A Mobile Cloud Computing Framework Integrating Multilevel Encoding for Performance Monitoring in Telerehabilitation

Recent years have witnessed a surge in telerehabilitation and remote healthcare systems blessed by the emerging low-cost wearable devices to monitor biological and biokinematic aspects of human beings. Although such telerehabilitation systems utilise cloud computing features and provide automatic biofeedback and performance evaluation, there are demands for overall optimisation to enable these systems to operate with low battery consumption and low computational power and even with weak or no network connections. This paper proposes a novel multilevel data encoding scheme satisfying these requirements in mobile cloud computing applications, particularly in the field of telerehabilitation. We introduce architecture for telerehabilitation platform utilising the proposed encoding scheme integrated with various types of sensors. The platform is usable not only for patients to experience telerehabilitation services but also for therapists to acquire essential support from analysis oriented decision support system (AODSS) for more thorough analysis and making further decisions on treatment.

[1]  D. Box,et al.  Simple object access protocol (SOAP) 1.1 , 2000 .

[2]  Katinka Wolter,et al.  Mobile Healthcare Systems with Multi-cloud Offloading , 2013, 2013 IEEE 14th International Conference on Mobile Data Management.

[3]  Robin J. Evans,et al.  Noncontact Detection and Analysis of Respiratory Function Using Microwave Doppler Radar , 2015, J. Sensors.

[5]  Grigore C. Burdea,et al.  A virtual-reality-based telerehabilitation system with force feedback , 2000, IEEE Transactions on Information Technology in Biomedicine.

[6]  Riccardo Magni,et al.  Tele-rehabilitation: present and future. , 2008, Annali dell'Istituto superiore di sanita.

[7]  Yee Siong Lee,et al.  Monitoring and Analysis of Respiratory Patterns Using Microwave Doppler Radar , 2014, IEEE Journal of Translational Engineering in Health and Medicine.

[8]  F. Nouri,et al.  The effectiveness of EMG biofeedback in the treatment of arm function after stroke. , 1989, International disability studies.

[9]  Chonho Lee,et al.  A survey of mobile cloud computing: architecture, applications, and approaches , 2013, Wirel. Commun. Mob. Comput..

[10]  C. Koh,et al.  Systematic review of randomized controlled trials of the effectiveness of biofeedback for pelvic floor dysfunction , 2008, The British journal of surgery.

[11]  Bharat K. Bhargava,et al.  A Survey of Computation Offloading for Mobile Systems , 2012, Mobile Networks and Applications.

[12]  何晨光,et al.  Toward Ubiquitous Healthcare Services with A Novel Efficient Cloud Platform , 2012 .

[13]  S. Dimmick,et al.  A Case Study of Benefits & Potential Savings in Rural Home Telemedicine , 2000, Home healthcare nurse.

[14]  Samitha W Ekanayake,et al.  BioKin: an ambulatory platform for gait kinematic and feature assessment. , 2015, Healthcare technology letters.

[15]  Oonagh M. Giggins,et al.  Biofeedback in rehabilitation , 2013, Journal of NeuroEngineering and Rehabilitation.

[16]  Klaus Moessner,et al.  Providing SOAP Web Services and RESTful Web Services from Mobile Hosts , 2010, 2010 Fifth International Conference on Internet and Web Applications and Services.

[17]  Pubudu N. Pathirana,et al.  A Kinematic Based Evaluation of Upper Extremity Movement Smoothness for Tele-Rehabilitation , 2015, ICOST.

[18]  E. K. Park,et al.  e-Healthcare Security Solution Framework , 2012, 2012 21st International Conference on Computer Communications and Networks (ICCCN).

[19]  W. Liu,et al.  e-Healthcare cloud computing application solutions: Cloud-enabling characteristices, challenges and adaptations , 2013, 2013 International Conference on Computing, Networking and Communications (ICNC).

[20]  Chunming Gao,et al.  CloudHealth: Developing a reliable cloud platform for healthcare applications , 2013, 2013 IEEE 10th Consumer Communications and Networking Conference (CCNC).

[21]  Nabil Ahmed Sultan,et al.  Making use of cloud computing for healthcare provision: Opportunities and challenges , 2014, Int. J. Inf. Manag..

[22]  Carlo Petrini,et al.  Informed consent in experimentation involving mentally impaired persons: ethical issues. , 2010, Annali dell'Istituto superiore di sanita.

[23]  Robert Richards,et al.  Representational State Transfer (REST) , 2006 .

[24]  Puja Padiya,et al.  Web Services Based On SOAP and REST Principles , 2013 .

[25]  P. Lehoux,et al.  A systematic review of clinical outcomes, clinical process, healthcare utilization and costs associated with telerehabilitation , 2009, Disability and rehabilitation.

[26]  Tuck-Voon How,et al.  Co-design of cognitive telerehabilitation technologies , 2014, MobileHCI '14.

[28]  Dursun Delen,et al.  Leveraging the capabilities of service-oriented decision support systems: Putting analytics and big data in cloud , 2013, Decis. Support Syst..

[29]  D.J. Reinkensmeyer,et al.  Web-based telerehabilitation for the upper extremity after stroke , 2002, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[30]  Wei Cheng,et al.  Enabling smartphone-based HD video chats by cooperative transmissions in CRNs , 2015, EURASIP J. Wirel. Commun. Netw..

[31]  Hermano I Krebs,et al.  Telerehabilitation robotics: bright lights, big future? , 2006, Journal of rehabilitation research and development.

[32]  Terry Caelli,et al.  A Syntactic Two-Component Encoding Model for the Trajectories of Human Actions , 2014, IEEE Journal of Biomedical and Health Informatics.

[33]  Richard Wootton,et al.  The diagnostic reliability of Internet-based observational kinematic gait analysis , 2003, Journal of telemedicine and telecare.

[34]  S. Harris,et al.  A systematic review of the effectiveness of exercise, manual therapy, electrotherapy, relaxation training, and biofeedback in the management of temporomandibular disorder. , 2006, Physical therapy.

[35]  Micha L. Post,et al.  TeleRehab: Stroke Teletherapy and Management Using Two‐Way Interactive Video , 2002 .

[36]  Yonggwan Won,et al.  3D motion matching algorithm using signature feature descriptor , 2014, Multimedia Tools and Applications.

[37]  Yung-Hsiang Lu,et al.  Cloud Computing for Mobile Users: Can Offloading Computation Save Energy? , 2010, Computer.

[38]  Lili Liu,et al.  Telerehabilitation at the University of Alberta , 2000, Journal of Telemedicine and Telecare.

[39]  Julie Maitland,et al.  Pervasive healthcare: from orange alerts to mindcare , 2011, SIGH.

[40]  M. Grigioni,et al.  Ten years of telerehabilitation: A literature overview of technologies and clinical applications. , 2010, NeuroRehabilitation.

[41]  Pubudu N. Pathirana,et al.  Remote Monitoring System Enabling Cloud Technology upon Smart Phones and Inertial Sensors for Human Kinematics , 2014, 2014 IEEE Fourth International Conference on Big Data and Cloud Computing.

[42]  Ferdinand Fuhrmann,et al.  EVALUATION OF THE SPATIAL RESOLUTION ACCURACY OF THE FACE TRACKING SYSTEM FOR KINECT FOR WINDOWS V 1 AND V 2 , 2014 .

[43]  Ching-Seh Wu,et al.  E-Healthcare Web Service Broker Infrastructure in Cloud Environment , 2012, 2012 IEEE Eighth World Congress on Services.

[44]  Chandra Krintz,et al.  Using bandwidth data to make computation offloading decisions , 2008, 2008 IEEE International Symposium on Parallel and Distributed Processing.

[45]  M. Holden,et al.  Telerehabilitation Using a Virtual Environment Improves Upper Extremity Function in Patients With Stroke , 2007, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[46]  Josune Hernantes,et al.  Service-Oriented Architecture and Legacy Systems , 2014, IEEE Software.