A Novel On-Wrist Fall Detection System Using SDL Technique
195
13. Mo, H., Luo, C., Jan, G.E.: EEG classification based on sparse representation. In:
2017 International Joint Conference on Neural Networks (IJCNN), pp. 59–62, May
2017
14. Nho, Y., Lim, J.G., Kim, D., Kwon, D.: User-adaptive fall detection for patients
using wristband. In: 2016 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS), pp. 480–486, October 2016
15. Noury, N., et al.: Fall detection - principles and methods. In: 2007 29th Annual
International Conference of the IEEE Engineering in Medicine and Biology Society,
pp. 1663–1666, August 2007
16. World Health Organization: WHO Global Report on Falls Prevention in Older
Age. NIH Publication, Bethesda (2008)
17. World Health Organization of Aging, U.N.I.: Global Health and Ageing. NIH Publication, Bethesda (2011)
18. Ozdemir, A.: An analysis on sensor locations of the human body for wearable fall
detection devices: principles and practice. Sensors 16, 1161 (2016)
19. de Quadros, T., Lazzaretti, A.E., Schneider, F.K.: A movement decomposition and
machine learning-based fall detection system using wrist wearable device. IEEE
Sens. J. 18, 5082–5089 (2018)
20. Urresty Sanchez, J.A., Mu˜ noz, D.M.: Fall detection using accelerometer on the
user’s wrist and artificial neural networks. In: Costa-Felix, R., Machado, J.C.,
Alvarenga, A.V. (eds.) XXVI Brazilian Congress on Biomedical Engineering. IP,
vol. 70/1, pp. 641–647. Springer, Singapore (2019). https://doi.org/10.1007/978981-13-2119-1 98
21. Suo, Y., Dao, M., Srinivas, U., Monga, V., Tran, T.D.: Structured dictionary learning for classification (2014)
22. Vallabh, P., Malekian, R.: Fall detection monitoring systems: a comprehensive
review. J. Ambient Intell. Humaniz. Comput. 9(6), 1809–1833 (2017). https://doi.
org/10.1007/s12652-017-0592-3
23. Vallabh, P., Malekian, R., Ye, N., Capeska Bogatinoska, D.: Fall detection
using machine learning algorithms, September 2016. https://doi.org/10.1109/
SOFTCOM.2016.7772142
24. Vu, T.H., Monga, V.: Learning a low-rank shared dictionary for object classification, pp. 4428–4432, September 2016
25. Vu, T., Monga, V.: Fast low-rank shared dictionary learning for image classification. IEEE Trans. Image Process. 26, 5160–5175 (2016)
26. Wang, F.T., Chan, H.L., Hsu, M.H., Lin, C.K., Chao, P.K., Chang, Y.J.:
Threshold-based fall detection using a hybrid of tri-axial accelerometer and gyroscope. Physiol. Meas. 39, 105002 (2018)
27. World Health Organization: ICD-11 for mortality and morbidity statistics
(version 04/2019). http://icd.who.int/browse11/l-m/en#/id.who.int/icd/entity/
134290789. Accessed 09 Nov 2019
28. Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition
via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 210–227
(2009)
29. Xu, T., Zhou, Y., Zhu, J.: New advances and challenges of fall detection systems:
a survey. Appl. Sci. 8, 418 (2018)
30. Xu, Y., Li, Z., Yang, J., Zhang, D.: A survey of dictionary learning algorithms for
face recognition. IEEE Access 5, 8502–8514 (2017)
31. Yang, M., Zhang, L., Feng, X., Zhang, D.: Fisher discrimination dictionary learning
for sparse representation, pp. 543–550, November 2011
195
13. Mo, H., Luo, C., Jan, G.E.: EEG classification based on sparse representation. In:
2017 International Joint Conference on Neural Networks (IJCNN), pp. 59–62, May
2017
14. Nho, Y., Lim, J.G., Kim, D., Kwon, D.: User-adaptive fall detection for patients
using wristband. In: 2016 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS), pp. 480–486, October 2016
15. Noury, N., et al.: Fall detection - principles and methods. In: 2007 29th Annual
International Conference of the IEEE Engineering in Medicine and Biology Society,
pp. 1663–1666, August 2007
16. World Health Organization: WHO Global Report on Falls Prevention in Older
Age. NIH Publication, Bethesda (2008)
17. World Health Organization of Aging, U.N.I.: Global Health and Ageing. NIH Publication, Bethesda (2011)
18. Ozdemir, A.: An analysis on sensor locations of the human body for wearable fall
detection devices: principles and practice. Sensors 16, 1161 (2016)
19. de Quadros, T., Lazzaretti, A.E., Schneider, F.K.: A movement decomposition and
machine learning-based fall detection system using wrist wearable device. IEEE
Sens. J. 18, 5082–5089 (2018)
20. Urresty Sanchez, J.A., Mu˜ noz, D.M.: Fall detection using accelerometer on the
user’s wrist and artificial neural networks. In: Costa-Felix, R., Machado, J.C.,
Alvarenga, A.V. (eds.) XXVI Brazilian Congress on Biomedical Engineering. IP,
vol. 70/1, pp. 641–647. Springer, Singapore (2019). https://doi.org/10.1007/978981-13-2119-1 98
21. Suo, Y., Dao, M., Srinivas, U., Monga, V., Tran, T.D.: Structured dictionary learning for classification (2014)
22. Vallabh, P., Malekian, R.: Fall detection monitoring systems: a comprehensive
review. J. Ambient Intell. Humaniz. Comput. 9(6), 1809–1833 (2017). https://doi.
org/10.1007/s12652-017-0592-3
23. Vallabh, P., Malekian, R., Ye, N., Capeska Bogatinoska, D.: Fall detection
using machine learning algorithms, September 2016. https://doi.org/10.1109/
SOFTCOM.2016.7772142
24. Vu, T.H., Monga, V.: Learning a low-rank shared dictionary for object classification, pp. 4428–4432, September 2016
25. Vu, T., Monga, V.: Fast low-rank shared dictionary learning for image classification. IEEE Trans. Image Process. 26, 5160–5175 (2016)
26. Wang, F.T., Chan, H.L., Hsu, M.H., Lin, C.K., Chao, P.K., Chang, Y.J.:
Threshold-based fall detection using a hybrid of tri-axial accelerometer and gyroscope. Physiol. Meas. 39, 105002 (2018)
27. World Health Organization: ICD-11 for mortality and morbidity statistics
(version 04/2019). http://icd.who.int/browse11/l-m/en#/id.who.int/icd/entity/
134290789. Accessed 09 Nov 2019
28. Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition
via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 210–227
(2009)
29. Xu, T., Zhou, Y., Zhu, J.: New advances and challenges of fall detection systems:
a survey. Appl. Sci. 8, 418 (2018)
30. Xu, Y., Li, Z., Yang, J., Zhang, D.: A survey of dictionary learning algorithms for
face recognition. IEEE Access 5, 8502–8514 (2017)
31. Yang, M., Zhang, L., Feng, X., Zhang, D.: Fisher discrimination dictionary learning
for sparse representation, pp. 543–550, November 2011