Human Behaviour Analysis through Smartphones

Human behaviour analysis through smartphone devices has been an active field for more than a decade and there are still a lot of key aspects to be addressed. This paper surveys the state-of-the-art in human behaviour analysis based on smartphones. We categorise prior works into four main sensing modalities related to physical, cognitive, emotional and social behaviour. Finally, we conclude with the outcomes of this survey and we illustrate our ideas for future research in the area of human behaviour understanding.

[1]  Widyawan,et al.  Smartphone-based Pedestrian Dead Reckoning as an indoor positioning system , 2012, 2012 International Conference on System Engineering and Technology (ICSET).

[2]  Sule Gündüz Ögüdücü,et al.  Identifying topical influencers on twitter based on user behavior and network topology , 2018, Knowl. Based Syst..

[3]  Lauren A. Grieco,et al.  Validation of Physical Activity Tracking via Android Smartphones Compared to ActiGraph Accelerometer: Laboratory-Based and Free-Living Validation Studies , 2015, JMIR mHealth and uHealth.

[4]  Enzo Pasquale Scilingo,et al.  Speech analysis for mood state characterization in bipolar patients , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[5]  John Torous,et al.  New Tools for New Research in Psychiatry: A Scalable and Customizable Platform to Empower Data Driven Smartphone Research , 2016, JMIR mental health.

[6]  D. Mohr,et al.  Harnessing Context Sensing to Develop a Mobile Intervention for Depression , 2011, Journal of medical Internet research.

[7]  Dinesh John,et al.  ActiGraph and Actical physical activity monitors: a peek under the hood. , 2012, Medicine and science in sports and exercise.

[8]  Dong Xuan,et al.  Mobile phone-based pervasive fall detection , 2010, Personal and Ubiquitous Computing.

[9]  W H Brouwer,et al.  The efficacy of SMS text messages to compensate for the effects of cognitive impairments in schizophrenia. , 2010, The British journal of clinical psychology.

[10]  Kamiar Aminian,et al.  Mobile Health Applications to Promote Active and Healthy Ageing , 2017, Sensors.

[11]  J Lomranz,et al.  Indoor and Outdoor Activities of Aged Women and Men as Related to Depression and Well-Being , 1988, International journal of aging & human development.

[12]  M. Karunanithi,et al.  Can a mobile phone be used as a pedometer in an outpatient cardiac rehabilitation program? , 2010, IEEE/ICME International Conference on Complex Medical Engineering.

[13]  Fabio Pianesi,et al.  Happiness Recognition from Mobile Phone Data , 2013, 2013 International Conference on Social Computing.

[14]  Michael Mock,et al.  A step counter service for Java-enabled devices using a built-in accelerometer , 2009, CAMS '09.

[15]  Ignacio Rojas,et al.  Design, implementation and validation of a novel open framework for agile development of mobile health applications , 2015, BioMedical Engineering OnLine.

[16]  Chris D. Nugent,et al.  A Lightweight Hierarchical Activity Recognition Framework Using Smartphone Sensors , 2014, Sensors.

[17]  L. George,et al.  Social Support and the Outcome of Major Depression , 1989, British Journal of Psychiatry.

[18]  Ryosuke Shibasaki,et al.  Activity-Aware Map: Identifying Human Daily Activity Pattern Using Mobile Phone Data , 2010, HBU.

[19]  Syin Chan,et al.  iBEST: Intelligent balance assessment and stability training system using smartphone , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[20]  Sinziana Mazilu,et al.  Online detection of freezing of gait with smartphones and machine learning techniques , 2012, 2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops.

[21]  D. Nieman,et al.  Objective Light-Intensity Physical Activity Associations With Rated Health in Older Adults , 2011 .

[22]  Oscar Mayora-Ibarra,et al.  Monitoring activity of patients with bipolar disorder using smart phones , 2013, MoMM '13.

[23]  Agnes Grünerbl,et al.  Assessing Bipolar Episodes Using Speech Cues Derived from Phone Calls , 2014, MindCare.

[24]  René Meier,et al.  Proceedings of the 1st International Workshop on Context-Aware Middleware and Services: affiliated with the 4th International Conference on Communication System Software and Middleware (COMSWARE 2009) , 2009 .

[25]  M. Rogers,et al.  Validation of measures from the smartphone sway balance application: a pilot study. , 2014, International journal of sports physical therapy.

[26]  Kazuya Okamoto,et al.  Objective assessment of abnormal gait in patients with rheumatoid arthritis using a smartphone , 2012, Rheumatology International.

[27]  Youngnam Han,et al.  SmartPDR: Smartphone-Based Pedestrian Dead Reckoning for Indoor Localization , 2015, IEEE Sensors Journal.

[28]  Gary M. Weiss,et al.  Activity recognition using cell phone accelerometers , 2011, SKDD.

[29]  Hartmut König,et al.  Location-independent fall detection with smartphone , 2013, PETRA '13.

[30]  Stan Kurkovsky,et al.  Automatic Fall Detection Using Mobile Devices , 2015, 2015 12th International Conference on Information Technology - New Generations.

[31]  Lin Sun,et al.  Activity Recognition on an Accelerometer Embedded Mobile Phone with Varying Positions and Orientations , 2010, UIC.

[32]  Lorenzo Chiari,et al.  Validity of a Smartphone-based instrumented Timed Up and Go. , 2012, Gait & posture.

[33]  Guilin Qi,et al.  Detecting bursts in sentiment-aware topics from social media , 2018, Knowl. Based Syst..

[34]  D. Dinges,et al.  Sleep, circadian rhythms, and psychomotor vigilance. , 2005, Clinics in sports medicine.

[35]  Ana M. Bernardos,et al.  Activity logging using lightweight classification techniques in mobile devices , 2012, Personal and Ubiquitous Computing.

[36]  Miwako Doi,et al.  Indoor-outdoor activity recognition by a smartphone , 2012, UbiComp.

[37]  Oscar Mayora-Ibarra,et al.  Mobile phones as medical devices in mental disorder treatment: an overview , 2014, Personal and Ubiquitous Computing.

[38]  Alex Pentland,et al.  Using Social Sensing to Understand the Links between Sleep, Mood, and Sociability , 2011, 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third International Conference on Social Computing.

[39]  Young-Koo Lee,et al.  Comprehensive Context Recognizer Based on Multimodal Sensors in a Smartphone , 2012, Sensors.

[40]  Andrea K. Wittenborn,et al.  #MyDepressionLooksLike: Examining Public Discourse About Depression on Twitter , 2017, JMIR mental health.

[41]  B. Recht,et al.  Well-Being Tracking via Smartphone-Measured Activity and Sleep: Cohort Study , 2017, JMIR mHealth and uHealth.

[42]  Joel J. P. C. Rodrigues,et al.  Mobile-health: A review of current state in 2015 , 2015, J. Biomed. Informatics.

[43]  Adil Mehmood Khan,et al.  Activity Recognition on Smartphones via Sensor-Fusion and KDA-Based SVMs , 2014, Int. J. Distributed Sens. Networks.

[44]  Sungyoung Lee,et al.  Smartphone Location-Independent Physical Activity Recognition Based on Transportation Natural Vibration Analysis , 2017, Sensors.

[45]  Paul Norman,et al.  Health behaviour: Current issues and challenges , 2017, Psychology & health.

[46]  J Brian Rutland,et al.  Development of a Scale to Measure Problem Use of Short Message Service: The SMS Problem Use Diagnostic Questionnaire , 2007, Cyberpsychology Behav. Soc. Netw..

[47]  Matthew Kay,et al.  Cognitive rhythms: unobtrusive and continuous sensing of alertness using a mobile phone , 2016, UbiComp.

[48]  Andrew T. Campbell,et al.  BeWell: Sensing Sleep, Physical Activities and Social Interactions to Promote Wellbeing , 2014, Mobile Networks and Applications.

[49]  Héctor Pomares,et al.  On the Use of Sensor Fusion to Reduce the Impact of Rotational and Additive Noise in Human Activity Recognition , 2012, Sensors.

[50]  J. Pennebaker,et al.  The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods , 2010 .

[51]  D. Dinges An overview of sleepiness and accidents , 1995, Journal of sleep research.

[52]  Sian Lun Lau,et al.  Movement recognition using the accelerometer in smartphones , 2010, 2010 Future Network & Mobile Summit.

[53]  Yuan Zhang,et al.  Pedestrian dead reckoning for MARG navigation using a smartphone , 2014, EURASIP J. Adv. Signal Process..

[54]  Alessio Vecchio,et al.  A smartphone-based fall detection system , 2012, Pervasive Mob. Comput..

[55]  Mark Begale,et al.  A single vs. multi-sensor approach to enhanced detection of smartphone placement , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[56]  David Pastor-Escuredo,et al.  Flooding through the lens of mobile phone activity , 2014, IEEE Global Humanitarian Technology Conference (GHTC 2014).

[57]  T. Isho,et al.  Accelerometry-based gait characteristics evaluated using a smartphone and their association with fall risk in people with chronic stroke. , 2015, Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association.

[58]  Marcos R. Vieira,et al.  Characterizing Dense Urban Areas from Mobile Phone-Call Data: Discovery and Social Dynamics , 2010, 2010 IEEE Second International Conference on Social Computing.

[59]  C. Schmidt,et al.  A time to think: Circadian rhythms in human cognition , 2007, Cognitive neuropsychology.

[60]  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.

[61]  Martin Pielot,et al.  When attention is not scarce - detecting boredom from mobile phone usage , 2015, UbiComp.

[62]  Héctor Pomares,et al.  Dealing with the Effects of Sensor Displacement in Wearable Activity Recognition , 2014, Sensors.

[63]  Aleksandar Matic,et al.  Mobile Network Data for Public Health: Opportunities and Challenges , 2015, Front. Public Health.

[64]  Christian Peter,et al.  Mobile physical activity recognition of stand-up and sit-down transitions for user behavior analysis , 2010, PETRA '10.

[65]  Jeonghee Kim,et al.  Cell phone based balance trainer , 2012, Journal of NeuroEngineering and Rehabilitation.

[66]  Jakob E. Bardram,et al.  Designing mobile health technology for bipolar disorder: a field trial of the monarca system , 2013, CHI.

[67]  Lars Vedel Kessing,et al.  Supporting disease insight through data analysis: refinements of the monarca self-assessment system , 2013, UbiComp.