A revised framework of machine learning application for optimal activity recognition

Author:

Bilal Mohsin,Shaikh Faisal K.,Arif Muhammad,Wyne Mudasser F.

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Software

Reference31 articles.

1. Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.L.: A public domain dataset for human activity recognition using smartphones. In: 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013, Bruges, Belgium (2013)

2. Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.: Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine. In: Bravo, J., Hervs, R., Rodrguez, M. (eds.) Ambient Assisted Living and Home Care, 4th IWAAL, Lecture Notes in Computer Science, vol. 7657, pp. 216–223. Springer, Berlin (2012)

3. HUYNH, D.T.G.: Human activity recognition with wearable sensors. Ph.D. dissertation, Technische Univ., Darmstadt (2008)

4. Chan, M., Estève, D., Fourniols, J.-Y., Escriba, C., Campo, E.: Smart wearable systems: current status and future challenges. Artif. Intell. Med. 56(3), 137–156 (2012)

5. Ryder, J., Longstaff, B., Reddy, S., Estrin, D.: Ambulation: a tool for monitoring mobility patterns over time using mobile phones. In: International Conference on Computer Science Engineering, Vancouver, pp. 927–931 (2009)

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