m-Health: Lessons Learned by m-Experiences

m-Health is an emerging area that is transforming how people take part in the control of their wellness condition. This vision is changing traditional health processes by discharging hospitals from the care of people. Important advantages of continuous monitoring can be reached but, in order to transform this vision into a reality, some factors need to be addressed. m-Health applications should be shared by patients and hospital staff to perform proper supervised health monitoring. Furthermore, the uses of smartphones for health purposes should be transformed to achieve the objectives of this vision. In this work, we analyze the m-Health features and lessons learned by the experiences of systems developed by MAmI Research Lab. We have focused on three main aspects: m-interaction, use of frameworks, and physical activity recognition. For the analysis of the previous aspects, we have developed some approaches to: (1) efficiently manage patient medical records for nursing and healthcare environments by introducing the NFC technology; (2) a framework to monitor vital signs, obesity and overweight levels, rehabilitation and frailty aspects by means of accelerometer-enabled smartphones and, finally; (3) a solution to analyze daily gait activity in the elderly, carrying a single inertial wearable close to the first thoracic vertebra.

[1]  Cathryn Jackson,et al.  A mobile clinical e-portfolio for nursing and medical students, using wireless personal digital assistants (PDAs). , 2006, Nurse education in practice.

[2]  José Miguel Latorre,et al.  Correlation between videogame mechanics and executive functions through EEG analysis , 2016, J. Biomed. Informatics.

[3]  Shuozhi Yang,et al.  Estimation of spatio-temporal parameters for post-stroke hemiparetic gait using inertial sensors. , 2013, Gait & posture.

[4]  Stefan Madansingh,et al.  Smartphone based fall detection system , 2015, 2015 15th International Conference on Control, Automation and Systems (ICCAS).

[5]  Diane J. Cook,et al.  Simple and Complex Activity Recognition through Smart Phones , 2012, 2012 Eighth International Conference on Intelligent Environments.

[6]  Arantza Illarramendi,et al.  Real-time classification of ECGs on a PDA , 2005, IEEE Transactions on Information Technology in Biomedicine.

[7]  G. Zilvold,et al.  Simultaneous measurement of surface EMG and movements for clinical use , 1989, Medical and Biological Engineering and Computing.

[8]  Sooyoung Yoo,et al.  MobileMed: A PDA-Based Mobile Clinical Information System , 2006, IEEE Transactions on Information Technology in Biomedicine.

[9]  Vladimir Villarreal,et al.  An NFC Approach for Nursing Care Training , 2011, 2011 Third International Workshop on Near Field Communication.

[10]  Davide Anguita,et al.  Human Activity Recognition on Smartphones Using a Multiclass Hardware-Friendly Support Vector Machine , 2012, IWAAL.

[11]  Carmen C. Y. Poon,et al.  Perspectives on High Technologies for Low-Cost Healthcare , 2008, IEEE Engineering in Medicine and Biology Magazine.

[12]  Emil Jovanov,et al.  Stress monitoring using a distributed wireless intelligent sensor system. , 2003, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

[13]  K. Patrick,et al.  A Text Message–Based Intervention for Weight Loss: Randomized Controlled Trial , 2009, Journal of medical Internet research.

[14]  M. Tinetti Performance‐Oriented Assessment of Mobility Problems in Elderly Patients , 1986, Journal of the American Geriatrics Society.

[15]  M. Vergara,et al.  Mobile Prescription: An NFC-Based Proposal for AAL , 2010, 2010 Second International Workshop on Near Field Communication.

[16]  Jesús Fontecha,et al.  A Mobile and Ubiquitous Approach for Supporting Frailty Assessment in Elderly People , 2013, Journal of medical Internet research.

[17]  Jesús Favela,et al.  Assessing empathy and managing emotions through interactions with an affective avatar , 2018, Health Informatics J..

[18]  Rafael C González,et al.  Real-time gait event detection for normal subjects from lower trunk accelerations. , 2010, Gait & posture.

[19]  William C. Mann,et al.  Smart Phone Based Cognitive Assistant , 2003 .

[20]  Dong Xuan,et al.  PerFallD: A pervasive fall detection system using mobile phones , 2010, 2010 8th IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops).

[21]  James A. Landay,et al.  The Mobile Sensing Platform: An Embedded Activity Recognition System , 2008, IEEE Pervasive Computing.

[22]  Vincenzo Della Mea,et al.  What is e-Health (2): The death of telemedicine? , 2001 .

[23]  Emiliano Miluzzo,et al.  A survey of mobile phone sensing , 2010, IEEE Communications Magazine.

[24]  Martínez Manuel Quintanilla,et al.  [Frail elderly]. , 2004, Revista de enfermeria.

[25]  R. Hawkins,et al.  Evaluation of Roche Accu-Chek Go and Medisense Optium blood glucose meters. , 2005, Clinica chimica acta; international journal of clinical chemistry.

[26]  Valérie Gay,et al.  A mobile rehabilitation application for the remote monitoring of cardiac patients after a heart attack or a coronary bypass surgery , 2009, PETRA '09.

[27]  Lale Akarun,et al.  A Smartphone Based Fall Detector with Online Location Support , 2010 .

[28]  Yang Yong-jun IT-MEMS technology , 2002 .

[29]  O. Beauchet,et al.  Gait variability is associated with frailty in community-dwelling older adults. , 2011, The journals of gerontology. Series A, Biological sciences and medical sciences.

[30]  Robert S. H. Istepanian,et al.  Ubiquitous M-Health Systems and the Convergence Towards 4G Mobile Technologies , 2006 .

[31]  Guy Albert Dumont,et al.  Experience report: functional programming of mHealth applications , 2013, ICFP.

[32]  Jesús Fontecha,et al.  Mobile and ubiquitous architecture for the medical control of chronic diseases through the use of intelligent devices: Using the architecture for patients with diabetes , 2014, Future Gener. Comput. Syst..

[33]  José Bravo,et al.  m-Physio: Personalized Accelerometer-based Physical Rehabilitation Platform , 2010 .

[34]  Nancy Longnecker,et al.  Doctor-patient communication: a review. , 2010, The Ochsner journal.

[35]  Michael L. Littman,et al.  Activity Recognition from Accelerometer Data , 2005, AAAI.

[36]  Jesús Fontecha,et al.  Mobile services infrastructure for frailty diagnosis support based on Gower's similarity coefficient and treemaps , 2014, Mob. Inf. Syst..

[37]  Diego López-de-Ipiña,et al.  Enabling NFC Technology for Supporting Chronic Diseases: A Proposal for Alzheimer Caregivers , 2008, AmI.

[38]  Ahmad Zmily,et al.  Alzheimer's Disease rehabilitation using smartphones to improve patients' quality of life , 2013, 2013 7th International Conference on Pervasive Computing Technologies for Healthcare and Workshops.

[39]  T. Wyatt,et al.  Smartphones in Nursing Education , 2011, Computers, informatics, nursing : CIN.

[40]  Adrian Holzer,et al.  Mobile application market: A developer's perspective , 2011, Telematics Informatics.

[41]  Jesús Fontecha,et al.  A Context Model based on Ontological Languages: a Proposal for Information Visualization , 2010, J. Univers. Comput. Sci..

[42]  Feng Xia,et al.  iCare: A Mobile Health Monitoring System for the Elderly , 2010, 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing.

[43]  B. Dobkin,et al.  Reliability and Validity of Bilateral Ankle Accelerometer Algorithms for Activity Recognition and Walking Speed After Stroke , 2011, Stroke.

[44]  Kristina Höök,et al.  License to chill!: how to empower users to cope with stress , 2008, NordiCHI.

[45]  José Bravo,et al.  COIVA: context‐aware and ontology‐powered information visualization architecture , 2011, Softw. Pract. Exp..

[46]  Iván González,et al.  Estimation of Temporal Gait Events from a Single Accelerometer Through the Scale-Space Filtering Idea , 2016, Journal of Medical Systems.

[47]  Patricia Biller Krauskopf,et al.  E-health and Nursing: Using Smartphones to Enhance Nursing Practice , 2012 .

[48]  S. Sharma,et al.  A strategic approach to m-health , 2009, Health Informatics J..

[49]  Rachid Benlamri,et al.  MORF: A Mobile Health-Monitoring Platform , 2010, IT Professional.

[50]  Olivier Beauchet,et al.  Association of increased gait variability while dual tasking and cognitive decline: results from a prospective longitudinal cohort pilot study , 2017, GeroScience.

[51]  Paul Lukowicz,et al.  Wearable Sensing to Annotate Meeting Recordings , 2002, SEMWEB.

[52]  N. Vodjdani,et al.  The ambient assisted living joint programme , 2008, 2008 2nd Electronics System-Integration Technology Conference.

[53]  Richard Oehler,et al.  Surfing the web: practicing medicine in a technological age: using smartphones in clinical practice. , 2008, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[54]  Iván González,et al.  Relationship between stride interval variability and aging: use of linear and non-linear estimators for gait variability assessment in assisted living environments , 2019, J. Ambient Intell. Humaniz. Comput..

[55]  Deborah Estrin,et al.  PEIR, the personal environmental impact report, as a platform for participatory sensing systems research , 2009, MobiSys '09.

[56]  Jesús Fontecha,et al.  An Assistive Navigation System Based on Augmented Reality and Context Awareness for People With Mild Cognitive Impairments , 2014, IEEE Journal of Biomedical and Health Informatics.

[57]  Jiannong Cao,et al.  WAITER: A Wearable Personal Healthcare and Emergency Aid System , 2008, 2008 Sixth Annual IEEE International Conference on Pervasive Computing and Communications (PerCom).

[58]  Eric C. Larson,et al.  SpiroSmart: using a microphone to measure lung function on a mobile phone , 2012, UbiComp.

[59]  Swamy Laxminarayan,et al.  UNWIRED E-MED: the next generation of wireless and internet telemedicine systems , 2000, IEEE Transactions on Information Technology in Biomedicine.

[60]  Emil Jovanov,et al.  Guest Editorial Introduction to the Special Section on M-Health: Beyond Seamless Mobility and Global Wireless Health-Care Connectivity , 2004, IEEE Transactions on Information Technology in Biomedicine.

[61]  R. Istepanian,et al.  Mobile e-health: the unwired evolution of telemedicine. , 2003, Telemedicine journal and e-health : the official journal of the American Telemedicine Association.

[62]  Andrew T. Campbell,et al.  Bewell: A smartphone application to monitor, model and promote wellbeing , 2011, PervasiveHealth 2011.

[63]  Angélica Muñoz-Meléndez,et al.  Comparison of a Vision-Based System and a Wearable Inertial-Based System for a Quantitative Analysis and Calculation of Spatio-Temporal Parameters , 2015, AmIHEALTH.

[64]  Russell A. McCann,et al.  mHealth for mental health: Integrating smartphone technology in behavioral healthcare. , 2011 .

[65]  José Bravo,et al.  Using a Communication Model to Collect Measurement Data through Mobile Devices , 2012, Sensors.

[66]  Yoshiharu Yonezawa,et al.  A daily living activity remote monitoring system for solitary elderly people , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[67]  David W. McDonald,et al.  Activity sensing in the wild: a field trial of ubifit garden , 2008, CHI.

[68]  Marios S. Pattichis,et al.  Wireless telemedicine systems: an overview , 2002 .

[69]  Diego López-de-Ipiña,et al.  Integration of Multisensor Hybrid Reasoners to Support Personal Autonomy in the Smart Home , 2014, Sensors.

[70]  Friedrich Foerster,et al.  Motion pattern and posture: Correctly assessed by calibrated accelerometers , 2000, Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc.

[71]  Michel RIVEILL SOAP , 2015, Technologies logicielles Architectures des systèmes.