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International Journal of Computer Engineering in Research Trends. Scholarly, Peer-Reviewed, Platinum Open Access and Multidisciplinary

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Human Activity Recognition Using Smartphone and Smartwatch

Hamid M. Ali, Ali M. Muslim, , ,
Affiliations
Computer Engineering Dept., Baghdad University, Iraq
:10.22362/ijcert/2016/v3/i10/48906


Abstract
Human activity recognition is influential subject in different fields of human daily life especially in the mobile health. As the smartphone becomes an integrated part of human daily life which has the ability of complex computation, internet connection and also contains a large number of hardware sensors, encourage implementation of the human activity recognition system. Most of the works done in this field imposed the restriction of firmly fixing the smartphone in a certain position on the human body, together with machine learning mechanism, to facilitate the process of classifying human activities from the smartphone sensors raw data. To overcome this restriction, the proposed approach incorporated a smartwatch, fixed on the human ankle, together with smartphone freely carried by the user. The use of smartwatch assisted in providing distinct separable signal variation from the smartwatch accelerometer and gyroscope sensors raw data which in turn facilitated the use of a threshold-based mechanism to classify 20 various human activities. Furthermore, this work provides a service for remotely real-time monitoring of the user human activities. The system is tested with different subjects and achieved an accuracy of 97.5%.


Citation
Hamid M. Ali et al. ," Human Activity Recognition Using Smartphone and Smartwatch”, International Journal of Computer Engineering In Research Trends, 3(10):568-576,October-2016. DOI:10.22362/ijcert/2016/v3/i10/48906


Keywords : Real-time monitoring, human activity recognition, threshold-based mechanism, mHealth, smartphone, and smartwatch.

References
[1] 	S. Adibi, "Introduction," in Mobile Health A Technology Road Map, Springer International Publishing Switzerland, 2015, p. 1.
[2] 	R. Amin, C. Chien-An and L. Yan, "Human Activity Recognition using Smartphone," CSCE666, 2011.
[3] 	K. Mustafa, I. Ozlem Durmaz and E. Cem, "Online Human Activity Recognition on Smart Phones," in 2nd International Workshop on Mobile Sensing, Beijing, China, 2012. 
[4] 	H. Yi and L. Ye, "Physical Activity Recognition Utilizing the Built-In Kinematic Sensors of a Smartphone," International Journal of Distributed Sensor Networks, vol. 9, no. 1550-1477, p. 10, 2013. 
[5] 	A. Davide, G. Alessandro, O. Luca, P. Xavier and L. R.-O. Jorge, "A Public Domain Dataset for Human Activity Recognition Using Smartphones," in European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges Belgium, ESANN, 2013, pp. 437-442.
[6] 	Y. Bingchuan, H. John and E. Yalda, "Smartphone-based Activity Recognition Using Hybrid Classifier-Utilizing Cloud Infrastructure for Data Analysis," in Proceedings of the 4th International Conference on Pervasive and Embedded Computing and Communication Systems, Lisbon, Portugal, 2014. 
[7] 	T. H. Cesar and A. L. Andres, "Accelerometer-Based Human Activity Recognition in Smartphones for Healthcare Services," in Mobile Health A Technology Road Map, Springer International Publishing Switzerland, 2015, p. 147.
[8] 	D. Natarajasivan and M. Govindarajan, "Filter Based Sensor Fusion for Activity Recognition using Smartphone," International Journal of Computer Science and Telecommunications, vol. 7, no. 5, pp. 26-31, 2016. 


DOI Link : http://www.dx.doi.org/10.22362/ijcert/2016/v3/i10/48906

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