Abstract
Mobile phone is becoming a very popular tool due to having various user friendly applications with all flexible options. It is highly popular for its light weight, wearable and comfortable uses. Many extrinsic habitat of human being can be monitored by the help of inbuilt sensors and its application software. This has appealing use for healthcare applications using exploitation of Ambient Intelligence for daily activity monitoring system. Here, a standard dataset of UCI HAR (University of California, Irvine, Human Activity Recognition, http://archive.ics.uci.edu) is used for analysis purpose. Naive Bayes Classifier is used for recognition of runtime activities minimizing dimension of large feature vectors. Threshold based condition box is designed by us and finally these two results are compared with that of another classifier HF-SVM (Hardware Friendly-Support Vector Machine) of previous related work.
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References
Acharjee D, Mukherjee A, Mukherjee N (2012) Computing aspects of monitoring walking disorder using body sensor network and neural network. The 2nd IEEE international conference on ‘parallel, distributed and grid computing-PDGC2012’. http://ieeeXplore.ieee.org
Aiello F, Bellifemine FL, Fortino G, Galzarano S, Gravina R (2011) An agent-based signal processing in-node environment for real-time human activity monitoring based on wireless body sensor networks. Eng Appl Artif Intell. Elsevier Pub. 24:1147–1161. doi:10.1016/j.engappai.2011.06.007
Autoregression coefficient with burg order 4 follow: http://paulbourke.net/miscellaneous/ar/. Accessed on 24 June 2014
Bao L, Intille SS (2004) Activity recognition from user-annotated acceleration data. Pervasive. Springer-Verlag Berlin Heidelberg, LNCS 3001. pp 1–17
Davide A, Ghio A, Oneto L, Parra X, Reyes-Ortiz JL (2012) Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine. International Workshop of Ambient Assisted Living (IWAAL). Vitoria-Gasteiz, Spain
For IQR Follow: http://terpconnect.umd.edu/~toh/spectrum/SignalsAndNoise.hml. Accessed on 24 May 2014
For Reference, 64 BINS: http://www.pd-tutorial.com/english/ch03s08.html. Accessed on 22 Sept 2014
http://www.mathworks.com/matlabcentral/answers/142765-calculate-signal-magnitude-area-of-an-accelerometer-in-matlab. Accessed on 21 Sept 2014
http://en.wikipedia.org/wiki/Frequency_modulation/. Accessed on 21 Aug 2014
Kela J, Korpipää P, Mäntyjärvi J, Kallio S, Savino G, Jozzo L, Marca D (2006) Accelerometer-based gesture control for a design environment. Pers Ubiquitous Comput 10(5):285–299
Kim DJ, Pravhakaran B (2011) Motion fault detection and isolation in body sensor networks. Pervasive Mob Comput 7:727–745. Elsevier. doi:10.1016/j.pmcj.2011.09.006
Kurtosis: http://www.itl.nist.gov/div898/handbook/eda/section3/eda35b.htm. Accessed on 20 July 2014
Matthew K, Zhou G, Xing G, Wu J, Pyles A (2011) PBN: towards practical activity recognition using smartphone-based body sensor networks. SenSys-11, ACM 978-1-4503-0718-5/11/11
Roy SH, Cheng MS, Chang S-S, Moore J, De Luca G, Hamid Nawab S, De Luca CJ (2009) A combined sEMG and accelerometer system for monitoring functional activity in stroke. IEEE Trans Neural Syst Rehabil Eng 17(6):585–594. doi:10.1109/TNSRE.2009.2036615
Shnayder V, Chen B, Chen B, Lorincz K, Jones TRFF, Welshand M (2005) Sensor networks for medical care. Technical report TR-08-05. Division of Engineering and Applied Sciences, Harvard University. http://www.eecs.harvard.edu/˜mdw/proj/codeblue
Thiemjarus S, Lo B, Yang G-Z (2006) A spatio-temporal architecture for context aware sensing. In: Proceedings of the international workshop on wearable and implantable body sensor networks (BSN’06), 0-7695-2547-4/06, IEEE
UCI HAR Dataset: http://archive.ics.uci.edu/ml/machine-learning-databases/00240/. Accessed on 23 Sept 2014
Van Kasteren TLM, Englebienne G, Krose BJA (2010) An activity monitoring system for elderly care using generative and discriminative models. Pers Ubiquitous Comput 14:489–498. doi:10.1007/s00779-009-0277-9, springerlink.com
Wang L, Gu T, Chen H, Tao X, Lu J (2011) Real-time activity recognition in wireless body sensor networks: from simple gestures to complex activities. The 16th IEEE international conference on embedded and real-time computing systems and applications
Zhu C, Sheng W (2012) Realtime recognition of complex human daily activities using human motion and location data. IEEE Trans Biomed Eng 59(9):2012. doi:10.1109/TBME.2190602
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Acharjee, D., Mukherjee, A., Mandal, J.K. et al. Activity recognition system using inbuilt sensors of smart mobile phone and minimizing feature vectors. Microsyst Technol 22, 2715–2722 (2016). https://doi.org/10.1007/s00542-015-2551-2
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DOI: https://doi.org/10.1007/s00542-015-2551-2