Development of Motion Detection Algorithms Based on Simultaneous Execution Using Mobile Phone Sensors

[1] Freeman, C., Louca, F., “The Emergence of a New Techno-Economic Paradigm: The Age of Information and Communication Technology (ICT)”, As Time Goes By – From the Industrial Revolutions to the Information Revolution, New York, 2002, pp. 318–324.10.1093/0199251053.003.0009Search in Google Scholar

[2] Naqvi, N. Z., Kumar, A., Chauhan, A., Sahni, K., “Step Counting Using Smartphone-Based Accelerometer”, International Journal on Computer Science and Engineering, 4(3), pp. 675–681, May. 2012.Search in Google Scholar

[3] Jayalath, S., Abhayasinghe, N., Murray, I., “A Gyroscope Based Accurate Pedometer Algorithm”, Presented at International Conference on Indoor Positioning and Indoor Navigation, Oct. 2013.Search in Google Scholar

[4] Cruz-Silva, N., Mendes-Moreira, J., Menezes, P., “Features Selection for Human Activity Recognition with iPhone Inertial Sensors”, Presented at Portuguese Conference on Artificial Intelligence, 2013.Search in Google Scholar

[5] Das, S., Green, L., Perez, B., Murphy, M., “Detecting User Activities using the Accelerometer on Android Smartphones”, Jul. 2010.Search in Google Scholar

[6] Cerqueira da Silva, J. R., “Smartphone Based Human Activity Prediction”, M.S. thesis, Faculdade de Engenhariau., Universidade do Porto., 2013.Search in Google Scholar

[7] Siirtola, P., Röning, J., “Recognizing Human Activities User independently on Smartphones Based on Accelerometer Data”, International Journal of Interactive Multimedia and Artificial Intelligence, 1(5), pp. 38–45, Jun 2012.10.9781/ijimai.2012.155Search in Google Scholar

[8] Khan, A. M., Lee, Y.-K., Lee, S. Y., Kim, T.-S., “Human Activity Recognition via An Accelerometer-Enabled-Smartphone Using Kernel Discriminant Analysis”. Presented at Future Information Technology, 2010 5th International Conference, May. 2010.10.1109/FUTURETECH.2010.5482729Search in Google Scholar

[9] Tomlein, M., Bielik, P., Krátky, P., Mitrík, S., Barla, M., Bieliková, M., “Advanced Pedometer for Smartphone-based Activity Tracking”, Presented at Proceedings of the International Conference on Health Informatics, 2012.Search in Google Scholar

[10] Shin, J., Shin, D., Shin, D., Her, S., Kim, S., Lee, M., “Human Movement Detection Algorithm Using 3-Axis Accelerometer Sensor Based on Low-Power Management Scheme for Mobile Health Care System”, Presented at GPC'10 Proceedings of the 5th international conference on Advances in Grid and Pervasive Computing, May. 2010.10.1007/978-3-642-13067-0_12Search in Google Scholar

[11] Kwapisz, J. R., Weiss, G. M., Moore, S. A., “Activity Recognition using Cell Phone Accelerometers”, ACM SIGKDD Explorations Newsletter, Dec. 2010.10.1145/1964897.1964918Search in Google Scholar

[12] Dernbach, S., Das, B., Krishnan, N. C., “Simple and Complex Activity Recognition Through Smart Phones”, Presented at Intelligent Environments (IE), 2012 8th International Conference, Jun. 2012.10.1109/IE.2012.39Search in Google Scholar

[13] Yang, C.C., Hsu, Y.L., “Development of a wearable motion detector for telemonitoring and real-time identification of physical activity”, Telemed. J. E. Health, no. 15, 2009, pp. 62–72.10.1089/tmj.2008.006019199849Search in Google Scholar

[14] Karantonis, D.M., Narayanan, M.R., Mathie, M., Lovell, N.H., Celler, B.G., “Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring”, IEEE. Trans. Inf. Technol. Biomed. no. 10, 2006, pp. 156–167.10.1109/TITB.2005.85686416445260Search in Google Scholar

[15] Ozdemir, A. T. and Barshan, B., “Detecting Falls with Wearable Sensors Using Machine Learning Techniques” Sensors, vol. 14, MDPI, pp. 10691–10708, 2014.Search in Google Scholar