automatic stress recognition using low-cost thermal imaging in unconstrained settings , 2017, 2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII).

[29]  Eman M. G. Younis,et al.  Deep learning analysis of mobile physiological, environmental and location sensor data for emotion detection , 2019, Inf. Fusion.

[30]  R. Kawashima,et al.  Biofeedback-based training for stress management in daily hassles: an intervention study , 2014, Brain and behavior.

[31]  Albert Rizzo,et al.  Virtual Reality Exposure Therapy for Post-Traumatic Stress Disorder and Other Anxiety Disorders , 2010, Current psychiatry reports.

[32]  Karin Niemantsverdriet,et al.  Interactive jewellery as memory cue , 2016 .

[33]  Bradley E. Belsher,et al.  Prediction Models for Suicide Attempts and Deaths: A Systematic Review and Simulation. , 2019, JAMA psychiatry.

[34]  M. Tsakiris,et al.  The calming effect of a new wearable device during the anticipation of public speech , 2017, Scientific Reports.

[35]  S D Imber,et al.  National Institute of Mental Health Treatment of Depression Collaborative Research Program. General effectiveness of treatments. , 1989, Archives of general psychiatry.

[36]  Kate Dupuis,et al.  Aging Affects Identification of Vocal Emotions in Semantically Neutral Sentences. , 2015, Journal of speech, language, and hearing research : JSLHR.

[37]  Chee Siang Ang,et al.  NotiMind: Utilizing Responses to Smart Phone Notifications as Affective Sensors , 2017, IEEE Access.

[38]  Thanasis Daradoumis,et al.  Emotion Measurement in Intelligent Tutoring Systems: What, When and How to Measure , 2011, 2011 Third International Conference on Intelligent Networking and Collaborative Systems.

[39]  T. Gonzalez,et al.  ICD 10 , 2015, Foot & ankle international.

[40]  M. J. Bell,et al.  Improvements in Stress, Affect, and Irritability Following Brief Use of a Mindfulness-based Smartphone App: A Randomized Controlled Trial , 2018, Mindfulness.

[41]  Chen-Hua Yeow,et al.  A wearable, EEG-based massage headband for anxiety alleviation , 2017, 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[42]  I. Singh,et al.  Can Your Phone Be Your Therapist? Young People’s Ethical Perspectives on the Use of Fully Automated Conversational Agents (Chatbots) in Mental Health Support , 2019, Biomedical informatics insights.

[43]  Tamás D. Gedeon,et al.  Objective measures, sensors and computational techniques for stress recognition and classification: A survey , 2012, Comput. Methods Programs Biomed..

[44]  Akane Sano,et al.  Stress Recognition Using Wearable Sensors and Mobile Phones , 2013, 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction.

[45]  Shrikanth S. Narayanan,et al.  Primitives-based evaluation and estimation of emotions in speech , 2007, Speech Commun..

[46]  Anthony J. Maeder,et al.  A Conversational Agent for an Online Mental Health Intervention , 2016, BIH.

[47]  Reda A. El-Khoribi,et al.  Emotion Recognition based on EEG using LSTM Recurrent Neural Network , 2017 .

[48]  Toshiyo Tamura,et al.  The Advantages of Wearable Green Reflected Photoplethysmography , 2011, Journal of Medical Systems.

[49]  Frank Kirchner,et al.  A Framework for High Performance Embedded Signal Processing and Classification of Psychophysiological Data , 2013 .

[50]  L. Bellina,et al.  Appropriate healthcare technologies for low resource settings: use of m-technology in rural health care and education , 2014 .

[51]  D. Asch,et al.  Facebook language predicts depression in medical records , 2018, Proceedings of the National Academy of Sciences.

[52]  C Koch Good vibes. , 1993, Proceedings of the National Academy of Sciences of the United States of America.

[53]  G. Dunn,et al.  Virtual reality in the treatment of persecutory delusions: randomised controlled experimental study testing how to reduce delusional conviction , 2016, British Journal of Psychiatry.

[54]  Mark B. Powers,et al.  Virtual reality exposure therapy for anxiety disorders: A meta-analysis. , 2008, Journal of anxiety disorders.

[55]  José Manuel Pastor,et al.  Electrodermal Activity Sensor for Classification of Calm/Distress Condition , 2017, Sensors.

[56]  R. Lederman,et al.  Artificial Intelligence-Assisted Online Social Therapy for Youth Mental Health , 2017, Front. Psychol..

[57]  Alexander Travis Adams,et al.  Keppi: A Tangible User Interface for Self-Reporting Pain , 2018, CHI.

[58]  Hazem M. Hajj,et al.  Emotion Recognition from Text Based on Automatically Generated Rules , 2014, 2014 IEEE International Conference on Data Mining Workshop.

[59]  Jessica R. Cauchard,et al.  Remote Biofeedback Sharing, Opportunities and Challenges , 2018, UbiComp/ISWC Adjunct.

[60]  Lijun Yin,et al.  A high-resolution 3D dynamic facial expression database , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.

[61]  Lyle H. Ungar,et al.  Understanding and Measuring Psychological Stress using Social Media , 2018, ICWSM.

[62]  David Coyle,et al.  Design and evaluation guidelines for mental health technologies , 2010, Interact. Comput..

[63]  Terumi Umematsu,et al.  Improving Students' Daily Life Stress Forecasting using LSTM Neural Networks , 2019, 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI).

[64]  Basel Kikhia,et al.  Utilizing a Wristband Sensor to Measure the Stress Level for People with Dementia , 2016, Sensors.

[65]  S. Shiffman,et al.  Ecological momentary assessment. , 2008, Annual review of clinical psychology.

[66]  Friedhelm Schwenker,et al.  A dataset of continuous affect annotations and physiological signals for emotion analysis , 2018, Scientific Data.

[67]  VALENTIN RADU,et al.  Multimodal Deep Learning for Activity and Context Recognition , 2018, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

[68]  Hong-Goo Kang,et al.  A Deep Learning-based Stress Detection Algorithm with Speech Signal , 2018, AVSU@MM.

[69]  Lyle H. Ungar,et al.  What Twitter Profile and Posted Images Reveal About Depression and Anxiety , 2019, ICWSM.

[70]  Tat-Seng Chua,et al.  What Does Social Media Say about Your Stress? , 2016, IJCAI.

[71]  Judith Good,et al.  Exploring affective technologies for the classroom with the subtle stone , 2010, CHI.

[72]  Begoña García Zapirain,et al.  A Stress Sensor Based on Galvanic Skin Response (GSR) Controlled by ZigBee , 2012, Sensors.

[73]  Mehdi Boukhechba,et al.  Vector Space Representation of Bluetooth Encounters for Mental Health Inference , 2018, UbiComp/ISWC Adjunct.

[74]  S. R. Livingstone,et al.  The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English , 2018, PloS one.

[75]  Daniel McDuff,et al.  Advancements in Noncontact, Multiparameter Physiological Measurements Using a Webcam , 2011, IEEE Transactions on Biomedical Engineering.

[76]  M. Keshavan,et al.  Smartphone Ownership and Interest in Mobile Applications to Monitor Symptoms of Mental Health Conditions , 2014, JMIR mHealth and uHealth.

[77]  D. Bainbridge Data protection , 2000 .

[78]  G. Parker,et al.  Mobile mental health: Review of the emerging field and proof of concept study , 2011, Journal of mental health.

[79]  Jennifer Wortman Vaughan Making Better Use of the Crowd: How Crowdsourcing Can Advance Machine Learning Research , 2017, J. Mach. Learn. Res..

[80]  Hao Liu,et al.  Towards mental stress detection using wearable physiological sensors , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[81]  C. Botella,et al.  Virtual reality exposure therapy in anxiety disorders: a quantitative meta‐analysis , 2012, Depression and anxiety.

[82]  Frode Guribye,et al.  Tangible Interaction in the Dentist Office , 2018, TEI.

[83]  José Bravo,et al.  Ambient Intelligence for Health: First International Conference, AmIHEALTH 2015, Puerto Varas, Chile, December 1-4, 2015, Proceedings , 2015, AmIHEALTH.

[84]  J. Os,et al.  Experience sampling research in psychopathology: opening the black box of daily life , 2009, Psychological Medicine.

[85]  J. Boettcher,et al.  Internet-based mindfulness treatment for anxiety disorders: a randomized controlled trial. , 2014, Behavior therapy.

[86]  D. Zatzick,et al.  Assessing Barriers to Care and Readiness for Cognitive Behavioral Therapy in Early Acute Care PTSD Interventions , 2011, Psychiatry.

[87]  Muhammad Abdul-Mageed,et al.  EmoNet: Fine-Grained Emotion Detection with Gated Recurrent Neural Networks , 2017, ACL.

[88]  Cynthia Breazeal,et al.  A Social Robot to Mitigate Stress, Anxiety, and Pain in Hospital Pediatric Care , 2015, HRI.

[89]  Ho-Jin Choi,et al.  The chatbot feels you - a counseling service using emotional response generation , 2017, 2017 IEEE International Conference on Big Data and Smart Computing (BigComp).

[90]  S. Cady,et al.  Massage Therapy as a Workplace Intervention for Reduction of Stress , 1997, Perceptual and motor skills.

[91]  Udo Hahn,et al.  EmoBank: Studying the Impact of Annotation Perspective and Representation Format on Dimensional Emotion Analysis , 2017, EACL.

[92]  Nabile M. Safdar,et al.  Protecting Your Patients' Interests in the Era of Big Data, Artificial Intelligence, and Predictive Analytics. , 2018, Journal of the American College of Radiology : JACR.

[93]  Tian Guo,et al.  Cloud-Based or On-Device: An Empirical Study of Mobile Deep Inference , 2017, 2018 IEEE International Conference on Cloud Engineering (IC2E).

[94]  J. Pirkis,et al.  Service Use for Mental Health Problems: Findings from the 2007 National Survey of Mental Health and Wellbeing , 2009, The Australian and New Zealand journal of psychiatry.

[95]  Matthew M. Graziose On the Accuracy of Self-Report Instruments for Measuring Food Consumption in the School Setting. , 2017, Advances in nutrition.

[96]  D. Bai,et al.  Stress and Heart Rate Variability: A Meta-Analysis and Review of the Literature , 2018, Psychiatry investigation.

[97]  Say Wei Foo,et al.  Speech emotion recognition using hidden Markov models , 2003, Speech Commun..

[98]  M. Hogan,et al.  A randomised active-controlled trial to examine the effects of an online mindfulness intervention on executive control, critical thinking and key thinking dispositions in a university student sample , 2018, BMC psychology.

[99]  M. Tanida,et al.  Relation between mental stress-induced prefrontal cortex activity and skin conditions: A near-infrared spectroscopy study , 2007, Brain Research.

[100]  D. DeSteno,et al.  Mindfulness and Compassion: An Examination of Mechanism and Scalability , 2015, PloS one.

[101]  C. Walters The Psychological and Physiological Effects of Vibrotactile Stimulation, Via a Somatron, on Patients Awaiting Scheduled Gynecological Surgery , 1996 .

[102]  Bao-Liang Lu,et al.  Differential entropy feature for EEG-based emotion classification , 2013, 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER).

[103]  Mohammad Soleymani,et al.  A Multimodal Database for Affect Recognition and Implicit Tagging , 2012, IEEE Transactions on Affective Computing.

[104]  Olivia J. Kirtley,et al.  Experience sampling methodology in mental health research: new insights and technical developments , 2018, World psychiatry : official journal of the World Psychiatric Association.

[105]  Ryan S. McGinnis,et al.  Giving Voice to Vulnerable Children: Machine Learning Analysis of Speech Detects Anxiety and Depression in Early Childhood , 2019, IEEE Journal of Biomedical and Health Informatics.

[106]  Eiman Kanjo,et al.  EmoEcho: A Tangible Interface to Convey and Communicate Emotions , 2018, UbiComp/ISWC Adjunct.

[107]  Ionut Damian,et al.  The SSJ Framework: Augmenting Social Interactions Using Mobile Signal Processing and Live Feedback , 2018, Front. ICT.

[108]  Andrew T. Campbell,et al.  Next-generation psychiatric assessment: Using smartphone sensors to monitor behavior and mental health. , 2015, Psychiatric rehabilitation journal.

[109]  Blay Whitby The Ethical Implications of Non-Human Agency in Health Care Ethical Problems in System-Patient Interaction , 2014 .

[110]  Naoko Kudo,et al.  Heart Rate Variability Biofeedback Intervention for Reduction of Psychological Stress During the Early Postpartum Period , 2014, Applied psychophysiology and biofeedback.

[111]  V. Carr Patients' techniques for coping with schizophrenia: an exploratory study. , 1988, The British journal of medical psychology.

[112]  Ioannis Patras,et al.  AMIGOS: A Dataset for Affect, Personality and Mood Research on Individuals and Groups , 2017, IEEE Transactions on Affective Computing.

[113]  Jae-Young Pyun,et al.  Deep Recurrent Neural Networks for Human Activity Recognition , 2017, Sensors.

[114]  B. Löwe,et al.  An ultra-brief screening scale for anxiety and depression: the PHQ-4. , 2009, Psychosomatics.

[115]  Alex S. Taylor,et al.  Let's Talk About Race: Identity, Chatbots, and AI , 2018, CHI.

[116]  Miguel Bruns Alonso,et al.  Squeeze, rock, and roll; can tangible interaction with affective products support stress reduction? , 2008, TEI.

[117]  D. DeSteno,et al.  Meditation Inhibits Aggressive Responses to Provocations , 2018 .

[118]  Diana Inkpen,et al.  Deep Learning for Depression Detection of Twitter Users , 2018, CLPsych@NAACL-HTL.

[119]  Hong Yan,et al.  A Machine Learning Approach to Improve Contactless Heart Rate Monitoring Using a Webcam , 2014, IEEE Journal of Biomedical and Health Informatics.

[120]  Moin Nadeem,et al.  Identifying Depression on Twitter , 2016, ArXiv.

[121]  K. Cavanagh,et al.  A Randomised Controlled Trial of a Brief Online Mindfulness-Based Intervention in a Non-clinical Population: Replication and Extension , 2018, Mindfulness.

[122]  Aude Billard,et al.  A survey of Tactile Human-Robot Interactions , 2010, Robotics Auton. Syst..

[123]  Christopher M. Danforth,et al.  Instagram photos reveal predictive markers of depression , 2016, EPJ Data Science.

[124]  G. Clifford,et al.  Daily longitudinal self-monitoring of mood variability in bipolar disorder and borderline personality disorder , 2016, Journal of affective disorders.

[125]  T. Mills,et al.  Measuring Health: A Guide to Rating Scales and Questionnaires , 2006 .

[126]  Eiman Kanjo,et al.  A supermarket stress map , 2013, UbiComp.

[127]  John Woods,et al.  Survey on Chatbot Design Techniques in Speech Conversation Systems , 2015 .

[128]  Julius Lester Let's Talk About Race , 2005 .

[129]  A. Stone,et al.  The science of self-report. Implications for research and practice , 1999 .

[130]  Xiangmin Xu,et al.  A novel deep-learning based framework for multi-subject emotion recognition , 2017, 2017 4th International Conference on Information, Cybernetics and Computational Social Systems (ICCSS).

[131]  S. Farooq,et al.  Mobile telephone apps in mental health practice: uses, opportunities and challenges , 2015, BJPsych Bulletin.

[132]  S. Shiffman,et al.  Patient non-compliance with paper diaries , 2002, BMJ : British Medical Journal.

[133]  N. Rüsch,et al.  What is the impact of mental health-related stigma on help-seeking? A systematic review of quantitative and qualitative studies , 2014, Psychological Medicine.

[134]  Paul J. Harrison,et al.  Insomnia, Nightmares, and Chronotype as Markers of Risk for Severe Mental Illness: Results from a Student Population. , 2016, Sleep.

[135]  B. Inkster,et al.  An Empathy-Driven, Conversational Artificial Intelligence Agent (Wysa) for Digital Mental Well-Being: Real-World Data Evaluation Mixed-Methods Study , 2018, JMIR mHealth and uHealth.

[136]  Martin L. Griss,et al.  Activity-Aware Mental Stress Detection Using Physiological Sensors , 2010, MobiCASE.

[137]  T. Vanhala,et al.  Mobile Mental Wellness Training for Stress Management: Feasibility and Design Implications Based on a One-Month Field Study , 2013, JMIR mHealth and uHealth.

[138]  I. Gotlib,et al.  Mood regulation in depression: Differential effects of distraction and recall of happy memories on sad mood. , 2007, Journal of abnormal psychology.

[139]  Eiman Kanjo,et al.  LabelSens: enabling real-time sensor data labelling at the point of collection using an artificial intelligence-based approach , 2020, Personal and Ubiquitous Computing.

[140]  H. Christensen,et al.  Smartphones for Smarter Delivery of Mental Health Programs: A Systematic Review , 2013, Journal of medical Internet research.

[141]  Eman M. G. Younis,et al.  NeuroPlace: Categorizing urban places according to mental states , 2017, PloS one.

[142]  Jennifer Pearson,et al.  Tangible Drops: A Visio-Tactile Display Using Actuated Liquid-Metal Droplets , 2018, CHI.

[143]  Ulrich Kirk,et al.  Online-based Mindfulness Training Reduces Behavioral Markers of Mind Wandering , 2017 .

[144]  Kai Keng Ang,et al.  A Brain-Computer Interface for classifying EEG correlates of chronic mental stress , 2011, The 2011 International Joint Conference on Neural Networks.

[145]  J. Geddes,et al.  Ethical perspectives on recommending digital technology for patients with mental illness , 2017, International Journal of Bipolar Disorders.

[146]  R. Nager,et al.  Skin temperature reveals the intensity of acute stress , 2015, Physiology & Behavior.

[147]  Biserka Radošević-Vidaček Stress at the workplace , 2002 .

[148]  Fernando De la Torre,et al.  Detecting depression from facial actions and vocal prosody , 2009, 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops.

[149]  Li Fei-Fei,et al.  Measuring Depression Symptom Severity from Spoken Language and 3D Facial Expressions , 2018, ArXiv.

[150]  Mark Matthews,et al.  In the mood: engaging teenagers in psychotherapy using mobile phones , 2011, CHI.

[151]  Bo Yu,et al.  Convolutional Neural Networks for human activity recognition using mobile sensors , 2014, 6th International Conference on Mobile Computing, Applications and Services.

[152]  Kaoru Hirota,et al.  Softmax regression based deep sparse autoencoder network for facial emotion recognition in human-robot interaction , 2018, Inf. Sci..

[153]  Thierry Pun,et al.  DEAP: A Database for Emotion Analysis ;Using Physiological Signals , 2012, IEEE Transactions on Affective Computing.

[154]  Hsiu-Sen Chiang,et al.  ECG-based Mental Stress Assessment Using Fuzzy Computing and Associative Petri Net , 2015 .

[155]  Jennifer Healey,et al.  Detecting stress during real-world driving tasks using physiological sensors , 2005, IEEE Transactions on Intelligent Transportation Systems.

[156]  P. Picton,et al.  Heart rate variability biofeedback as a behavioral neurocardiac intervention to enhance vagal heart rate control. , 2005, American heart journal.

[157]  G. Parker,et al.  Community Attitudes to the Appropriation of Mobile Phones for Monitoring and Managing Depression, Anxiety, and Stress , 2010, Journal of medical Internet research.

[158]  Carolina Fuentes,et al.  Helping Elderly Users Report Pain Levels: A Study of User Experience with Mobile and Wearable Interfaces , 2017, Mob. Inf. Syst..

[159]  R. Edelberg,et al.  A plethysmographic method for demonstrating the response specificity of the oral vascular bed. , 1997, Psychophysiology.

[160]  P. Emmelkamp,et al.  Can virtual reality exposure therapy gains be generalized to real-life? A meta-analysis of studies applying behavioral assessments. , 2015, Behaviour research and therapy.

[161]  Chungyoon Chun,et al.  Measurement of occupants' stress based on electroencephalograms (EEG) in twelve combined environments , 2015 .

[162]  Takehiko Yamaguchi,et al.  The Effects of Haptic Feedback and Visual Distraction on Pointing Task Performance , 2016, Int. J. Hum. Comput. Interact..

[163]  Hiroshi Ishii,et al.  Design of haptic interfaces for therapy , 2009, CHI.

[164]  Jun Hu,et al.  BioFidget: Biofeedback for Respiration Training Using an Augmented Fidget Spinner , 2018, CHI.

[165]  Mike Thelwall,et al.  TensiStrength: Stress and relaxation magnitude detection for social media texts , 2016, Inf. Process. Manag..

[166]  Shikha Tripathi,et al.  Real-time emotion recognition from facial images using Raspberry Pi II , 2016, 2016 3rd International Conference on Signal Processing and Integrated Networks (SPIN).

[167]  Susan M. Kaiser,et al.  Development and usability testing of FOCUS: a smartphone system for self-management of schizophrenia. , 2013, Psychiatric rehabilitation journal.

[168]  Ling Chen,et al.  AROMA , 2018, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

[169]  Fan Zhang,et al.  MediaPipe: A Framework for Building Perception Pipelines , 2019, ArXiv.

[170]  Mark Dredze,et al.  Quantifying Mental Health Signals in Twitter , 2014, CLPsych@ACL.

[171]  K. Larkin,et al.  Biofeedback of Heart Rate Variability and Related Physiology: A Critical Review , 2010, Applied psychophysiology and biofeedback.

[172]  Luca Citi,et al.  Nonlinear digital signal processing in mental health: characterization of major depression using instantaneous entropy measures of heartbeat dynamics , 2015, Front. Physiol..

[173]  Gerhard Tröster,et al.  Discriminating Stress From Cognitive Load Using a Wearable EDA Device , 2010, IEEE Transactions on Information Technology in Biomedicine.

[174]  Emily Mower Provost,et al.  Ecologically valid long-term mood monitoring of individuals with bipolar disorder using speech , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[175]  Robin Williams Why is it difficult to achieve e-health systems at scale? , 2016 .

[176]  Alessandra Grassi,et al.  Self-help stress management training through mobile phones: an experience with oncology nurses. , 2013, Psychological services.

[177]  Daniel McDuff,et al.  Remote measurement of cognitive stress via heart rate variability , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[178]  Giuseppe Riva,et al.  The Effects of a Mobile Stress Management Protocol on Nurses Working with Cancer Patients: a Preliminary Controlled Study , 2012, MMVR.

[179]  Stefan Debener,et al.  EEG Recording and Online Signal Processing on Android: A Multiapp Framework for Brain-Computer Interfaces on Smartphone , 2017, BioMed research international.

[180]  Eiman Kanjo,et al.  Things of the Internet (ToI): Physicalization of Notification Data , 2018, UbiComp/ISWC Adjunct.

[181]  Xiaogang Wang,et al.  Deep Learning Face Attributes in the Wild , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).

[182]  D. Swendeman,et al.  Reliability and Validity of Daily Self-Monitoring by Smartphone Application for Health-Related Quality-of-Life, Antiretroviral Adherence, Substance Use, and Sexual Behaviors Among People Living with HIV , 2014, AIDS and Behavior.

[183]  Shrikanth S. Narayanan,et al.  The psychologist as an interlocutor in autism spectrum disorder assessment: insights from a study of spontaneous prosody. , 2014, Journal of speech, language, and hearing research : JSLHR.

[184]  S. Seo,et al.  Stress and EEG , 2010 .

[185]  Nikolaos G. Bourbakis,et al.  A Survey on Wearable Sensor-Based Systems for Health Monitoring and Prognosis , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[186]  Wessel Kraaij,et al.  The SWELL Knowledge Work Dataset for Stress and User Modeling Research , 2014, ICMI.

[187]  E. Scilingo,et al.  Advanced Technology Meets Mental Health: How smartphones, textile electronics, and signal processing can serve mental health monitoring, diagnosis, and treatment. , 2014, IEEE Pulse.

[188]  N. Tarrier,et al.  Virtual reality in mental health , 2007, Social Psychiatry and Psychiatric Epidemiology.

[189]  Eman M. G. Younis,et al.  Designing and evaluating mobile self-reporting techniques: crowdsourcing for citizen science , 2019, Personal and Ubiquitous Computing.

[190]  Bertil Hultén,et al.  The Touch Sense , 2009 .

[191]  Rui Wang,et al.  Tracking Depression Dynamics in College Students Using Mobile Phone and Wearable Sensing , 2018, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

[192]  Munmun De Choudhury,et al.  Modeling and Understanding Visual Attributes of Mental Health Disclosures in Social Media , 2017, CHI.

[193]  Wei Yu,et al.  A Survey of Deep Learning: Platforms, Applications and Emerging Research Trends , 2018, IEEE Access.