194
F. Othmen et al.
efficient compared with the one presenting similar accuracy thanks to its simpler
algorithm complexity compared with the CNN architecture.
Thorough experimentation will be conducted in future work, we expect an
additional improvement of results even further. As being a popular representation based paradigm, we plan next to test the performance of the SDL on
jointly learn a frame-like representation of further complex patterns like cepstral
representations and classification parameters in order to enhance the system’s
reliability.
In our future related work, we will study a further advantage of the DLA
benefits by testing its robustness in regards to noisy signals. For this proposal, we
will be combining a Signal-to-Noise Ratio (SNR) to the raw signal and compare
its performance behavior with traditional machine learning models.
References
1. Ansari, M., Mahmood, N., Nadeem, A., Mehmood, A., Rizwan, K.: Fall detection
system for the elderly based on the classification of shimmer sensor prototype data.
Healthc. Inform. Res. 23, 147–158 (2017)
2. Aziz, O., Musngi, M., Park, E.J., Mori, G., Robinovitch, S.N.: A comparison of
accuracy of fall detection algorithms (threshold-based vs. machine learning) using
waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and
non-fall trials. Med. Biol. Eng. Comput. 55(1), 45–55 (2016). https://doi.org/10.
1007/s11517-016-1504-y
3. Ceylan, R.: The effect of feature extraction based on dictionary learning on ECG
signal classification. Int. J. Intell. Syst. Appl. Eng. 1, 40–46 (2018)
4. Degen, T., Jaeckel, H., Rufer, M., Wyss, S.: SPEEDY: a fall detector in a wrist
watch. In: Seventh IEEE International Symposium on Wearable Computers 2003,
Proceedings, pp. 184–187, October 2003
5. Gangeh, M., Farahat, A., Ghodsi, A., Kamel, M.S.: Supervised dictionary learning
and sparse representation-a review, February 2015
6. Genoud, D., Cuendet, V., Torrent, J.: Soft fall detection using machine learning
in wearable devices. In: 2016 IEEE 30th International Conference on Advanced
Information Networking and Applications (AINA), pp. 501–505 (2016)
7. Hsieh, S., Chen, C., Wu, S., Yue, T.: A wrist -worn fall detection system using
accelerometers and gyroscopes. In: Proceedings of the 11th IEEE International
Conference on Networking, Sensing and Control, pp. 518–523, April 2014
8. Igual, R., Medrano, C., Plaza, I.: A comparison of public datasets for accelerationbased fall detection. Med. Eng. Phys. 37, 870–878 (2015)
9. Jiang, Z., Lin, Z., Davis, L.S.: Learning a discriminative dictionary for sparse coding via label consistent K-SVD, pp. 1697–1704, June 2011
10. Khel, M.A.B., Ali, M.: Technical analysis of fall detection techniques. In: 2019 2nd
International Conference on Advancements in Computational Sciences (ICACS),
pp. 1–8 (2019)
11. Khojasteh, S.B., Villar, J.R., Chira, C., Gonz´ alez, V.M., De la Cal, E.: Improving
fall detection using an on-wrist wearable accelerometer. Sensors 18(5), 1350 (2018)
12. Krupitzer, C., Sztyler, T., Edinger, J., Breitbach, M., Stuckenschmidt, H., Becker,
C.: Beyond position-awareness-extending a self-adaptive fall detection system. Pervasive Mob. Comput. 58, 101026 (2019)
F. Othmen et al.
efficient compared with the one presenting similar accuracy thanks to its simpler
algorithm complexity compared with the CNN architecture.
Thorough experimentation will be conducted in future work, we expect an
additional improvement of results even further. As being a popular representation based paradigm, we plan next to test the performance of the SDL on
jointly learn a frame-like representation of further complex patterns like cepstral
representations and classification parameters in order to enhance the system’s
reliability.
In our future related work, we will study a further advantage of the DLA
benefits by testing its robustness in regards to noisy signals. For this proposal, we
will be combining a Signal-to-Noise Ratio (SNR) to the raw signal and compare
its performance behavior with traditional machine learning models.
References
1. Ansari, M., Mahmood, N., Nadeem, A., Mehmood, A., Rizwan, K.: Fall detection
system for the elderly based on the classification of shimmer sensor prototype data.
Healthc. Inform. Res. 23, 147–158 (2017)
2. Aziz, O., Musngi, M., Park, E.J., Mori, G., Robinovitch, S.N.: A comparison of
accuracy of fall detection algorithms (threshold-based vs. machine learning) using
waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and
non-fall trials. Med. Biol. Eng. Comput. 55(1), 45–55 (2016). https://doi.org/10.
1007/s11517-016-1504-y
3. Ceylan, R.: The effect of feature extraction based on dictionary learning on ECG
signal classification. Int. J. Intell. Syst. Appl. Eng. 1, 40–46 (2018)
4. Degen, T., Jaeckel, H., Rufer, M., Wyss, S.: SPEEDY: a fall detector in a wrist
watch. In: Seventh IEEE International Symposium on Wearable Computers 2003,
Proceedings, pp. 184–187, October 2003
5. Gangeh, M., Farahat, A., Ghodsi, A., Kamel, M.S.: Supervised dictionary learning
and sparse representation-a review, February 2015
6. Genoud, D., Cuendet, V., Torrent, J.: Soft fall detection using machine learning
in wearable devices. In: 2016 IEEE 30th International Conference on Advanced
Information Networking and Applications (AINA), pp. 501–505 (2016)
7. Hsieh, S., Chen, C., Wu, S., Yue, T.: A wrist -worn fall detection system using
accelerometers and gyroscopes. In: Proceedings of the 11th IEEE International
Conference on Networking, Sensing and Control, pp. 518–523, April 2014
8. Igual, R., Medrano, C., Plaza, I.: A comparison of public datasets for accelerationbased fall detection. Med. Eng. Phys. 37, 870–878 (2015)
9. Jiang, Z., Lin, Z., Davis, L.S.: Learning a discriminative dictionary for sparse coding via label consistent K-SVD, pp. 1697–1704, June 2011
10. Khel, M.A.B., Ali, M.: Technical analysis of fall detection techniques. In: 2019 2nd
International Conference on Advancements in Computational Sciences (ICACS),
pp. 1–8 (2019)
11. Khojasteh, S.B., Villar, J.R., Chira, C., Gonz´ alez, V.M., De la Cal, E.: Improving
fall detection using an on-wrist wearable accelerometer. Sensors 18(5), 1350 (2018)
12. Krupitzer, C., Sztyler, T., Edinger, J., Breitbach, M., Stuckenschmidt, H., Becker,
C.: Beyond position-awareness-extending a self-adaptive fall detection system. Pervasive Mob. Comput. 58, 101026 (2019)