3. Klasnja, P., Pratt, W.: Healthcare in the pocket: mapping the space of mobile-phone health
interventions. J. Biomed. Inf. 45, 184–198 (2012)
4. Candás, J.L.C., Peláez, V., López, G., Fernández, M.Á., Álvarez, E., Díaz, G.: An automatic
data mining method to detect abnormal human behaviour using physical activity
measurements. Pervasive Mob. Comput. 15, 228–241 (2014)
5. Shoaib, M., Bosch, S., Incel, D., Scholten, H., Havinga, P.J.M.: A survey of online activity
recognition using mobile phones. J. Sens. (Basel, Switzerland) 15(1), 2059–2085 (2015)
6. Fu, Y.: Human Activity Recognition and Prediction. Springer, Switzerland (2016). https://
doi.org/10.1007/978-3-319-27004-3
7. Chetty, G., White, M., Akther, F.: Smart phone based data mining for human activity
recognition, Elsevier Procedia Comput. Sci. – ICICT 46(8), 1181–1187 (2015)
8. Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.L.: Human activity recognition on
smartphones using a multiclass hardware-friendly support vector machine. In: Bravo, J.,
Hervás, R., Rodríguez, M. (eds.) IWAAL 2012. LNCS, vol. 7657, pp. 216–223. Springer,
Heidelberg (2012). https://doi.org/10.1007/978-3-642-35395-6_30
9. Ahmad, N., Han, L., Iqbal, K., Ahmad, R., Abid, M.A., Iqbal, N.: SARM: salah activities
recognition model based on smartphone. Electronics 8(8), 881 (2019)
10. Tran, D.N., Phan, D.D.: Human activities recognition in android smartphone using support
vector machine. In: 2016 7th International Conference on Intelligent Systems, Modelling and
Simulation (ISMS), pp. 64–68. IEEE (2016)
11. Awan, M.A., Guangbin, Z., Kim, H.C., Kim, S.D.: Subject-independent human activity
recognition using smartphone accelerometer with cloud support. Int. J. Ad Hoc Ubiq.
Comput. 20(3), 172–185 (2015)
12. Shoaib, M., Scholten, H., Havinga, P.J.M.: Towards physical activity recognition using
smartphone sensors. In: Proceedings of the 2013 IEEE 10th International Conference on
Ubiquitous Intelligence and Computing and IEEE 10th International Conference on
Autonomic and Trusted Computing, Italy 18–21, pp. 80–87 (2013)
13. Abidine, M.B., Yala, N., Fergani, B., Clavier, L.: Soft margin SVM modeling for handling
imbalanced human activity datasets in multiple homes. In: 2014 International Conference on
Multimedia Computing and Systems (ICMCS), pp. 421–426. IEEE (2014)
14. Alman, A., Lawi, A., Tahir, Z.: Pattern recognition of human activity based on smartphone
data sensors using SVM multiclass. In: 1st International Conference on Science and
Technology, ICOST 2019. European Alliance for Innovation (EAI) (2019)
15. Cheng, B.C., Tsai, Y.A., Liao, G.T., Byeon, E.S.: HMM machine learning and inference for
Activities of Daily Living recognition. J. Supercomput. 54(1), 29–42 (2010)
16. Jolliffe, I.T.: Principal Component Analysis, 2nd edn. Springer, NewYork (2010)
17. Osuna, E., Freund, R., Girosi, F.: Support vector machines: training and applications.
Massachusetts Institute of Technology, Cambridge (1997)
18. Veropoulos, K., Campbell, C., Cristianini, N.: Controlling the sensitivity of support vector
machines. In: Proceedings of the International Joint Conference on AI, pp. 55–60 (1999)
19. Huang, Y.M., Du, S.X.: Weighted support vector machine for classification with uneven
training class sizes. In: Proceedings of the IEEE International Conference on Machine
Learning and Cybernetics, vol. 7, pp. 4365–4369 (2005)
20. Abidine, B.M., Fergani, L., Fergani, B., Oussalah, M.: The joint use of sequence features
combination and modified weighted SVM for improving daily activity recognition. Pattern
Anal. Appl. 21(1), 119–138 (2016). https://doi.org/10.1007/s10044-016-0570-y
21. Cheng, B.C., Tsai, Y.A., Liao, G.T., Byeon, E.S.: HMM machine learning and inference for
activities of daily living recognition. J. Supercomput. 54(1), 29–42 (2010). https://doi.org/
10.1007/s11227-009-0335-0
22. https://archive.ics.uci.edu/ml/machine-learning-databases/00240/. Accessed Mar 2017
Human Activities Recognition in Android Smartphone Using WSVM-HMM Classifier
393
interventions. J. Biomed. Inf. 45, 184–198 (2012)
4. Candás, J.L.C., Peláez, V., López, G., Fernández, M.Á., Álvarez, E., Díaz, G.: An automatic
data mining method to detect abnormal human behaviour using physical activity
measurements. Pervasive Mob. Comput. 15, 228–241 (2014)
5. Shoaib, M., Bosch, S., Incel, D., Scholten, H., Havinga, P.J.M.: A survey of online activity
recognition using mobile phones. J. Sens. (Basel, Switzerland) 15(1), 2059–2085 (2015)
6. Fu, Y.: Human Activity Recognition and Prediction. Springer, Switzerland (2016). https://
doi.org/10.1007/978-3-319-27004-3
7. Chetty, G., White, M., Akther, F.: Smart phone based data mining for human activity
recognition, Elsevier Procedia Comput. Sci. – ICICT 46(8), 1181–1187 (2015)
8. Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.L.: Human activity recognition on
smartphones using a multiclass hardware-friendly support vector machine. In: Bravo, J.,
Hervás, R., Rodríguez, M. (eds.) IWAAL 2012. LNCS, vol. 7657, pp. 216–223. Springer,
Heidelberg (2012). https://doi.org/10.1007/978-3-642-35395-6_30
9. Ahmad, N., Han, L., Iqbal, K., Ahmad, R., Abid, M.A., Iqbal, N.: SARM: salah activities
recognition model based on smartphone. Electronics 8(8), 881 (2019)
10. Tran, D.N., Phan, D.D.: Human activities recognition in android smartphone using support
vector machine. In: 2016 7th International Conference on Intelligent Systems, Modelling and
Simulation (ISMS), pp. 64–68. IEEE (2016)
11. Awan, M.A., Guangbin, Z., Kim, H.C., Kim, S.D.: Subject-independent human activity
recognition using smartphone accelerometer with cloud support. Int. J. Ad Hoc Ubiq.
Comput. 20(3), 172–185 (2015)
12. Shoaib, M., Scholten, H., Havinga, P.J.M.: Towards physical activity recognition using
smartphone sensors. In: Proceedings of the 2013 IEEE 10th International Conference on
Ubiquitous Intelligence and Computing and IEEE 10th International Conference on
Autonomic and Trusted Computing, Italy 18–21, pp. 80–87 (2013)
13. Abidine, M.B., Yala, N., Fergani, B., Clavier, L.: Soft margin SVM modeling for handling
imbalanced human activity datasets in multiple homes. In: 2014 International Conference on
Multimedia Computing and Systems (ICMCS), pp. 421–426. IEEE (2014)
14. Alman, A., Lawi, A., Tahir, Z.: Pattern recognition of human activity based on smartphone
data sensors using SVM multiclass. In: 1st International Conference on Science and
Technology, ICOST 2019. European Alliance for Innovation (EAI) (2019)
15. Cheng, B.C., Tsai, Y.A., Liao, G.T., Byeon, E.S.: HMM machine learning and inference for
Activities of Daily Living recognition. J. Supercomput. 54(1), 29–42 (2010)
16. Jolliffe, I.T.: Principal Component Analysis, 2nd edn. Springer, NewYork (2010)
17. Osuna, E., Freund, R., Girosi, F.: Support vector machines: training and applications.
Massachusetts Institute of Technology, Cambridge (1997)
18. Veropoulos, K., Campbell, C., Cristianini, N.: Controlling the sensitivity of support vector
machines. In: Proceedings of the International Joint Conference on AI, pp. 55–60 (1999)
19. Huang, Y.M., Du, S.X.: Weighted support vector machine for classification with uneven
training class sizes. In: Proceedings of the IEEE International Conference on Machine
Learning and Cybernetics, vol. 7, pp. 4365–4369 (2005)
20. Abidine, B.M., Fergani, L., Fergani, B., Oussalah, M.: The joint use of sequence features
combination and modified weighted SVM for improving daily activity recognition. Pattern
Anal. Appl. 21(1), 119–138 (2016). https://doi.org/10.1007/s10044-016-0570-y
21. Cheng, B.C., Tsai, Y.A., Liao, G.T., Byeon, E.S.: HMM machine learning and inference for
activities of daily living recognition. J. Supercomput. 54(1), 29–42 (2010). https://doi.org/
10.1007/s11227-009-0335-0
22. https://archive.ics.uci.edu/ml/machine-learning-databases/00240/. Accessed Mar 2017
Human Activities Recognition in Android Smartphone Using WSVM-HMM Classifier
393