Annals of Computer Science and Information Systems, Volume 11

DOI: http://dx.doi.org/10.15439/2017F405

Citation: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 11, pages 877–883 (2017)

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Abstract. Frequency domain features of inertial movement enables multi-resolution analysis for fall detection, yet they are computationally intensive. This paper proposes a computationally light frequency domain feature extraction method based on lifting wavelet transform (LWT) which provides computational efficiency suitable for real-time low power devices such as wearable sensors for human fall detection. LWT is combined with support vector machine (SVM) to identify falls from activities of daily living. Performance of the Haar and Biorthogonal 2.2 wavelets were compared with the time domain feature of root-mean square acceleration using a human fall dataset. Results show that the first level detail coefficients features for both Haar and Biorthogonal 2.2 wavelets achieve good overall fall detection accuracy, sensitivity and specificity.

References

1. Pierleoni P, Pernini L, Belli A, et al. “SVM-based fall detection method for elderly people using Android low-cost smartphones,” IEEE Sensors Applications Symposium (SAS 2015), 2015: 1-5.
2. Banaee H, Ahmed M U, Loutfi A., “Data mining for wearable sensors in health monitoring systems: a review of recent trends and challenges,” Sensors, 2013, 13(12): 17472-17500.
3. Carlsson T., “Individualized Motion Monitoring by Wearable Sensor: Pre-impact fall detection using SVM and sensor fusion,” Masters Thesis, School of Technology and Health, KTH Royal Institute of Technology, Stockholm, Sweden, 2015.
4. Özdemir A T, Barshan B., “Detecting falls with wearable sensors using machine learning techniques,” Sensors, 2014, 14(6): 10691-10708.
5. Su B Y, Ho K C, Rantz M J, et al., “Doppler radar fall activity detection using the wavelet transform,” IEEE Transactions on Biomedical Engineering, 2015, 62(3): 865-875.
6. Björklund S, Petersson H, Hendeby G. , “Features for micro-Doppler based activity classification,” IET Radar, Sonar & Navigation, 2015, 9(9): 1181-1187.
7. Palmerini L, Bagalà F, Zanetti A, et al., “A wavelet-based approach to fall detection,” Sensors, 2015, 15(5): 11575-11586.
8. Aziz O, Musngi M, Park E J, et al., “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,” Medical & Biological Engineering & Computing, 2017, 55(1): 45-55.
9. Bilski P, Mazurek P, Wagner J, et al., “Application of Decision trees to the Fall Detection of elderly People using Depth-based sensors,” Proc. IEEE International Conference on Intelligent Data Acqut and Advanced Computing Systems (IDAACS 2015), Warsaw, Poland, September, 2015.
10. Parkka J, Ermes M, Korpipaa P, et al. , “Activity classification using realistic data from wearable sensors,” IEEE Transactions on Information Technology in Biomedicine, 2006, 10(1): 119-128.
11. Wang Z, Jiang M, Hu Y, et al., “An incremental learning method based on probabilistic neural networks and adjustable fuzzy clustering for human activity recognition by using wearable sensors,” IEEE Transactions on Information Technology in Biomedicine, 2012, 16(4): 691-699.
12. Tong L, Song Q, Ge Y, et al., “HMM-based human fall detection and prediction method using tri-axial accelerometer,” IEEE Sensors Journal, 2013, 13(5): 1849-1856.
13. Kianoush S, Savazzi S, Vicentini F, et al., “Leveraging RF signals for human sensing: fall detection and localization in human-machine shared workspaces,” //Industrial Informatics (INDIN), 2015 IEEE 13th International Conference on. IEEE, 2015: 1456-1462.
14. Pierleoni P, Belli A, Palma L, et al. “A high reliability wearable device for elderly fall detection,” IEEE Sensors Journal, 2015, 15(8): 4544- 4553.
15. Liu S H, Cheng W C., “ Fall detection with the support vector machine during scripted and continuous unscripted activities,” Sensors, 2012, 12(9): 12301-12316.
16. Shibuya N, Nukala B T, Rodriguez A I, et al., “A real-time fall detection system using a wearable gait analysis sensor and a support vector machine (SVM) classifier,” Mobile Computing and Ubiquitous Networking (ICMU), 2015 Eighth International Conference on. IEEE, 2015: 66-67.
17. Sweldens, Wim. “The Lifting Scheme: A Construction of Second Generation Wavelets," Journal on Mathematical Analysis. SIAM. 1997, 29 (2): 511–546.
18. Wójtowicz B, Dobrowolski A, Tomczykiewicz K., “Fall detector using discrete wavelet decomposition and SVM classifier,” Metrol. Meas. Syst, 2015, 22(2): 303-314.
19. Isu Shin, Jongsang Son, Soonjae Ahn, Jeseong Ryu, Sunwoo Park, Jongman Kim, Baekdong Cha, Eunkyoung Choi, and Youngho Kim, "A Novel Short-Time Fourier Transform-Based Fall Detection Algorithm Using 3-Axis Accelerations," Mathematical Problems in Engineering, 2015, Article ID 394340.
20. Hsu C W, Chang C C, Lin C J. A practical guide to support vector classification, 2003.
21. Cortes C, Vapnik V., “Support-vector networks,” Machine Learning, 1995, 20(3): 273-297.
22. Chang C C, Lin C J., “ LIBSVM: a library for support vector machines,” ACM Transactions on Intelligent Systems and Technology, 2011, 2(3): 27.
23. Altman D G, Bland J M., “Diagnostic Tests : Sensitivity and specificity,” British Medical Journal, 1994, 308(6943): 1552.
24. Kwolek B, Kepski M., “Human fall detection on embedded platform using depth maps and wireless accelerometer,” Computer Methods and Programs in Biomedicine, 2014, 117(3): 489-501.