FAST: A fog computing assisted distributed analytics system to monitor fall for stroke mitigation

Proceedings of the 2015 IEEE International Conference on Networking, Architecture and Storage, NAS 2015

Volumn , Issue , 2015, Pages 2-11

  Cao, Yu ^a   Chen, Songqing ^b   Hou, Peng ^a   Brown, Donald ^a  

^a University of Massachusetts Lowell   (United States)

^b Krasnow Institute for Advanced Study   (United States)

Author keywords

Accelerometers; Brain models; Detection algorithms; Monitoring; Sensors; Smart phones

Indexed keywords

ACCELEROMETERS; BRAIN MODELS; DECISION MAKING; DESIGN; DIGITAL STORAGE; ENERGY UTILIZATION; FIRE FIGHTING EQUIPMENT; FOG; HEALTH; MONITORING; NETWORK ARCHITECTURE; SENSORS; SIGNAL DETECTION; SMARTPHONES; TIME SERIES ANALYSIS;

ACCELERATION MAGNITUDE; COMPUTING PARADIGM; DETECTION ALGORITHM; EDGE INTELLIGENCE; FILTERING TECHNIQUE; NONLINEAR TIME-SERIES ANALYSIS; PERVASIVE HEALTH MONITORING SYSTEMS; PERVASIVE HEALTHS;

DISTRIBUTED COMPUTER SYSTEMS;

EID: 84960857858     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/NAS.2015.7255196     Document Type: Conference Paper

References (34)

1. Amazon web services, inc. http://aws.amazon.com
2. Stroke 101 fact sheet by national stroke association, centennial, co, u.s.a
3. S. Abbate, M. Avvenuti, P. Corsini, J. Light, and A. Vecchio. Monitoring of Human Movements for Fall Detection and Activities Recognition in Elderly Care Using Wireless Sensor Network: a Survey. InTech, 2010
4. Stefano Abbate, Marco Avvenuti, Guglielmo Cola, Paolo Corsini, Janet Light, and Alessio Vecchio. Recognition of false alarms in fall detection systems. In CCNC 2011, 2011
5. Fabio Bagal, Clemens Becker, Angelo Cappello, Lorenzo Chiari, Kamiar Aminian, Jeffrey M Hausdorff, Wiebren Zijlstra, and Jochen Klenk. Evaluation of accelerometer-based fall detection algorithms on realworld falls. PloS one, 7(5):e37062, 2012
6. Flavio Bonomi, Rodolfo Milito, Preethi Natarajan, and Jiang Zhu. Fog computing: A platform for internet of things and analytics. In Big Data and Internet of Things: A Roadmap for Smart Environments, pages 169-186. Springer, 2014
7. Flavio Bonomi, Rodolfo Milito, Jiang Zhu, and Sateesh Addepalli. Fog computing and its role in the internet of things. In Proceedings of the first edition of the MCC workshop on Mobile cloud computing, pages 13-16. ACM, 2012
8. AK Bourke, JV O'brien, and GM Lyons. Evaluation of a thresholdbased tri-axial accelerometer fall detection algorithm. Gait and Posture, 26(2):194-199, 2007
9. AK Bourke, P Van Ven, M Gamble, R O'Connor, K Murphy, E Bogan, E McQuade, P Finucane, G OLaighin, and J Nelson. Evaluation of waist-mounted tri-axial accelerometer based fall-detection algorithms during scripted and continuous unscripted activities. Journal of Biomechanics, 43(15):3051-3057, 2010
10. Alan K Bourke and Gerald M Lyons. A threshold-based fall-detection algorithm using a bi-axial gyroscope sensor. Medical Engineering and Physics, 30(1):84-90, 2008
11. Alan K Bourke, Pepijn WJ , Amy E Chaya, Gearoid M Olaighin, and John Nelson. Testing of a long-term fall detection system incorporated into a custom vest for the elderly. In EMBS 2008, pages 2844-2847. IEEE
12. Jose Carlos Castillo, Davide Carneiro, Juan Serrano-Cuerda, Paulo Novais, Antonio Fernandez-Caballero, and Jose Neves. A multi-modal approach for activity classification and fall detection. International Journal of Systems Science, 45(4):810-824, 2014
13. Jay Chen, Karric Kwong, Dennis Chang, Jerry Luk, and Ruzena Bajcsy. Wearable sensors for reliable fall detection. In EMBC 2005, pages 3551-3554. IEEE, 2006
14. JH Crosbie, S Lennon, JR Basford, and SM McDonough. Virtual reality in stroke rehabilitation: still more virtual than real. Disability and Rehabilitation, 29(14):1139-1146, 2007
15. Thato E Foko, Nomusa Dlodlo, and Litsietsi Montsi. An integrated smart system for ambient-assisted living, pages 128-138. Springer, 2013
16. Jordan Frank, Shie Mannor, Joelle Pineau, and Doina Precup. Time series analysis using geometric template matching. PAMI, 35(3), 2013
17. Alan S. Go, Dariush Mozaffarian et al. Heart disease and stroke 2013 statistical update. Circulation Journal by American Heart Association, 127:6-245, 2013
18. Doug Howe, Maria Costanzo, Petra Fey, Takashi Gojobori, Linda Hannick, Winston Hide, David P Hill, Renate Kania, Mary Schaeffer, Susan Pierre, et al. Big data: The future of biocuration. Nature, 455(7209):47-50, 2008
19. Jingshan Huang, Dejing Dou, Haishan Liu, Lei He, P Hayes, R Rudnick, H Shah, D Fell, and Wei Liu. Neumore: Ontology in stroke recovery. In Proc. of 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW), 2010
20. Richard Kahn, Rose Marie Robertson, Robert Smith, and David Eddy. The impact of prevention on reducing the burden of cardiovascular disease. Circulation Journal by American Heart Association, 108:576-585, 2008
21. Maarit Kangas, Antti Konttila, Per Lindgren, Ilkka Winblad, and Timo Jams. Comparison of low-complexity fall detection algorithms for body attached accelerometers. Gait and Posture, 28(2):285-291, 2008
22. Alexander A. Khalessi, Sabareesh K. Natarajan, David Orion, Mandy J. Binning, Adnan Siddiqui, Elad I. Levy, and L. Nelson Hopkins. Acute stroke intervention. Journal of the American College of Cardiology: Cardiovascular Interventions, 4:261-269, 2011
23. Q. Li, J. Stankovic, M. Hanson, A. Barth, J. Lach, and G. Zhou. Accurate, fast fall detection using gyroscopes and accelerometer-derived posture information. In Proc. of BSN 2009, 2009
24. J. Lin, E. Keogh, S. Lonardi, and B. Chiu. A symbolic representation of time series, with implications for streaming algorithms. In Proc. of the 8th SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, 2003
25. J. Lockhart, G. Weiss, J. Xue, S. Gallagher, A. Grosner, and T. Pulickal. Design considerations for the wisdm smart phone-based sensor mining architecture. In Proc. of the Fifth International Workshop on Knowledge Discovery from Sensor Data, Held in Conjunction with The 17th ACM SIGKDD, volume 25-33, 2011
26. Jeffrey W. Lockhart, Tony Pulickal, and Gary M. Weiss. Applications of mobile activity recognition, 2012
27. S Madgwick. An efficient orientation filter for inertial and inertial/ magnetic sensor arrays. Report x-io and University of Bristol (UK), 2010
28. Violeta Mirchevska, Mitja Lutrek, and Matja Gams. Combining domain knowledge and machine learning for robust fall detection. Expert Systems, 2013
29. Heidenreich PA, Trogdon JG, Khavjou OA, Butler J, Dracup K, Ezekowitz MD, Finkelstein EA, Hong Y, Johnston SC, Khera A, Lloyd-Jones DM, Nelson SA, Nichol G, Orenstein D, Wilson PW, and Woo YJ. Forecasting the future of cardiovascular disease in the united states: a policy statement from the american heart association. Circulation Journal by American Heart Association, 123:933-944, 2011
30. N. Hema Rajini and R. Bhavani. Computer aided detection of ischemic stroke using segmentation and texture features. Journal of the Interna-tional Measurement Confederation by Elsevier, 46(6):1865-1874, 2013
31. Vladimir Stantchev, Ahmed Barnawi, Sarfaraz Ghulam, Johannes Schubert, and Gerrit Tamm. Smart items, fog and cloud computing as enablers of servitization in healthcare. 2015
32. AH Tayal, M Tian, KM Kelly, SC Jones, DG Wright, D Singh, J Jarouse, J Brillman, S Murali, and R Gupta. Atrial fibrillation detected by mobile cardiac outpatient telemetry in cryptogenic tia or stroke. Neurology, 71(21):1696-1701, 2008
33. Luis M Vaquero and Luis Rodero-Merino. Finding your way in the definition fog: Towards a comprehensive of fog computing. ACM SIGCOMM COMPUTER COMMUNICATION REVIEW, 44(5):27-32, 2014
34. Z Yang. Powertutor-A power monitor for android-based mobile platforms. EECS, University of Michigan, retrieved September, 2, 2012