Design, implementation and validation of a novel open framework for agile development of mobile health applications

The delivery of healthcare services has experienced tremendous changes during the last years. Mobile health or mHealth is a key engine of advance in the forefront of this revolution. Although there exists a growing development of mobile health applications, there is a lack of tools specifically devised for their implementation. This work presents mHealthDroid, an open source Android implementation of a mHealth Framework designed to facilitate the rapid and easy development of mHealth and biomedical apps. The framework is particularly planned to leverage the potential of mobile devices such as smartphones or tablets, wearable sensors and portable biomedical systems. These devices are increasingly used for the monitoring and delivery of personal health care and wellbeing. The framework implements several functionalities to support resource and communication abstraction, biomedical data acquisition, health knowledge extraction, persistent data storage, adaptive visualization, system management and value-added services such as intelligent alerts, recommendations and guidelines. An exemplary application is also presented along this work to demonstrate the potential of mHealthDroid. This app is used to investigate on the analysis of human behavior, which is considered to be one of the most prominent areas in mHealth. An accurate activity recognition model is developed and successfully validated in both offline and online conditions.

[1]  Edward Sazonov,et al.  RF hand gesture sensor for monitoring of cigarette smoking , 2011, 2011 Fifth International Conference on Sensing Technology.

[2]  Guy Lapalme,et al.  A systematic analysis of performance measures for classification tasks , 2009, Inf. Process. Manag..

[3]  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.

[4]  Michael R. Neuman,et al.  Automatic Detection of Swallowing Events by Acoustical Means for Applications of Monitoring of Ingestive Behavior , 2010, IEEE Transactions on Biomedical Engineering.

[5]  Sylvain Arlot,et al.  A survey of cross-validation procedures for model selection , 2009, 0907.4728.

[6]  Stefanie Havelka Mobile Resources for Nursing Students and Nursing Faculty , 2011 .

[7]  Fatimah Ibrahim,et al.  Smartphone-Based Solutions for Fall Detection and Prevention: Challenges and Open Issues , 2014, Sensors.

[8]  S. Cerutti,et al.  Barometric Pressure and Triaxial Accelerometry-Based Falls Event Detection , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[9]  Tim Bray,et al.  Internet Engineering Task Force (ietf) the Javascript Object Notation (json) Data Interchange Format , 2022 .

[10]  Mitja Lustrek,et al.  Fall Detection and Activity Recognition with Machine Learning , 2009, Informatica.

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

[12]  HungMing Chen,et al.  Framework Design-Integrating an Android Open Platform with Multiinterface Biomedical Modules for Physiological Measurement , 2012 .

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

[14]  C DinizPedro,et al.  Preprocessing techniques for context recognition from accelerometer data , 2010 .

[15]  M. Moy,et al.  Using Wearable Sensors to Monitor Physical Activities of Patients with COPD: A Comparison of Classifier Performance , 2009, 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks.

[16]  S. Snelgrove,et al.  Medication Monitoring for People with Dementia in Care Homes: The Feasibility and Clinical Impact of Nurse-Led Monitoring , 2014, TheScientificWorldJournal.

[17]  Radford M. Neal Pattern Recognition and Machine Learning , 2007, Technometrics.

[18]  Diogo R. Ferreira,et al.  Preprocessing techniques for context recognition from accelerometer data , 2010, Personal and Ubiquitous Computing.

[19]  Bernt Schiele,et al.  A tutorial on human activity recognition using body-worn inertial sensors , 2014, CSUR.

[20]  Johannes Peltola,et al.  Activity classification using realistic data from wearable sensors , 2006, IEEE Transactions on Information Technology in Biomedicine.

[21]  Toshiyo Tamura,et al.  A Wearable Airbag to Prevent Fall Injuries , 2009, IEEE Transactions on Information Technology in Biomedicine.

[22]  Daniel P. Siewiorek,et al.  Activity recognition and monitoring using multiple sensors on different body positions , 2006, International Workshop on Wearable and Implantable Body Sensor Networks (BSN'06).

[23]  Andrea Gaggioli,et al.  A mobile data collection platform for mental health research , 2013, Personal and Ubiquitous Computing.

[24]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[25]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[26]  黄亚明 MedScape , 2009 .

[27]  Héctor Pomares,et al.  Human activity recognition based on a sensor weighting hierarchical classifier , 2013, Soft Comput..

[28]  Roozbeh Jafari,et al.  Enabling Effective Programming and Flexible Management of Efficient Body Sensor Network Applications , 2013, IEEE Transactions on Human-Machine Systems.

[29]  M. Stone Asymptotics for and against cross-validation , 1977 .

[30]  Miguel A. Labrador,et al.  A Survey on Human Activity Recognition using Wearable Sensors , 2013, IEEE Communications Surveys & Tutorials.

[31]  V. Barnett,et al.  Applied Linear Statistical Models , 1975 .

[32]  Ling Bao,et al.  Activity Recognition from User-Annotated Acceleration Data , 2004, Pervasive.

[33]  Shwetak N. Patel,et al.  Implementing technology-based embedded assessment in the home and community life of individuals aging with disabilities: a participatory research and development study , 2014, Disability and rehabilitation. Assistive technology.

[34]  Jesse Hoey,et al.  Sensor-Based Activity Recognition , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[35]  Alberto Maria Segre,et al.  Programs for Machine Learning , 1994 .

[36]  Ignacio Rojas,et al.  PhysioDroid: Combining Wearable Health Sensors and Mobile Devices for a Ubiquitous, Continuous, and Personal Monitoring , 2014, TheScientificWorldJournal.

[37]  Adrian Burns,et al.  SHIMMER™ – A Wireless Sensor Platform for Noninvasive Biomedical Research , 2010, IEEE Sensors Journal.

[38]  Héctor Pomares,et al.  Window Size Impact in Human Activity Recognition , 2014, Sensors.

[39]  Silvia Lindtner,et al.  Fish'n'Steps: Encouraging Physical Activity with an Interactive Computer Game , 2006, UbiComp.

[40]  Jun Cheng,et al.  A Wearable Smartphone-Based Platform for Real-Time Cardiovascular Disease Detection Via Electrocardiogram Processing , 2010, IEEE Transactions on Information Technology in Biomedicine.

[41]  Paul Lukowicz,et al.  CRNTC+: A smartphone-based sensor processing framework for prototyping personal healthcare applications , 2013, 2013 7th International Conference on Pervasive Computing Technologies for Healthcare and Workshops.

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

[43]  Hlaing Minn,et al.  Apnea MedAssist: Real-time Sleep Apnea Monitor Using Single-Lead ECG , 2011, IEEE Transactions on Information Technology in Biomedicine.

[44]  Colin J. Ihrig JavaScript Object Notation , 2013 .

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

[46]  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.

[47]  Kolin Paul,et al.  Provenance framework for mHealth , 2013, 2013 Fifth International Conference on Communication Systems and Networks (COMSNETS).

[48]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[49]  Ching Y. Suen,et al.  Application of majority voting to pattern recognition: an analysis of its behavior and performance , 1997, IEEE Trans. Syst. Man Cybern. Part A.

[50]  K. Shadan,et al.  Available online: , 2012 .

[51]  Qiao Li,et al.  Open source Java-based ECG analysis software and Android app for Atrial Fibrillation screening , 2013, Computing in Cardiology 2013.

[52]  Salima Benbernou,et al.  A survey on service quality description , 2013, CSUR.