An m-health application for cerebral stroke detection and monitoring using cloud services

Abstract Over 25 million people suffered from cerebral strokes in a span of 23 years. Many systems are being developed to monitor and improve the life of patients that suffer from different diseases. However, solutions for cerebral strokes are hard to find. Moreover, due to their widespread utilization, smartphones have presented themselves as the most appropriate devices for many e-health systems. In this paper, we propose a cerebral stroke detection solution that employs the cloud to store and analyze data in order to provide statistics to public institutions. Moreover, the prototype of the application is presented. The three most important symptoms of cerebral strokes were considered to develop the tasks that are conducted. Thus, the first task detects smiles, the second task employs voice recognition to determine if a sentence is repeated correctly and, the third task determines if the arms can be raised. Several tests were performed in order to verify the application. Results show its ability to determine whether users have the symptoms of cerebral stroke or not.

[1]  Wan-Young Chung,et al.  WSN based mobile u-healthcare system with ECG, blood pressure measurement function. , 2008, Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference.

[2]  Atif Alamri,et al.  Cloud-Based E-Health Multimedia Framework for Heterogeneous Network , 2012, 2012 IEEE International Conference on Multimedia and Expo Workshops.

[3]  Meikang Qiu,et al.  Health-CPS: Healthcare Cyber-Physical System Assisted by Cloud and Big Data , 2017, IEEE Systems Journal.

[4]  Jaime Lloret Mauri,et al.  A smart communication architecture for ambient assisted living , 2015, IEEE Communications Magazine.

[5]  Jaime Lloret,et al.  Internet of Things for Measuring Human Activities in Ambient Assisted Living and e-Health , 2016, Netw. Protoc. Algorithms.

[6]  Jaime Lloret,et al.  Multimedia sensors embedded in smartphones for ambient assisted living and e-health , 2015, Multimedia Tools and Applications.

[7]  Houbing Song,et al.  Mobile Cloud Computing Model and Big Data Analysis for Healthcare Applications , 2016, IEEE Access.

[8]  Bin Sheng,et al.  Deep gesture interaction for augmented anatomy learning , 2019, Int. J. Inf. Manag..

[9]  Sung Wook Baik,et al.  Mobile-Cloud Assisted Video Summarization Framework for Efficient Management of Remote Sensing Data Generated by Wireless Capsule Sensors , 2014, Sensors.

[10]  Navpreet Kaur,et al.  mSwasthya: A mobile-enabled personal health record management system , 2015, International Conference on Computing, Communication & Automation.

[11]  Vijender Kumar Solanki,et al.  Resource Allocation for Heterogeneous Cloud Computing , 2017, Netw. Protoc. Algorithms.

[12]  Vladimir A. Oleshchuk,et al.  Privacy preserving mechanisms for enforcing security and privacy requirements in E-health solutions , 2016, Int. J. Inf. Manag..

[13]  Shervin Shirmohammadi,et al.  An intelligent cloud-based data processing broker for mobile e-health multimedia applications , 2017, Future Gener. Comput. Syst..

[14]  Song Ci,et al.  A remote markerless human gait tracking for e-healthcare based on content-aware wireless multimedia communications , 2010, IEEE Wireless Communications.

[15]  Jaime Lloret,et al.  Systems and WBANs for Controlling Obesity , 2018, Journal of healthcare engineering.

[16]  Gang Zhou,et al.  Accurate, Fast Fall Detection Using Gyroscopes and Accelerometer-Derived Posture Information , 2009, 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks.

[17]  G. Murray,et al.  Medical complications after stroke: a multicenter study. , 2000, Stroke.

[18]  Jaime Lloret,et al.  An architecture and protocol for smart continuous eHealth monitoring using 5G , 2017, Comput. Networks.

[19]  Emre Yilmaz,et al.  On the Development of an ASR-based Multimedia Game for Speech Therapy: Preliminary Results , 2016, MMHealth@ACM Multimedia.

[20]  Jaime Lloret,et al.  Smart system for children's chronic illness monitoring , 2018, Inf. Fusion.

[21]  Hugo Fuks,et al.  Wearable Computing: Accelerometers' Data Classification of Body Postures and Movements , 2012, SBIA.

[22]  Daniël Lakens,et al.  Using a Smartphone to Measure Heart Rate Changes during Relived Happiness and Anger , 2013, IEEE Transactions on Affective Computing.

[23]  Y. Stern,et al.  Cognitive impairment after stroke: frequency, patterns, and relationship to functional abilities. , 1994, Journal of neurology, neurosurgery, and psychiatry.

[24]  Bhumi Patel,et al.  Human body posture recognition — A survey , 2017, 2017 International Conference on Innovative Mechanisms for Industry Applications (ICIMIA).

[25]  Neetu Sardana,et al.  Hopeful hearts: A mobile health care application , 2014, 2014 Seventh International Conference on Contemporary Computing (IC3).

[26]  T. Samadhi,et al.  Conceptual model for reducing outpatient care waiting times in teaching hospital in Indonesia , 2014, 2014 IEEE International Conference on Management of Innovation and Technology.

[27]  Athanassios Skodras,et al.  On the Comparison of Wearable Sensor Data Fusion to a Single Sensor Machine Learning Technique in Fall Detection , 2018, Sensors.

[28]  Sanath S. Shenoy,et al.  Health cloud - Healthcare as a service(HaaS) , 2014, 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[29]  Dhiya Al-Jumeily,et al.  A mobile health monitoring application for obesity management and control using the internet-of-things , 2016, 2016 Sixth International Conference on Digital Information Processing and Communications (ICDIPC).