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• Palma, Adam [IFI/IAM], palma.adam@etu.unice.fr
• Rocher, Gerald [IFI/IAM], rocher.gerald@gmx.fr

• Cell phone reference: HTC WP8S
• Personal cell phone : No
• Cell phone IMEI : 358721050415354

The aim of this project is to evaluate an approach to accurately recognize a range of user’s activities and report the duration of each activity. For that purpose, tri-axial accelerometer and GPS sensors, made available in all modern smart phones, are used for the classification of four activities: resting, walking, running and driving a car. Time domain features are extracted from the GPS (User’s average speed) and tri-axis accelerometer (means, standard deviations) sensors. Accelerometer raw data is cleaned-up using the Butterworth low pass filter and a Fast Fourier Transform (FFT) is then applied to extract frequency domain features on each axis. Finally, the unsupervised k-means clustering algorithm is implemented for activities recognition and classification from time and frequency domains features. Clusters centroids are made persistent to keep activity learning and constantly improve the activity recognition accuracy.

• README File

an README file to explain all you install from Visual Studio 2013 SP2 with the SDK WP8.0 or later, to deploy and execute your project on the Windows Phone

• Project zip file

add the zip file of the Project

All needed external libraries are already embedded in the project zip file.

• MathNet.Numeric

Mean and standard deviation features are computed from the accelerometer sensor raw data for each axis. To enable mean and standard deviation computation one need to install MathNet.Numeric package. If needed, to install it, just open the Solution Explorer then right click on “References” and select “Manage NuGet packages”. In the search field, enter “MathNet” and found packages will automatically pop-up. Select Math.NET Numerics and click Install.

• Data visualization components of the Microsoft Silverlight Toolkit

Some data are displayed thru real time graphs and charts. This feature requires to install Silverlight Toolkit - Data Visualization (Charting) package. To install this package you have to run the following command in the Visual Studio 2013 Package Manager Console. More information on how to install packages from the Package Manager Console can be found at the following link : http://docs.nuget.org/docs/start-here/using-the-package-manager-console

PM> Install-Package SilverlightToolkit-DataViz

This will install:

1. System.Windows.Controls.DataVisualization.Toolkit
2. System.Windows.Controls

Once started, you will get the following user interface:

You have nothing to do. Just rest, walk, run or drive a car to get sensors data gathered. At the end of the day, you can open-up the statistics window to get a status about your daily activities:

Results are given real time to the user indicating his current activity. More interesting, results are also aggregated in the form of a histogram representing the amount of vectors assigned to each activities. Doing so we are able to provide, on a daily, weekly or monthly basis, the summary of the user activity. For that purpose a naïve algorithm can be used to compute user activity as a ratio of each activity value over the total amount of vectors composing the histogram.

A preliminary publication of this study can be found here after. It summarizes the approaches used for this project. This publication would still need to be updated with real sensors results gathered from the cell phone. Publication

• [1] Ismail, S. A., Matin, A. F. A., & Mantoro, T. (2012). A Comparison Study of Classifier Algorithms for Mobile-phone's Accelerometer Based Activity Recognition. Procedia Engineering, 41, 224-229.
• [2] M.-hee Lee, J. Kim, K. Kim, I. Lee, S. H. Jee, and S. K. Yoo, “Physical Activity Recognition Using a Single Tri-Axis Accelerometer,” vol. I, pp. 20-23, 2009
• [3] X. Long, B. Yin, and R. M. Aarts, “Single-Accelerometer-Based Daily Physical Activity Classification,” pp. 6107-6110, 2009.
• [4] L. Sun, D. Zhang, and N. Li, “Physical Activity Monitoring with Mobile Phones,” pp. 104-111, 2011.
• [5] J. R. Kwapisz, G. M. Weiss, and S. A. Moore, “Activity Recognition using Cell Phone Accelerometers,” Human Factors, vol. 12, no. 2, pp. 74-82, 2010.
• [6] L. Sun, D. Zhang, B. Li, B. Guo, and S. Li, “Activity Recognition on an Accelerometer Embedded Mobile Phone with Varying Positions and Orientations,” pp. 548-562, 2010.
• [7] T. Brezmes, J.-luis Gorricho, and J. Cotrina, “Activity Recognition from Accelerometer Data on a Mobile Phone,” Test, pp. 796-799, 2009.
• [8] Brezmes, T., Gorricho, J.L., and Cotrina, J. 2009. Activity Recognition from accelerometer data on mobile phones. In IWANN '09: Proceedings of the 10th International Work-Conference on Artificial Neural Networks, 796-799.
• [9] Ravi, N., Dandekar, N. 2005. Activity recognition from accelerometer data. In Proceedings of the Seventeenth Conference on Innovative Applications of Artificial Intelligence.
• [10] D. Anguita, A. Ghio, L. Oneto, X. Parra, and J.L. Reyes-Ortiz. Human activity recognition on smart- phones using a multiclass hardware-friendly support vector machine. In Proceedings of the Interna- tional Workshop of Ambient Assited Living, 2012.
• [11] Bernecker, T., Graf, F., Kriegel, H. P., Moennig, C., Dill, D., & Tuermer, C. Activity Recognition on 3D Accelerometer Data (Technical Report).
• [12] Gartner survey on smart phones worldwide penetration (http://www.gartner.com/newsroom/id/2665715 )
• [13] Connaissances et comportements de la population française en matière d’alimentation et d’activité physique (http://www.inpes.sante.fr/CFESBases/catalogue/pdf/1283.pdf )
• [14] Fujiki Y.: iPhone as a Physical Activity Measurement Platform. In: CHI’10 USA (2010).
• [15] Ayu M. A., Mantoro T., Abdul Matin A. F., Basamh S. S. O., “Recognizing user activity based on accelerometer data from a mobile phone,” in Proc. 2011 IEEE Symp. on Computers and Informatics, Kuala Lumpur, Malaysia, 2011, pp.617-621.
• [16] Subramanya, A., Raj, A., Bilmes, J., and Fox, D. 2006. Recognizing activities and spatial context using wearable sensors. In Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence.
• [17] Bao, L., & Intille, S. S. (2004). Activity recognition from user-annotated acceleration data. In Pervasive computing (pp. 1-17). Springer Berlin Heidelberg.
• [18] A. Krause, D.P. Siewiorek, A. Smailagic, and J. Farringdon. Unsupervised, dynamic identification of physiological and activity context in wearable computing. In Proceedings of the 7th International Symposium on Wearable Computers, pages 88–97. IEEE Press.
• [19] Alizadeh, A. (2014). Gesture Recognition based on Hidden Markov Models from Joints' Coordinates of a Depth Camera for Kids age of 3-8