Authors:
P. Jegathesh,P.Preetha,S. Chitra,A.S. Harivignesh,DOI NO:
https://doi.org/10.26782/jmcms.2020.05.00013Keywords:
Artificial Intelligent,Machine Learning,RFID(RadioFrequency Identification) sensor,Decision Tree,SVM tree,Data analytics,K-Means,Naïve Bayes theorem,Abstract
Perching on cot, perching on recliner, obtaining out of cot and step dancing (ambulating standing, walking round the room) somewhere is troublesome for the older folks. Ambulating with facilitate of the folks or oversight is known jointly of the key causes of patient falls in hospitals and rest home thus we tend to use Artificial Intelligent and Machine Learning for top falls risks of older folks. Machine learning is associate algorithmic rule that's used for predicting outcomes accurately. we tend to incontestable 2 datasets that embrace time in seconds, frontal axis of acceleration, vertical axis of acceleration, and Lateral axis of acceleration, label of activity, frequency, phase, received signal strength indicator and Id of antenna reading sensing element. such a big amount of technological solutions area unit foreseen for bed existing detection employing a style of sensors that area unit fastened with older folk body, their cot or around somewhere with context to the older folks orfloor.Refference:
I. A. Godfrey, A. Bourke, G. O´laighin, P. van de Ven, and J. Nelson. Activity classification using a single chest mounted tri-axial accelerometer. Med. Eng. Phys.,33(9):1127–1135, 2011.
II. A. M. Khan, A. Tufail, A. M. Khattak, and T. H. Laine. Activity recognition on smartphones viasensor-fusion and KDA-based SVMs. Int. J. Distrib. Sens.Netw., 2014:e503291, 2014.
III. B. Najafi, K. Aminian, A. Paraschiv-Ionescu, F. Loew,C. Bula, and P. Robert. Ambulatory system for human motion analysis using a kinematic sensor: Monitoring of daily physical activity in the elderly. IEEE Trans. Biomed. Eng., 50(6):711–723, 2003.
IV. D. C. Ranasinghe, R. L. Shinmoto Torres, A. P. Sample, J. R. Smith, K. Hill, and R. Visvanathan. Towards falls prevention: A wearable wireless and battery-less sensing and automatic identification tag for real time monitoring of human movements. In Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
V. D. C. Ranasinghe, R. L. Shinmoto Torres, K. Hill, and R. Visvanathan. Low cost and batteryless sensor-enabled radio frequency identification tag based approaches to identify patient bed entry and exit posture transitions. Gait & Posture, 39(1):118–123, 2014.
VI. D. Karantonis, M. Narayanan, M. Mathie, N Lovell, and B. Celler. Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring. IEEE Trans. Inf. Technol. Biomed.,10(1):156–167, 2006.
VII. F. Miskelly. A novel system of electronic tagging in patients with dementia and wandering. Age and Ageing,33(3):304–306, 2004.
VIII. H. He and E. Garcia. Learning from imbalanced data.IEEE Trans. Knowl. Data Eng., 21(9):1263–1284,2009.
IX. H. Junker, O. Amft, P. Lukowicz, and G. Tro¨ster. Gesture spotting with body-worn inertial sensors to detect user activities. Pattern Recognition, 41(6):2010–2024, 2008. Bakharev T., “Durability of geopolymer materials in sodium and magnesium sulfate solutions”, Cement and Concrete Research. vol. 35, no. 6, pp: 1233-1246, 2005
X. J. Fessler and B. Sutton. Nonuniform fast fourier transforms using min-max interpolation. IEEE Trans. Signal Process., 51(2):560–574, 2003
XI. L. Gao, A. K. Bourke, and J. Nelson. Evaluation of accelerometer based multi-sensor versus single-sensor activity recognition systems. Med. Eng. Phys.,36(6):779–785, 2014.
XII. L. Gao, A. K. Bourke, and J. Nelson. Sensor positioning for activity recognition using multiple accelerometer-based sensors. In European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), 2013.
XIII. L. Greengard and J. Lee. Accelerating the nonuniform fast fourier transform. SIAM Review, 46(3):443–454, 2004.
XIV. M. G o¨vercin, Y. Ko¨ltzsch, M. Meis, S. Wegel,M. Gietzelt, J. Spehr, S. Winkelbach, M. Marschollek, and E. Steinhagen-Thiessen. Defining the user requirements for wearable and optical fall prediction and fall detection devices for home use. Inform. Health Soc. Care, 35(3-4):177–187, 2010.
XV. M. Patel and J. Wang. Applications, challenges, and prospective in emerging body area networking technologies. IEEE Wireless Commun. Mag., 17(1):80–88, 2010.
XVI. M.-W. Lee, A. M. Khan, and T.-S. Kim. A singletri-axial accelerometer-based real-time personal life log system capable of human activity recognition and exercise information generation. Pers. Ubiquitous Comput.,15(8):887–898, 2011.
XVII. N. C. Krishnan and D. J. Cook. Activity recognition on streaming sensor data. Pervasive and Mobile Computing,2012.
XVIII. R. Keys. Cubic convolution interpolation for digital image processing. IEEE Transactions on Acoustics, Speech and Signal Processing, 29(6):1153–1160, 1981.
XIX. R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin. LIBLINEAR: A library for large linear classification. J. Mach. Learn. Res., 9:1871–1874,2008
XX. Shinmoto Torres, R. L., Ranasinghe, D. C., Shi, Q. (2013, December). Evaluation of wearable sensor tag data segmentation approaches for real time activity classification in elderly. In International Conference on Mobile and Ubiquitous Systems: Computing, Networking, and Services (pp. 384-395). Springer International Publishing.
XXI. Shinmoto Torres, R. L., Ranasinghe, D. C., Shi, Q., Sample, A. P. (2013, April). Sensor enabled wearable RFID technology for mitigating the risk of falls near beds. In 2013 IEEE International Conference on RFID (pp.191-198). IEEE.
XXII. Shinmoto Torres, R. L., Visvanathan, R., Hoskins, S., van den Hengel, A., Ranasinghe, D. C. (2016). Effectiveness of a batteryless and wireless wearable sensor system for identifying bed and chair exits in healthy older people. Sensors, 16(4), 546.
XXIII. T. Kaufmann, D. C. Ranasinghe, M. Zhou, and C. Fumeaux. Wearable quarter-wave folded microstrip antenna for passive UHF RFID applications. Int. J. Antennas Propag., 2013.
XXIV. T. Pl o¨tz, N. Y. Hammerla, and P. Olivier. Feature learning for activity recognition in ubiquitous computing. In IJCAI Proceedings-International Joint Conference on Artificial Intelligence, volume 22, page 1729, 2011.
XXV. Wickramasinghe, A., Ranasinghe, D. C. (2015, August). Recognising Activities in Real Time Using Body Worn Passive Sensors With Sparse Data Streams: To Interpolate or Not To Interpolate?. In proceedings of the 12th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (pp.21-30). ICST.
XXVI. Wickramasinghe, A., Ranasinghe, D. C., Fumeaux, C., Hill, K. D., Visvanathan, R. (2016), ‘Sequence Learning with Passive RFID Sensors for Real Time Bed-egress Recognition in Older People,’ in IEEE Journal of Biomedical and Health Informatics