Big Data Fusion for eHealth and Ambient Assisted Living Cloud Applications
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The document describes a cloud-based system for monitoring senior citizens' healthcare using data from heterogeneous sensors. It discusses existing ambient assisted living solutions, challenges with data fusion from multiple sources, and proposed hardware and software components. The system would gather data from various physiological, environmental, and lifestyle sensors. Software components include a big data fusion platform running on a distributed cloud architecture. The goal is to enable applications for monitoring health conditions and daily activities.
Big Data Fusion for eHealth and Ambient Assisted Living Cloud Applications
- 1. Big Data Fusion for eHealth and Ambient Assisted Living Cloud Applications • George Suciu, Alexandru Vulpe and Razvan Craciunescu Faculty of Electronics, Telecommunications and Information Technology, University POLITEHNICA of Bucharest • Cristina Butca and Victor Suciu R&D Department, Beia Consult International Constanta, Romania, 2015
- 2. Content Short Biography Introduction State-of-the-art : Virtual Collaboration Spaces Proposed Cloud Acceleration Platform For Innovation In Industry Conclusions and Discussions A. Vulpe, et.al. (2015)
- 3. Short Biography (1) Graduated from the Faculty of Electronics, Telecommunications and Information Technology at the University “Politehnica” of Bucharest (UPB), Romania (www.upb.ro) Currently, Ph.D. Eng. Post-doc Researcher focused on the field………Alex Vulpe….. A. Vulpe, et.al. (2015)
- 4. Short Biography (2) Projects – www.beiaro.eu / www.mobcomm.pub.ro FP7 (2 on-going) REDICT : Regional Economic Development by ICT eWALL : Electronic Wall for Active Long Living Cloud Consulting : Cloud-based Automation of ERP and CRM software for Small Businesses ACCELERATE: A Platform for the Acceleration of go-to market in the ICT industry H2020 (1 on-going) SWITCH: Software Workbench for Interactive, Time Critical and Highly self-adaptive Cloud applications (ICT-9) National (more than 10 past projects, 5 on-going) MobiWay: Mobility Beyond Individualism: an Integrated Platform for Intelligent Transportation Systems of Tomorrow EV-BAT: Redox battery with fast charging capacity as a main source of energy for electric autovehicles CarbaDetect: Imuno-biosensors for fast detection of carbamic pesticide residues (carbaryl, carbendazim) in horticultural products SARAT-IWSN : Scalable Radio Transceiver for Instrumental Wireless Sensor Networks COMM-CENTER : Developing of a “cloud communication center" by integrating a call/contact center platform with unified communication technology, CRM system, “text-to-speech” and “automatic speech recognition” solutions in different languages (including Romanian) A. Vulpe, et.al. (2015)
- 5. Introduction (1) We describe a cloud-based approach for monitoring the healthcare condition of senior citizens and the fusion of big data from heterogeneous information flows coming from the sensors. Solutions for Ambient Assisted Living Cloud Applications exists already and under development: eCAALYX - the monitoring system is implemented both inside and outside the home through three main subsystems: the Mobile Monitoring System; the Home Monitoring System and the Caretaker Site. Persona - solution is very complex, with a sophisticated and up–to–date software architecture implemented in the house. eWALL - system has an architecture based on two main blocks: the sensing environment and the eWALL cloud. The sensing environment is linked to the local processing unit using a local gateway. The connection between the sensing environment and the eWALL cloud is done via a cloud proxy. This component collects all the data from the sensing environment and sends it to the cloud processing components. A. Vulpe, et.al. (2015)
- 6. DATA SOURCES AND DATA FUSION (1) 1. Data sources Data sources can be in home metadata and external data sources. In home metadata are generated by applying perceptual components on the signals from diverse sensors and are temporarily stored in one database. The in home metadata may span the following categories: person - location, gender and age; environment - humidity, illumination, temperature; steps of the person monitored; communication - Usage of phone, messaging services, social media; sound - level, angle of arrival, speaker analytics; sleep - Bed pressure & acceleration, sleep sounds. External data sources can be social networks like Facebook, Twitter and LinkedIn, entertainment and gaming sources such as YouTube, Video on Demand (VOD) and Audio and Video on Demand (AVOD), games and gaming platforms. A. Vulpe, et.al. (2015)
- 7. DATA SOURCES AND DATA FUSION (2) 2. Data fusion challenges A single source it is not sufficient to detect a situation person - location, gender and age Combining audio and visual localization, an audiovisual localization system can be: Accurate, since now the location comes from two signals; Persistent, since a person can continue being localized under adverse visual conditions (occlusions) or audio conditions (noise, absence of speech. Early fusion refers to the combination of the signals from different sources, combining the unprocessed signals. Late fusion is done when each data source is used independently to estimate the state. A. Vulpe, et.al. (2015)
- 8. DATA SOURCES AND DATA FUSION (3) 3. Fusion methods Fusion of imperfect data Bayesian estimator is one of the probabilistic methods, easy implementation and optimality in a mean-squared error sense. Monte Carlo method for fusion of imperfect data. This is very flexible because it doesn't make any assumptions regarding the probability densities to be approximated. Fusion of correlated data It is possible to design a fusion algorithm that takes into account correlated data, and Covariance Intersection (CI). The problem solving is made by formulation of an estimate of the covariance matrix as a convex combination of the means and covariances of the input data. One of the methods for fusion of correlated data is the Largest Ellipsoid (LE) algorithm that is an alternative to CI and provides a tighter estimate of covariance matrix by finding the largest ellipse that fits within the intersection region of the input covariances. A. Vulpe, et.al. (2015)
- 9. COMPONENTS OF THE SYSTEM (1) We describe the hardware and software components of the proposed solution for big data gathering, processing and fusion. A. Vulpe, et.al. (2015)
- 10. COMPONENTS OF THE SYSTEM (2) 1. Hardware components (1) The healthcare condition of the patient can be monitored through various sensors, even by using traditional home automation systems. Our proposed sensor devices measure: pulse rate, ECG, body core temperature, breathing rate, blood pressure, oxygen saturation, glucose to home safety and smart home sensors and actuators that measure light, ambient humidity, CO2 level, facial expression, presence of other people motion and lifestyle sensors. A. Vulpe, et.al. (2015)
- 11. COMPONENTS OF THE SYSTEM (3) 1. Hardware components (2) The TMP 102 temperature sensor is used for retrieving body temperature for the person who wears it. The sensor for ECG has a 1 inch diameter enclosure and is the one who combines amplification, bandpass filtering and analog-to-digital conversion. The pulse sensor is used for heart rate measurement for detection of human heart rate. The Sensor for CO2 level detection can detect a concentration between 20 and 2000 ppm. For temperature and humidity of the air it will be used a DHT11 sensor. This sensor uses a thermistor to measure the surrounding air and then returns a digital signal on the data pin. For motion detection will be used a PIR sensor. A. Vulpe, et.al. (2015)
- 12. COMPONENTS OF THE SYSTEM (4) 2. Cloud software components (1) The system is based on distributed cloud computing software SlapOS. The software components of the cloud platform are hosted on several server nodes following an architecture based on the concept of Master and Slave nodes Master nodes act as a central marketplace of the cloud system Slave nodes report their availability and costs of resources. The software components that need to be installed on Slave nodes are Slapgrid and Supervisord. To understand the full extent of a person movement, multiple sensors sending data about his activity are needed. A. Vulpe, et.al. (2015)
- 13. COMPONENTS OF THE SYSTEM (5) 2. Cloud software components (2) Beside the accelerometer, a PIR sensor will be used to detect the room in which the user is in. In Fig. we present the Daily Function Monitoring The PIR detects the room that the user is in and the accelerometer detects no movement for several minutes. The conclusion is that the user is resting. A. Vulpe, et.al. (2015
- 14. Conclusions In this paper we described a cloud-based approach for monitoring the healthcare condition of senior citizens. We presented the components of this monitoring system, both software and hardware. The hardware component is made up of several sensors. The software component is represented by a big data fusion software running on a cloud system. As future work we envision adding data sources from social networks in order to enable other cloud applications such as games and unified communications. A. Vulpe, et.al. (2015)
- 15. University “POLITEHNICA“ of Bucharest Faculty of Electronics, Telecommunications & Information Technology Any questions ? The work has been funded by the Sectoral Operational Programme Human Resources Development 2007-2013 of the Ministry of European Funds through the Financial Agreement POSDRU/159/1.5/S/134398. A. Vulpe, et.al. (2015)
- 16. References (1) Ford, Earl S., Janet B. Croft, David M. Mannino, Anne G. Wheaton, Xingyou Zhang, and Wayne H. Giles. "COPD surveillance—United States, 1999-2011." CHEST Journal 144, no. 1, pp. 284-305, 2013. H. Carvalho, W. Heinzelman, A. Murphy, and C. Coelho, “A general data fusion architecture”, Int. Conf. on Info. Fusion, pp. 1465-1472, 2003. D.F.M. Rodrigues, E.T. Horta, B. Silva, F.D.M. Guedes, and J.P.C. Rodrigues. "A mobile healthcare solution for ambient assisted living environments." In e-Health Networking, Applications and Services (Healthcom), 2014 IEEE 16th International Conference on, pp. 170-175, 2014. A. K. Bourke, S. Prescher, F. Koehler, V. Cionca, C. Tavares, S. Gomis, V. Garcia, and J. Nelson, “Embedded fall and activity monitoring for a wearable ambient assisted living solution for older adults”, Engineering in Medicine and Biology Society (EMBC), Annual International Conference of the IEEE, 2012. PERSONA Project, “Reference Architecture and information model for service infrastructure final version, January 2010, last access on Feb. 2, 2015. S. Kyriazakos, M. Mihaylov, B. Anggorojati, A. Mihovska, R. Craciunescu, O. Fratu, R. Prasad, “eWALL – An intelligent caring home environment offering personalized context-aware applications based on advanced sensing”, Wireless Personal Communications Journal, accepted L.D. Stone, T.L. Corwin, C.A. Barlow, “Bayesian Multiple Target Tracking”, Artech House Inc., Norwood, MA, 1999. M. Wimmer, B. Schuller, D. Arsic, G. Rigoll, and B. Radig. "Low-Level Fusion of Audio, Video Feature for Multi-Modal Emotion Recognition." In VISAPP, vol. 2, pp. 145-151, 2008. A. Doucet, N. de Freitas, N. Gordon, “Sequential Monte Carlo Methods in Practice (Statistics for Engineering and Information Science)”, Springer, New York, 2001. D. Crisan, A. Doucet, “A survey of convergence results on particle filtering methods for practitioners”, IEEE Transactions on Signal Processing vol. 50, no. 3, pp. 736–746, 2002. S.J. Julier, J.K. Uhlmann, “A non-divergent algorithm in the presence of unknown correlation”, Proc. of the American Control Conference, pp. 2369–2373, 1997. A. Vulpe, et.al. (2015)
- 17. References (2) Chen, P.O. Arambel, R.K. Mehra, “Estimation under unknown correlation: covariance intersection revisited”, IEEE Transactions on Automatic Control 47, no. 11 pp. 1879–1882, 2002. A.R. Benaskeur, “Consistent fusion of correlated data sources”, Proc. of IEEE Annual Conference of the Industrial Electronics Society, pp. 2652–2656, 2002. A.R. Benaskeur, “Consistent fusion of correlated data sources”, Proc. of IEEE Annual Conference of the Industrial Electronics Society, pp. 2652–2656, 2002. D. Patranabis, “Sensors and Transducers”, Second Edition, PHI, 2003. Craciunescu, R.; Halunga, S.; Fratu, O., „Wireless ZigBee home automation system”, Proc. SPIE 9258, Advanced Topics in Optoelectronics, Microelectronics, and Nanotechnologies VII, February 21, 2015, Constanta Romania T. Sullivan, S. Deiss, G. Cauwenberghs, “A Low-Noise, Non-Contact EEG/ECG Sensor”, In Biomedical Circuits and Systems Conference, 2007. BIOCAS 2007. IEEE, pp. 154-157, 2007. S. Cook, M. Togni, M. C. Schaub et al., “High heart rate: a cardiovascular risk factor?”, Eur Heart Journal, vol. 27, no. 20, pp. 2387-93, Oct, 2006. G. Suciu, S. Halunga, O. Fratu, A. Vasilescu, V. Suciu, "Study for renewable energy telemetry using a decentralized cloud M2M system," Wireless Personal Multimedia Communications (WPMC), 16th International Symposium on, IEEE, pp. 1-4, 2013. The eWALL Consortium, “D3.2.1, Perception from sensors,” eWALL for Active Long Living FP7 project, Oct. 2014. C. Tsai, Y. Bai, C. Chu, C. Chung and M. Lin ,”PIR-sensor-based lighting device with ultra-low standby power consumption”, Consumer Electronics, IEEE Transactions on, vol. 57, no. 3, pp. 1157-1164, 2011. F. Talantzis, A. Pnevmatikakis and A.G. Constantinides, “Audio-Visual Person Tracking: A Practical Approach”, World Scientific Publication Company, Imperial College Press, Dec. 2011 G. Suciu, C. Cernat, G. Todoran, V. Suciu, V. Poenaru, T. Militaru, and S. Halunga. "A solution for implementing resilience in open source Cloud platforms." In Communications (COMM), 2012 9th International Conference on, IEEE, pp. 335-338, 2012. A. Vulpe, et.al. (2015)