Abstract
In this paper, we proposed a new classification model to perform automatic recognition of activities using Smartphones data from a gyroscope and accelerometer sensors. We target assisted living applications such as remote patient activity monitoring for the disabled and the elderly. The proposed method LDA-KNN-SVM combine the Linear Discriminant Analysis (LDA) for dimension reduction and K-Nearest Neighbors (KNN) with Support Vector Machines (SVM) allowing to better discrimination between the classes of activities. Several experiments performed with real datasets show a significant improvement of our proposed approach in terms of recognition performance.
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References
Abidine MB, Fergani L, Fergani B, Fleury A (2015) Improving human activity recognition in smart homes. Int J E-Health Med Commun (IJEHMC) 6(3):19–37
Bishop C (2006) Pattern Recognition and Machine Learning. Springer. New York, ISBN 978-0-387-31073-2
Anjum A, Ilyas M (2013) Activity recognition using smartphone sensors. In: Proceedings of the 2013 IEEE Consumer Communications and Networking Conference, Las Vegas, NV, USA, pp 914–919, 11–14 January 2013
Anguita D, et al (2012) Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine. In: International Workshop of Ambient Assisted Living (IWAAL 2012), Vitoria-Gasteiz, Spain, December 2012
Siirtola P (2012) Recognizing human activities user-independently on smartphones based on accelerometer data. Int J Interact Multimed Artif Intell 1:38–45
Martin H, Bernardos AM, Iglesias J, Casar JR (2013) Activity logging using lightweight classification techniques in mobile devices. Pers Ubiquitous Comput 17:675–695
Mannini A, Sabatini AM (2010) Machine learning methods for classifying human physical activity from on-body accelerometers. Sensors 10(2):1154–1175
Fisher RA (1938) The statistical utilization of multiple measurements. Annal Eugen 8:376–386
https://archive.ics.uci.edu/ml/datasets/Smartphone+Dataset+for+Human+Activity+Recognition+%28HAR%29+in+Ambient+Assisted+Living+%28AAL%29. Accessed Mar 2016
https://archive.ics.uci.edu/ml/datasets/SmartphoneBased+Recognition+of+Human+Activities+and+Postural+Transitions. Accessed 10 Mar 2016
Chang CC, Lin CJ (2017) LIBSVM: a library for support vectormachines. ACM Trans Intell Syst Technol 2:1–27. http://www.csie.ntu.edu.tw/~cjlin/libsvm/
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Menhour, I., Fergani, B., Abidine, M.B. (2019). E-Health Human Activity Recognition Scheme Using Smartphone’s Data. In: Hajji, B., Tina, G.M., Ghoumid, K., Rabhi, A., Mellit, A. (eds) Proceedings of the 1st International Conference on Electronic Engineering and Renewable Energy. ICEERE 2018. Lecture Notes in Electrical Engineering, vol 519. Springer, Singapore. https://doi.org/10.1007/978-981-13-1405-6_17
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DOI: https://doi.org/10.1007/978-981-13-1405-6_17
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